The inference workloads of Large Language Models (LLMs) pose significant latency and cost challenges due to increasing model sizes and demand for real-time responses. Existing cluster schedulers for multi-instance LLM serving primarily focus on load balancing to optimize memory usage, which is insufficient for workloads with diverse request characteristics. In such cases, the compute layout — the arrangement of tokens across iterations within each instance—plays a crucial role in determining latency. We propose AdaGen, a workload-adaptive cluster scheduler that minimizes latency and thus maximizes SLO attainment by optimizing compute layouts across instances. AdaGen employs a multi-step scheduling strategy: it first classifies requests based on prefill and decode lengths, then balances load, and finally performs selective distributed execution across instances. Each step incrementally refines the scheduling based on the compute layouts derived from the decision of the previous step. To avoid the overhead of actual execution to generate the layouts, AdaGen introduces a novel simulation-based estimator. Extensive experiments using production workloads show that AdaGen achieves up to 3.6× higher SLO attainment and 2× better cost-efficiency compared to the existing systems, while ensuring scalability.

peer reviewed

The rapid adoption of large language models (LLMs) has increased the need for efficient multi-tenant inference systems that maximize GPU utilization. However, existing frameworks struggle to scale due to the high memory demands of model weights and key-value (KV) caches. We present DynamoServe, a multi-tenant LLM serving framework that addresses these challenges through three key innovations: (1) leveraging stranded GPU memory to offload model weights and KV caches, (2) mitigating resource fragmentation in multi-workload environments, and (3) improving memory locality through coordinated data placement and demand-driven weight migration across GPUs. Together, these techniques enable high-throughput, low-latency inference. Experiments on state-of-the-art models show that DynamoServe significantly improves memory efficiency without sacrificing latency.

The 1st free5GC World Forum brings together researchers, industry professionals, and open-source contributors to explore the challenges and recent advancements in 5G core networks, with a particular focus on security and the transformative role of free5GC, the leading open-source 5G Standalone (SA) core network. free5GC, a project hosted by the Linux Foundation, is a fully 3GPP-compliant, open-source 5G core network platform. It empowers researchers, developers, and businesses to drive innovation in 5G systems by providing a robust prototyping environment for next-generation connectivity solutions. Recent notable architectures, such as L25GC+, which aims to significantly reduce control plane latency while maintaining 3GPP compliance, exemplify how free5GC facilitates the development and testing of cutting-edge ideas on a production-grade 5G core. The forum offers a unique opportunity for participants to share insights, present innovations, and shape the future direction of free5GC and next-generation mobile networks.

Serverless computing offers resource efficiency but suffers from a heavyweight data plane. We present Palladium, a DPU-offloaded serverless data plane enabling distributed zero-copy communication. Palladium uses two-sided RDMA and cross-processor shared memory to mitigate limitations of wimpy DPU cores. Its DPU-enabled network engine (DNE) isolates RDMA resources and manages flows across tenants. By converting HTTP/TCP to RDMA at ingress, Palladium reduces protocol overhead on the critical path.

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Current serverless platforms introduce non-trivial overheads when chaining and orchestrating loosely-coupled microservices. Containerized function runtimes are also constrained by insufficient isolation and excessive startup time. This motivates our exploration of a more efficient, secure, and rapid serverless design. We describe SURE, a unikernel-based serverless framework for fast function startup, equipped with a high-performance and secure data plane. SURE's data plane supports distributed zero-copy communication via the seamless interaction between zero-copy protocol stack (Z-stack) and local shared memory processing. To establish a lightweight service mesh, SURE uses library-based sidecars instead of individual userspace sidecars. We leverage Intel's Memory Protection Keys (MPK) as a lightweight capability to ensure safe access to the shared memory data plane. It also isolates the Trusted Computing Base (TCB) components in SURE's function runtime (e.g., library-based sidecar, scheduler, etc) from untrusted user code, while preserving the efficient single-address-space nature of unikernels. In particular, SURE prevents unintended privilege escalation involving MPK with an enhanced TCB. These combined efforts create a more secure and robust data plane while improving throughput up to 79X over Knative, a representative open-source serverless platform.

Recent work shows that programmable switches can effectively detect attack traffic, such as denial-of-service attacks in the midst of high-volume network traffic. However, these techniques primarily rely on sampling or sketch-based data structures, which can only be used to approximate the characteristics of dominant flows in the network. As a result, such techniques are unable to effectively detect low-volume attacks that stealthily add only a few packets to the network. Our work explores how the combination of programmable switches, Smart network interface cards, and hosts can enable fine-grained analysis of every flow in a network, even those with only a small number of packets. We focus on analyzing packets at the start of each flow, as those packets often can help indicate whether a flow is benign or suspicious. We propose a unified architecture that spans the full programmable dataplane to take advantage of the strengths of each type of device. We are developing new filter data structures to efficiently track flows on the switch, dataplane-based communication protocols to quickly coordinate between devices, and caching approaches on the SmartNIC that help minimize the traffic load reaching the host. Our preliminary prototype can handle the full pipe bandwidth of 1.4 Tbps of traffic entering the Tofino switch, forward only 20 Gbps to the SmartNIC, and minimize the traffic load to 5 Gbps reaching the host due to our efficient flow filter, packet batching, and SmartNIC-based cache.

Data centers require high-performance and efficient networking for fast and reliable communication between applications. TCP/IP-based networking still plays a dominant role in data center networking to support a wide range of Layer-4 and Layer-7 applications, such as middleboxes and cloud-based microservices. However, traditional kernel-based TCP/IP stacks face performance challenges due to overheads such as context switching, interrupts, and copying. We present Z-stack, a high-performance userspace TCP/IP stack with a zero-copy design. Utilizing DPDK's Poll Mode Driver, Z-stack bypasses the kernel and moves packets between the NIC and the protocol stack in userspace, eliminating the overhead associated with kernel-based processing. Z-stack em-ploys polling-based packet processing that improves performance under high loads, and eliminates receive livelocks compared to interrupt-driven packet processing. With its zero-copy socket design, Z-stack eliminates copies when moving data between the user application and the protocol stack, which further minimizes latency and improves throughput. In addition, Z-stack seamlessly integrates with shared memory processing within the node, eliminating duplicate protocol processing and serializationldese-rialization overheads for intra-node communication. Z-stack uses F-stack as the starting point which integrates the proven TCP/IP stack from FreeBSD, providing a versatile solution for a variety of cloud use cases and improving performance of data center networking.

Hardware accelerators such as GPUs are required for real-time, low latency inference with Deep Neural Networks (DNN). Providing inference services in the cloud can be resource intensive, and effectively utilizing accelerators in the cloud is important. Spatial multiplexing of the GPU, while limiting the GPU resources (GPU%) to each DNN to the right amount, leads to higher GPU utilization and higher inference throughput. Right-sizing the GPU for each DNN the optimal batching of requests to balance throughput and service level objectives (SLOs), and maximizing throughput by appropriately scheduling DNNs are still significant challenges.This article introduces a dynamic and fair spatio-temporal scheduler (D-STACK) for multiple DNNs to run in the GPU concurrently. We develop and validate a model that estimates the parallelism each DNN can utilize and a lightweight optimization formulation to find an efficient batch size for each DNN. Our holistic inference framework provides high throughput while meeting application SLOs. We compare D-STACK with other GPU multiplexing and scheduling methods (e.g., NVIDIA Triton, Clipper, Nexus), using popular DNN models. Our controlled experiments with multiplexing several popular DNN models achieve up to$1.6\times$improvement in GPU utilization and up to$4\times$improvement in inference throughput.

Serverless computing promises an efficient, low-cost compute capability in cloud environments. However, existing solutions, epitomized by open-source platforms such as Knative, include heavyweight components that undermine this goal of serverless computing. Additionally, such serverless platforms lack dataplane optimizations to achieve efficient, high-performance function chains that facilitate the popular microservices development paradigm. Their use of unnecessarily complex and duplicate capabilities for building function chains severely degrades performance. ‘Cold-start’ latency is another deterrent. We describe, a lightweight, high-performance, responsive serverless framework. exploits shared memory processing and dramatically improves the scalability of the dataplane by avoiding unnecessary protocol processing and serialization-deserialization overheads. extensively leverages event-driven processing with the extended Berkeley Packet Filter (eBPF). We creatively use eBPF’s socket message mechanism to support shared memory processing, with overheads being strictly load-proportional. Compared to constantly-running, polling-based DPDK, achieves the same dataplane performance with 10$\times$less CPU usage under realistic workloads. Additionally, eBPF benefits, by replacing heavyweight serverless components, allowing us to keep functions ‘warm’ with negligible penalty. Our preliminary experimental results show that achieves an order of magnitude improvement in throughput and latency compared to Knative, while substantially reducing CPU usage, and obviates the need for ‘cold-start’.

Cloud service providers often provision excessive resources to meet the desired Service Level Objectives (SLOs), by setting lower CPU utilization targets. This can result in a waste of resources and a noticeable increase in power consumption in large-scale cloud deployments. To address this issue, this paper presents DeepScaling, an innovative solution for minimizing resource cost while ensuring SLO requirements are met in a dynamic, large-scale production microservice-based system. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization of servers in the data center, and we maintain it at a stable value to meet SLO constraints while using minimum amount of system resources. First, DeepScaling forecasts workloads for each service using a Spatio-temporal Graph Neural Network. Secondly, it estimates CPU utilization with a Deep Neural Network, considering factors such as periodic tasks and traffic. Finally, it uses a modified Deep Q-Network (DQN) to generate an autoscaling policy that controls service resources to maximize service stability while meeting SLOs. Evaluation of DeepScaling in Ant Group’s large-scale cloud environment shows that it outperforms state-of-the-art autoscaling approaches in terms of maintaining stable performance and resource savings. The deployment of DeepScaling in the real-world environment of 1900+ microservices saves the provisioning of over 100,000 CPU cores per day, on average.

Efficient and secure message dissemination plays an important role during a disaster environment. Name-based publish/subscribe systems, especially role-based names, using principles of Information-Centricity provide an efficient frame-work for communications among first responders. However, a challenge is maintaining confidentiality during communication. We have developed an encryption framework that leverages graph-based naming systems which provides role-based communication among first responders. Our framework is built on top of the dynamic role-based names and can be implemented using attribute-based encryption (ABE) or public key encryption (PKE). In this demo, we show the operations of our framework in a typical scenario of first responders using the application.

Name-based publish/subscribe systems using Information-Centric Networking (ICN) principles can provide a flexible and efficient framework for communication in disaster situations. Efficient, secure dissemination of information can play a critical role in disaster management. But, secure and authenticated group communications that maintain confidentiality and integrity remain a challenge. In this paper, we design a flexible and efficient encryption framework SAFE that leverages graph-based naming frameworks for providing role-based communication among first responders. We study the suitability of message-oriented encryption where the sender leverages the name hierarchy, and compare it with a key-oriented encryption scheme that requires the receiver to utilize appropriate keys to decrypt based on the publisher-targeted name for the message. Both encryption schemas can be built with attribute-based encryption (ABE) or public key encryption (PKE) implementations. We find message-oriented encryption provides the needed flexibility for dynamic environments when communicating with members changes frequently. With message-oriented encryption, we further address key revocation and support for infrastructure-less environments in disaster situations and consider the tradeoff between flexibility and optimization for large relatively static communication groups. We evaluate both encryption schemas built on top of ABE and PKE. We examine the key generation time, ciphertext length, encryption, and decryption time, and see that SAFE's design is the most suitable for large and dynamically changing groups.

Cloud-native microservice applications use different communication paradigms to network microservices, including both synchronous and asynchronous I/O for exchanging data. Existing solutions depend on kernel-based networking, incurring significant overheads. The interdependence between microservices for these applications involves considerable communication, including contention between multiple concurrent flows or user sessions. In this paper, we design X-IO, a high-performance unified I/O interface that is built on top of shared memory processing with lock-free producer/consumer rings, eliminating kernel networking overheads and contention. X-IO offers a feature-rich interface. X-IO’s zero-copy interface supports building provides truly zero-copy data transfers between microservices, achieving high performance. X-IO also provides a POSIX-like socket interface using HTTP/REST API to achieve seamless porting of microservices to X-IO, without any change to the application code. X-IO supports concurrent connections for microservices that require distinct user sessions operating in parallel. Our preliminary experimental results show that X-IO’s zero-copy interfaces achieve 2.8x-4.1x performance improvement compared to kernel-based interfaces. Its socket interfaces outperform kernel TCP sockets and achieve performance close to UNIX-domain sockets. The HTTP/REST APIs in X-IO perform 1.4 x-2.3 x better than kernel-based alternatives with concurrent connections.

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to softwarebased components. However, L2/L3 network functions (NFs) are being implemented on Network Function Virtualization (NFV) platforms that extensively exploit kernel-bypass technology. They often use DPDK for zero-copy delivery and high performance. On the other hand, L4/L7 middleboxes, which have a greater emphasis on functionality, take advantage of a full-fledged kernelbased system. L2/L3 NFs and L4/L7 middleboxes continue to be handled by distinct platforms on different nodes. This paper proposes MiddleNet that develops a unified network resident function framework that supports L2/L3 NFs and L4/L7 middleboxes. MiddleNet supports function chains that are essential in both NFV and middlebox environments. MiddleNet uses the Data Plane Development Kit (DPDK) library for zero-copy packet delivery without interrupt-based processing, to enable the ’bumpin-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack for processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities of the extended Berkeley Packet Filter (eBPF) and seamlessly integrates it with shared memory for high-performance communication in L4/L7 middlebox function chains. The overheads for MiddleNet in L4/L7 are strictly load-proportional, without needing the dedicated CPU cores of DPDK-based approaches. MiddleNet supports flow-dependent packet processing by leveraging Single Root I/O Virtualization (SR-IOV) to dynamically select the packet processing needed (Layers 2 -7). Our experimental results show that MiddleNet achieves high performance in such a unified environment.

Cloud service providers conservatively provision excessive resources to ensure service level objectives (SLOs) are met. They often set lower CPU utilization targets to ensure service quality is not degraded, even when the workload varies significantly. Not only does this potentially waste resources, but it can also consume excessive power in large-scale cloud deployments. This paper aims to minimize resource costs while ensuring SLO requirements are met in a dynamically varying, large-scale production microservice environment. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization to a level that is maintained at a stable value to meet SLO constraints while using minimum resources. First, DeepScaling forecasts the workload for each service using a Spatio-temporal Graph Neural Network. Second, DeepScaling estimates the CPU utilization by mapping the workload intensity to an estimated CPU utilization with a Deep Neural Network, while taking into account multiple factors in the cloud environment (e.g., periodic tasks and traffic). Third, DeepScaling generates an autoscaling policy for each service based on an improved Deep Q Network (DQN). The adaptive autoscaling policy updates the target CPU utilization to be a maximum, stable value, while ensuring SLOs is not violated. We compare DeepScaling with state-of-the-art autoscaling approaches in the large-scale production cloud environment of the Ant Group. It shows that DeepScaling outperforms other approaches both in terms of maintaining stable service performance, and saving resources, by a significant margin. The deployment of DeepScaling in Ant Group's real production environment with 135 microservices saves the provisioning of over 30,000 CPU cores per day, on average.

Augmented reality (AR) devices perform visual simultaneous localization and mapping (SLAM) to map the real world and localize themselves in it, enabling them to render the virtual holograms appropriately. Current multi-user AR platforms fall short in that they only allow asymmetric sharing of this SLAM information, resulting in multiple "secondary" devices viewing holograms placed by a single "primary" device, instead of equal participation. The goal of this work is to enable all AR devices to participate equally, by constructing a common global map to which all AR devices can contribute. However, doing so with low latency and high accuracy is challenging on resource-constrained mobile devices. This work proposes an appropriate partitioning between clients and a server to achieve high-throughput, low latency, multi-user SLAM. In our system, SLAM-Share, the edge server performs the complex SLAM computations so that the client devices need only perform lightweight operations. The server utilizes shared memory and efficient map merging to build and update a global map from different clients. It also exploits the parallelism of GPU processing to achieve high-performance tracking. Evaluations show that SLAM-Share is able to achieve significant tracking speedups (up to 50% reduction compared to alternative approaches), maintain good localization accuracy, and merge and update maps within 200 ms.

Most CPU power management techniques for web search assume that the time budget for a query is given a priori. However, determining the time budget on a per query granularity is challenging, because a difficult trade-off between the search latency, quality and power consumption has to be made. In this paper, we present Cottage, a coordinated time budget assignment framework between the aggregator and Index Serving Nodes (ISNs), which employs two distinct distributed search latency and quality predictors. The prediction results are integrated at a centralized optimizer for selecting the proper search time budget, while cutting off slow and low quality ISNs. Cottage also accelerates slow ISNs that have a high quality contribution, thus improving search quality. The implementation results on the Solr search engine show that Cottage outperforms state-of-the-art approaches with a 54% latency reduction and 41.3% less consumed power. In addition, the P@10 search quality with Cottage can still be as good as 0.947.

The 5G user plane function (UPF) is a critical inter-connection point between the data network and cellular network infrastructure. It governs the packet processing performance of the 5G core network. UPFs also need to be flexible to support several key control plane operations. Existing UPFs typically run on general-purpose CPUs, but have limited performance because of the overheads of host-based forwarding. We design Synergy, a novel 5G UPF running on SmartNICs that provides high throughput and low latency. It also supports monitoring functionality to gather critical data on user sessions for the prediction and optimization of handovers during user mobility. The SmartNIC UPF efficiently buffers data packets during handover and paging events by using a two-level flow-state access mechanism. This enables maintaining flow-state for a very large number of flows, thus providing very low latency for control and data planes and high throughput packet forwarding. Mobility prediction can reduce the handover delay by pre-populating state in the UPF and other core NFs. Synergy performs handover predictions based on an existing recurrent neural network model. Synergy's mobility predictor helps us achieve 2.32× lower average handover latency. Buffering in the SmartNIC, rather than the host, during paging and handover events reduces packet loss rate by at least 2.04×. Compared to previous approaches to building programmable switch-based UPFs, Synergy speeds up control plane operations such as handovers because of the low P4-programming latency leveraging tight coupling between SmartNIC and host.

Autotuning DNN models prior to their deployment is an essential but time-consuming task. Using expensive (and power-hungry) GPU and TPU accelerators efficiently is also key. Since DNNs do not always use a GPU fully, spatial multiplexing of multiple models can provide just the right amount of GPU resources for each DNN. We find that a DNN model tuned with the maximum GPU resources has higher inference latency if less GPU resources are available at inference time. We present methods to tune a DNN model, so that we provide the right amount of accelerator resources during tuning. Thus, even when a wide range of GPU resources are available at inference time, the tuned model achieves low inference latency. Further, existing autotuning frameworks take a long time to tune a model due to inefficient utilization of the client and server-side CPU and GPU. Our system, Slice-Tune, improves several autotuning frameworks to efficiently use system resources by re-thinking the partitioning of tasks between the client and server (where models are profiled on the server GPU), in a Kubernetes environment. We increase parallelism during tuning by sharding the tuning model across multiple tuning application instances, providing concurrent tuning of different operators of a model. We also scale server instances to achieve better GPU multiplexing. Slice-Tune reduces DNN autotuning time in a single GPU and in GPU clusters. Slice-Tune decreases DNN autotuning time by up to 75%, and increase autotuning throughput by a factor of 5, across 3 different autotuning frameworks (TVM, Ansor, and Chameleon).

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to software-based components. However, L2/L3 network functions (NFs) are being implemented on Network Function Virtualization (NFV) platforms that extensively exploit kernel-bypass technology. They often use DPDK for zero-copy delivery and high performance. On the other hand, L4/L7 middleboxes, which usually require full network protocol stack support, take advantage of a full-fledged kernel-based system with a greater emphasis on functionality. Thus, L2/L3 NFs and middleboxes continue to be handled by distinct platforms on different nodes.This paper proposes MiddleNet that seeks to overcome this dichotomy by developing a unified network resident function framework that supports L2/L3 NFs and L4/L7 middleboxes. MiddleNet supports function chains that are essential in both NFV and middlebox environments. MiddleNet uses DPDK for zero-copy packet delivery without interrupt-based processing, to enable the ‘bump-in-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities provided by the extended Berkeley Packet Filter (eBPF) and seamlessly integrates it with shared memory for high-performance communication in L4/L7 middlebox function chains. The overheads for MiddleNet are strictly load-proportional, without needing the dedicated CPU cores of DPDK-based approaches. MiddleNet supports flow-dependent packet processing by leveraging Single Root I/O Virtualization (SR-IOV) to dynamically select packet processing needed (Layer 2 to Layer 7). Our experimental results show that MiddleNet can achieve high performance in such a unified environment.

Softwarized network resident functions have been extensively used to replace purpose-built appliances. However, there is a lack of alternatives for richer network resident functionality with a seamless combination of L2/L3 Network Function Virtualization (NFV) and L4/L7 middleboxes.We propose MiddleNet, a unified L2/L3 NFV and L4/L7 middlebox framework. MiddleNet uses DPDK in L2/L3 NFV to achieve high-performance, zero-copy packet delivery. MiddleNet exploits the event-driven capabilities of extended Berkeley Packet Filter (eBPF) to build up lightweight L4/L7 middleboxes with load-proportional overheads. MiddleNet constructs complex L2/L3 NF and L4/L7 middlebox function chains with low overhead using shared memory communication. With the integration of Single Root I/O Virtualization (SR-IOV), MiddleNet supports dynamically selecting packet processing layers (L2 to L7) based on the flow. In this demo, we show MiddleNet’s operation.

Cellular network control procedures (e.g., mobility, idle-active transition to conserve energy) directly influence data plane behavior, impacting user-experienced delay. Recognizing this control-data plane interdependence, L25GC re-architects the 5G Core (5GC) network, and its processing, to reduce latency of control plane operations and their impact on the data plane. Exploiting shared memory, L25GC eliminates message serialization and HTTP processing overheads, while being 3GPP-standards compliant. We improve data plane processing by factoring the functions to avoid control-data plane interference, and using scalable, flow-level packet classifiers for forwarding-rule lookups. Utilizing buffers at the 5GC, L25GC implements paging, and an intelligent handover scheme avoiding 3GPP's hairpin routing, and data loss caused by limited buffering at 5G base stations, reduces delay and unnecessary message processing. L25GC's integrated failure resiliency transparently recovers from failures of 5GC software network functions and hardware much faster than 3GPP's reattach recovery procedure. L25GC is built based on free5GC, an open-source kernel-based 5GC implementation. L25GC reduces event completion time by ~50% for several control plane events and improves data packet latency (due to improved control plane communication) by ~2×, during paging and handover events, compared to free5GC. L25GC's design is general, although current implementation supports a limited number of user sessions.

Serverless computing promises an efficient, low-cost compute capability in cloud environments. However, existing solutions, epitomized by open-source platforms such as Knative, include heavyweight components that undermine this goal of serverless computing. Additionally, such serverless platforms lack dataplane optimizations to achieve efficient, high-performance function chains that facilitate the popular microservices development paradigm. Their use of unnecessarily complex and duplicate capabilities for building function chains severely degrades performance. 'Cold-start' latency is another deterrent.

In many scenarios, information must be disseminated over intermittently-connected environments when the network infrastructure becomes unavailable, e.g., during disasters where first responders need to send updates about critical tasks. If such updates pertain to a shared data set, dissemination consistency is important. This can be achieved through causal ordering and consensus. Popular consensus algorithms, e.g., Paxos, are most suited for connected environments. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is still need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends OTR to also support long-term network fragmentation and decision invalidation scenarios; we define local and global consensus pertaining to within and across fragments respectively. We integrate CoNICE’s consistency preservation with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Using the Heard-Of model formalism, we prove CoNICE’s consensus to be correct. Our technique extends previously established proof methods for consensus in asynchronous environments. Performing city-scale simulation, we demonstrate CoNICE’s scalability in achieving consistency in convergence time, utilization of network resources, and reduced energy consumption.

An appealing feature of Network Function Virtualization (NFV) is that in an NFV-based network, a network function (NF) instance may be placed at any node. On the one hand this offers great flexibility in allocation of redundant instances, but on the other hand it makes the allocation a unique and difficult challenge. One particular concern is that there is inherent correlation among nodes due to the structure of the network, thus requiring special care in this allocation. To this aim, our novel approach, calledCoShare, is proposed. Firstly, its design takes into consideration the effect of network structural dependency, which might result in the unavailability of nodes of a network after failure of a node. Secondly, to efficiently make use of resources, CoShare proposes the idea ofshared reservation, where multiple flows may be allowed to share the same reserved backup capacity at an NF instance. Furthermore, CoShare factors in the heterogeneity in nodes, NF instances and availability requirements of flows in the design. The results from a number of experiments conducted using realistic network topologies show that the integration of structural dependency allows meeting availability requirements for more flows compared to a baseline approach. Specifically, CoShare is able to meet diverse availability requirements in a resource-efficient manner, requiring, e.g., up to 85% in some studied cases, less resource overbuild than the baseline approach that uses the idea ofdedicated reservationcommonly adopted for redundancy allocation in NFV.

Deep neural networks (DNNs) are increasingly used for real-time inference, requiring low latency, but require significant computational power as they continue to increase in complexity. Edge clouds promise to offer lower latency due to their proximity to end users and having powerful accelerators like GPUs to provide the computation power needed for DNNs. But it is also important to ensure that the edge-cloud resources are utilized well. For this, multiplexing several DNN models through spatial sharing of the GPU can substantially improve edge-cloud resource usage. Typical GPU runtime environments have significant interactions with the CPU, to transfer data to the GPU, for CPU-GPU synchronization on inference task completions, etc. These result in overheads. We present a DNN inference framework with a set of software primitives that reduce the overhead for DNN inference, increase GPU utilization and improve performance, with lower latency and higher throughput. Our first primitive uses the GPU DMA effectively, reducing the CPU cycles spent to transfer the data to the GPU. A second primitive uses asynchronous ‘events' for faster task completion notification. GPU runtimes typically preclude fine-grained user control on GPU resources, causing long GPU downtimes when adjusting resources. Our third primitive supports overlapping of model-loading and execution, thus allowing GPU resource re-allocation with very little GPU idle time. Our other primitives increase inference throughput by improving scheduling and processing more requests. Overall, our primitives decrease inference latency by more than 35% and increase DNN throughput by 2-3x.

Serverless computing platforms simplify development, deployment, and automated management of modular software functions. However, existing serverless platforms typically assume an over-provisioned cloud, making them a poor fit for Edge Computing environments where resources are scarce. In this paper we propose a redesigned serverless platform that comprehensively tackles the key challenges for serverless functions in a resource constrained Edge Cloud.

Despite advances in network security, attacks targeting mission critical systems and applications remain a significant problem for network and datacenter providers. Existing telemetry platforms detect volumetric attacks at terabit scales using approximation techniques and coarse grain analysis. However, the prevalence of low and slow attacks that require very little bandwidth, makes flow-state tracking critical to overall attack mitigation. Traffic queries deployed on network switches are often limited by hardware constraints, preventing them from carrying out flow tracking features required to detect stealthy attacks. Such attacks can go undetected in the midst of high traffic volumes.

Name-based pub/sub allows for efficient and timely delivery of information to interested subscribers. A challenge is assigning the right name to each piece of content, so that it reaches the most relevant recipients. An example scenario is the dissemination of social media posts to first responders during disasters. We present FLARE, a framework using federated active learning assisted by naming. FLARE integrates machine learning and name-based pub/sub for accurate timely delivery of textual information. In this demo, we show FLARE’s operation.

Data center workload fluctuations need periodic, but careful scheduling to minimize power consumption while meeting the task completion time requirements. Existing data center scheduling systems tightly pack containers to save power. However, with the growth of multi-tiered applications, there is a significant need to account for the affinity between application components, to minimize communication overheads and latency. Centralized container scheduling systems using graph partitioning algorithms cause a significant number of task migrations, with associated downtime.We design pMACH, a novel distributed container scheduling scheme for optimizing both power and task completion time in data centers. It minimizes task migrations and packs frequently communicating containers together without overloading servers. pMACH operates at peak energy efficiency, thus reducing energy consumption while also providing greater headroom for unpredictable workload spikes. We also propose in-network monitoring using smartNICs (sNIC) to measure the communications and then perform scheduling in a hierarchical, parallelized framework to achieve high performance and scalability. pMACH is based on incremental partitioning and it leverages the previous scheduling decision to significantly reduce the number of containers moved between servers, avoiding application downtime.Both testbed measurements and large-scale trace-driven simulations show that pMACH saves at least 13.44% more power compared to previous scheduling systems. It speeds task completion, reducing the 95th percentile by a factor of 1.76-2.11 compared to existing container scheduling schemes. Compared to other static graph-based approaches, our incremental partitioning technique reduces migrations per epoch by 82%.

This paper focuses on the need for emerging domains such as serverless and in-network computing, where applications are often hosted on virtualized compute instances (e.g., containers and unikernels), to have applications startup as quickly as possible. We provide a qualitative and quantitative analysis of containers and unikernels with regard to the startup time. We analyze these in-depth and identify the key components and their impact under scale on the startup latency. We study how startup time scales as we launch multiple instances concurrently. We study the contribution of popular Container Networking Interfaces (CNIs), to the startup time.

Measuring the Available Bandwidth (ABW) is an important function for traffic engineering, and in software-defined metro and wide-area network (SD-WAN) applications. Because network speeds are increasing, it is timely to re-visit the effectiveness of ABW measurement again. A significant challenge arises because of Interrupt Coalescence (IC), that network interface drivers use to mitigate the overhead when processing packets at high speed, but introduce packet batching. IC distorts receiver timing and decreases the ABW estimation. This effect is further exacerbated with software-based forwarding platforms that exploit network function virtualization (NFV) and the lower-cost and flexibility that NFV offers, and with the increased use of poll-mode packet processing popularized by the Data Plane Development Kit (DPDK) library. We examine the effectiveness of the ABW estimation with the popular probe rate models (PRM) such as PathChirp and PathCos++, and show that there is a need to improve upon them. We propose a modular packet batching mitigation that can be adopted to improve both the increasing PRM models like PathChirp and decreasing models like PathCos++. Our mitigation techniques improve the accuracy of ABW estimation substantially when packet batching occurs either at the receiver due to IC, DPDK based processing or intermediate NFV-based forwarding nodes. We also show that our technique helps improve estimation significantly in the presence of cross-traffic.

Edge cloud solutions that bring the cloud closer to the sensors can be very useful to meet the low latency requirements of many Internet-of-Things (IoT) applications. However, IoT traffic can also be intermittent, so running applications constantly can be wasteful. Therefore, having a serverless edge cloud that is responsive and provides low-latency features is a very attractive option for a resource and cost-efficient IoT application environment.In this paper, we discuss the key components needed to support IoT traffic in the serverless edge cloud and identify the critical challenges that make it difficult to directly use existing serverless solutions such as Knative, for IoT applications. These include overhead from heavyweight components for managing the overall system and software adaptors for communication protocol translation used in off-the-shelf serverless platforms that are designed for large-scale centralized clouds. The latency imposed by ‘cold start’ is a further deterrent.To address these challenges we redesign several components of the Knative serverless framework. We use a streamlined protocol adaptor to leverage the MQTT IoT protocol in our serverless framework for IoT event processing. We also create a novel, event-driven proxy based on the extended Berkeley Packet Filter (eBPF), to replace the regular heavyweight Knative queue proxy. Our preliminary experimental results show that the event-driven proxy is a suitable replacement for the queue proxy in an IoT serverless environment and results in lower CPU usage and a higher request throughput.

Selecting the right time budget for a search query is challenging because a proper balance between the search latency, quality and efficiency has to be maintained. State-of-the-art approaches leverage a centralized sample index at the aggregator to select the Index Serving Nodes (ISNs) to maintain quality and responsiveness. In this paper, we propose Cottage, a coordinated framework between the aggregator and ISNs for latency and quality optimization in web search. Cottage has two separate neural network models at each ISN to predict the quality contribution and latency, respectively. Then, these prediction results are sent back to the aggregator for latency and quality optimizations. The key task is integration of the predictions at the aggregator in determining an optimal dynamic time budget for identifying slow and low quality ISNs to improve latency and search efficiency. Our experiments on the Solr search engine prove that Cottage can reduce the average query latency by 54% and achieve a good P@10 search quality of 0.947.

Serverless computing is increasingly popular because of its lower cost and easier deployment.Several cloud service providers (CSPs) offer serverless computing on their public clouds, but it may bring the vendor lock-in risk.To avoid this limitation, many open-source serverless platforms come out to allow developers to freely deploy and manage functions on self-hosted clouds.However, building effective functions requires much expertise and thorough comprehension of platform frameworks and features that affect performance.It is a challenge for a service developer to differentiate and select the appropriate serverless platform for different demands and scenarios.Thus, we elaborate the frameworks and event processing models of four popular open-source serverless platforms and identify their salient idiosyncrasies.We analyze the root causes of performance differences between different service exporting and auto-scaling modes on those platforms.Further, we provide several insights for future work, such as auto-scaling and metric collection.Index Terms-cloud computing,

Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability, and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different Pods (a group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source `CNI plugins'. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this article, we compare the various open-source CNI plugins available from the community, qualitatively, and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, especially because of the interaction with the datapath/iptables as well as the host network stack. Overlay tunnel offload support in the network interface card plays a significant role in achieving the good performance of CNIs that use overlay tunnels for inter-host Pod-to-Pod communication. We also study scalability with an increasing number of Pods, as well as with HTTP workloads, and briefly evaluate Pod startup latency. Our measurement results inform the outline of an ideal CNI environment for Kubernetes.

Graph-based namespaces are being increasingly used to represent the organization of complex and ever-growing information eco-systems and individual user roles. Timely and accurate information dissemination requires an architecture with appropriate naming frameworks, adaptable to changing roles, focused on content rather than network addresses. Today’s complex information organization structures make such dissemination very challenging. To address this, we propose POISE, a name-based publish/subscribe architecture for efficient topic-based and recipient-based content dissemination. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking solutions in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. POISE supports in-network graph-based naming, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE supports adaptive load-sharing via multiple RPs, each managing a dynamically chosen subset of the namespace graph. Excessive workload may cause one RP to turn into a “hot spot”, impeding performance and reliability. To eliminate such traffic concentration, we propose an automated load-splitting mechanism, consisting of an enhanced, namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid, iterative bi-partitioning solution, consisting of an initial and a refinement phase. We also implemented POISE on a DPDK-based platform. Using the important application of emergency response, our experimental results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing.

Named Data Networking (NDN) has many forwarding behaviors, strategies, and protocols to enable the benefits of Information-Centric Networking. This additional functionality introduces complexity, motivating the need for a tool to help reason about and verify that basic properties of an NDN data plane are guaranteed. This paper proposes Name Space Analysis (NSA), a network verification framework to model and analyze NDN data planes. NSA can take as input one or more snapshots, each representing a state of the data plane. It then provides the verification result against specified properties. NSA builds on the theory of Header Space Analysis, and extends it in a number of ways, e.g., supporting variable-sized headers with flexible formats, introduction of name space functions, allowing for name-based properties such as content reachability and name leakage-freedom, and multi-snapshot verification such as equivalence checks. These important additions reflect the behavior and requirements of NDN, requiring modeling and verification foundations fundamentally different from those of traditional host-centric networks. As a case study, we show how NSA can detect name space conflicts in NDN, which can be often hard to catch. Leveraging the learning from this study, we outline a conflict detection and resolution protocol and a name space registry to avoid such conflicts. We have implemented NSA and identified a number of optimizations to enhance the efficiency of verification. Results from our evaluations, using snapshots from various synthetic test cases and the real-world NDN testbed, show how NSA is effective, in finding errors, has good performance, and is scalable.

The increasing demand for cloud-based inference services requires the use of Graphics Processing Unit (GPU). It is highly desirable to utilize GPU efficiently by multiplexing different inference tasks on the GPU. Batched processing, CUDA streams and Multi-process-service (MPS) help. However, we find that these are not adequate for achieving scalability by efficiently utilizing GPUs, and do not guarantee predictable performance.

Predicting a user's field-of-view (FoV) accurately can help to significantly reduce the high bandwidth requirements for 360° video streaming, as it enables sending only the tiles corresponding to the predicted FoV. Since many approaches for user head-orientation (i.e., FoV) prediction have been proposed in the literature, ranging from simple linear regression to more complex neural networks, it is difficult to comprehensively decide which method to use. Towards resolving this gap in knowledge, in this work we benchmark user prediction algorithms over an aggregation of multiple datasets and study the implications of this analysis. Our results demonstrate that it is indeed difficult for any prediction algorithm to accurately predict a user's FoV beyond a very short future time window of approximately 300 ms. We also observe that users' viewing behavior is dominated by sideways head movement, rather than up-and-down. These findings have implications on network bandwidth, latency, and playback buffering at the client: (1) Extra "padding" tiles are needed around the user's FoV in order to correct for prediction errors; in particular, a rectangular padding achieves lower stall rate than square padding, for the same bandwidth usage; (2) Video playout buffers, network delay, and jitter need to be small in order to avoid stale predictions of the user's field-of-view, which are only valid 300 ms into the future; (3) Per-video and per-user personalization of the padding can save bandwidth for slow-moving users or videos. We mathematically quantify these tradeoffs and present simulation results to demonstrate these findings and implications. Our results have implications for FoV prediction methods in future 360°streaming systems.

With the increasing diversity of application needs (datacenters, IoT, content retrieval, industrial automation, etc.), new network architectures are continually being proposed to address specific and particular requirements. From a network management perspective, it is both important and challenging to enable evolution towards such new architectures. Given the ubiquity of the Internet, a clean-slate change of the entire infrastructure to a new architecture is impractical. It is believed that we will see new network architectures coming into existence with support for interoperability between separate architectural islands. We may have servers, and more importantly, content, residing in domains having different architectures. This paper presents COIN, a content-oriented interoperability framework for current and future Internet architectures. We seek to provide seamless connectivity and content accessibility across multiple of these network architectures, including the current Internet. COIN preserves each domain's key architectural features and mechanisms, while allowing flexibility for evolvability and extensibility. We focus on Information-Centric Networks (ICN), the prominent class of Future Internet architectures. COIN avoids expanding domain-specific protocols or namespaces. Instead, it uses an application-layer Object Resolution Service to deliver the right "foreign" names to consumers. COIN uses translation gateways that retain essential interoperability state, leverages encryption for confidentiality, and relies on domain-specific signatures to guarantee provenance and data integrity. Using NDN and MobilityFirst as important candidate solutions of ICN, and IP, we evaluate COIN. Measurements from an implementation of the gateways show that the overhead is manageable and scales well.

Edge clouds can provide very responsive services for end-user devices that require more significant compute capabilities than they have. But edge cloud resources such as CPUs and accelerators such as GPUs are limited and must be shared across multiple concurrently running clients. However, multiplexing GPUs across applications is challenging. Further, edge servers are likely to require considerable amounts of streaming data to be processed. Getting that data from the network stream to the GPU can be a bottleneck, limiting the amount of work GPUs do. Finally, the lack of prompt notification of job completion from GPU also results in ineffective GPU utilization. We propose a framework that addresses these challenges in the following manner. We utilize spatial sharing of GPUs to multiplex the GPU more efficiently. While spatial sharing of GPU can increase GPU utilization, the uncontrolled spatial sharing currently available with state-of-the-art systems such as CUDA-MPS can cause interference between applications, resulting in unpredictable latency. Our framework utilizes controlled spatial sharing of GPU, which limits the interference across applications. Our framework uses the GPU DMA engine to offload data transfer to GPU, therefore preventing CPU from being bottleneck while transferring data from the network to GPU. Our framework uses the CUDA event library to have timely, low overhead GPU notifications. Preliminary experiments show that we can achieve low DNN inference latency and improve DNN inference throughput by a factor of ~ 1.4.

In many scenarios, information must be disseminated over intermittently-connected environments when network infrastructure becomes unavailable. Example scenarios include disasters in which first responders need to send updates about their critical tasks. If such updates pertain to a shared data set (e.g., pins on a map), their consistent dissemination is important. We can achieve this through causal ordering and consensus. Popular consensus algorithms, such as Paxos and Raft, are most suited for connected environments with reliable links. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users' views, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends basic OTR to support long-term fragmentation and critical decision invalidation scenarios. We integrate the multilevel consistency schema of CoNICE, with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Performing city-scale simulation experiments, we demonstrate that CoNICE is effective in achieving its consistency goals, and is efficient and scalable in the time for convergence and utilized network resources.

The service quality of web search depends considerably on the request tail latency from Index Serving Nodes (ISNs), prompting data centers to operate them at low utilization and wasting server power. ISNs can be made more energy efficient utilizing Dynamic Voltage and Frequency Scaling (DVFS) or sleep states techniques to take advantage of slack in latency of search queries. However, state-of-the-art frameworks use a single distribution to predict a request's service time and select a high percentile tail latency to derive the CPU's frequency or sleep states. Unfortunately, this misses plenty of energy saving opportunities. In this paper, we develop a simple linear regression predictor to estimate each individual search request's service time, based on the length of the request's posting list. To use this prediction for power management, the major challenge lies in reducing miss rates for deadlines due to prediction errors, while improving energy efficiency. We present Swan, a two-Step poWer mAnagement for distributed search eNgines. For each request, Swan selects an initial, lower frequency to optimize power, and then appropriately boosts the CPU frequency just at the right time to meet the deadline. Additionally, we re-configure the time instant for boosting frequency, when a critical request arrives and avoid deadline violations. Swan is implemented on the widely-used Solr search engine and evaluated with two representative, large query traces. Evaluations show Swan outperforms state-of-the-art approaches, saving at least 39% CPU power on average.

Recent developments in In-band Network Telemetry (INT) provide granular monitoring of performance and load on network elements by collecting information in the data plane. INT enables traffic sources to embed telemetry instructions in data packets, avoiding separate probing or infrequent management-based monitoring. INT sink nodes track and collect metrics by retrieving INT metadata instructions appended by different sources of INT information. However, tracking the INT state in packets arriving at the sink is both compute intensive (requiring complex operations on each packet), and challenging for the standard P4 match-action packet processing pipeline to maintain line-rate. We propose a network telemetry platform in which the INT sink is implemented using distinct (C-based) algorithms on a SmartNIC in the monitoring host, complementing the P4 packet processing pipeline. This design accelerates packet processing and handles complex INT-related operations more efficiently than P4 match-action processing alone. While the P4 pipeline parses INT headers, a general-purpose Micro-C algorithms performs complex INT tasks (e.g. aggregation, event-detection, notification, etc.). We demonstrate that partitioning of INT processing significantly reduces processing overhead vs. a P4-on1y implementation, providing accurate, timely and almost loss-free event notification.

Ensuring high scalability (elastic scale-out and consolidation), as well as high availability (failure resiliency) are critical in encouraging adoption of software-based network functions (NFs). In recent years, two paradigms have evolved in terms of the way the NFs manage their state - namely the Stateful (state is coupled with the NF instance) and a Stateless (state is externalized to a datastore) manner. These two paradigms present unique challenges and opportunities for ensuring high scalability and high availability of NFs and NF chains. In this work, we assess the impact on ensuring the correctness of NF state including the implications of non-determinism in packet processing, and carefully analyze and present the benefits and disadvantages of the two state management paradigms. We leverage OpenNetVM and Redis in-memory datastore to implement both state management paradigms and empirically compare the two. Although the stateless paradigm is desirable for elastic scaling, our experimental results show that, even at line-rate packet processing (10 Gbps), stateful NFs can achieve chain-level failover across servers in a LAN incurring less than 10% performance. The state-of-the-art stateless counterparts incur severe throughput penalties. We observe 30-85% overhead on normal processing, depending on the mode of state updated to the externalized datastore.

BBR, a congestion control algorithm proposed by Google, regulates the source sending rate by deriving an estimate of the bottleneck’s available bandwidth and RTT of the path. The initial version of BBR, called BBRvl, was found to be unfair, getting higher than the fair share of bandwidth when co-existing on bottleneck links with other congestion control algorithms. It also does not perform as well with networks having routers with shallow buffers. To overcome these concerns, a newer version, called BBRv2, has been proposed. Our goal in this paper is to understand the differences between the two versions and examine the primary reasons behind the improvement in performance of BBRv2. We present an experimental evaluation of BBRvl and BBRv2, evaluating their fairness across connections using the same protocol (intra-protocol fairness) and using different protocols (inter-protocol fairness) as well as delay and link utilization. From experiments with shallow and deep buffers, BBRv2 is most effective when it uses Explicit Congestion Notification (ECN), but fairness issues continue to exist in BBRv2 when ECN is disabled. A concern for BBRv2 is that it is somewhat complex to deploy in Wide Area Networks (WAN) because of the dependency with the DCTCP-style reduction of the congestion window, which is primarily usable in low-feedback delay Data Center Networks.

Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different pods (group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source ‘CNI plugins’. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this paper, we compare the various open source CNI plugins available from the community, qualitatively and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, as a result of the network model it implements, interaction with the host network protocol stack and the network policies implemented in iptables rules. The choice of the CNI plugin may also be based on whether intra-host or inter-host communication dominates.

Saving energy for latency-critical applications like web search can be challenging because of their strict tail latency constraints. State-of-the-art power management frameworks use Dynamic Voltage and Frequency Scaling (DVFS) and Sleep states techniques to slow down the request processing and finish the search just-in-time. However, accurately predicting the compute demand of a request can be difficult. In this paper, we present Gemini, a novel power management framework for latency-critical search engines. Gemini has two unique features to capture the per query service time variation. First, at light loads without request queuing, a two-step DVFS is used to manage the CPU power. Our two-step DVFS selects the initial CPU frequency based on the query specific service time prediction and then judiciously boosts the initial frequency at the right time to catch-up to the deadline. The determination of boosting time further relies on estimating the error in the prediction of individual query's service time. At high loads, where there is request queuing, only the current request being executed and the critical request in the queue adopt a two-step DVFS. All the other requests in-between use the same frequency to reduce the frequency transition overhead. Second, we develop two separate neural network models, one for predicting the service time and the other for the error in the prediction. The combination of these two predictors significantly improves the power saving and tail latency results of our two-step DVFS. Gemini is implemented on the Solr search engine. Evaluations on three representative query traces show that Gemini saves 41% of the CPU power, and is better than other state-of-the-art techniques.

Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN within 10ms, incurring less than 10% performance overhead, and adds average latency only ~400 μs, with a worst-case latency of less than 1ms. REINFORCE also recovers from software failures within the same node in less than 100 μs, incurring less than 1% performance overhead and adds less than 5 μs latency during normal operation.

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and latency) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.

With the rapid increase in the number of users and changing pattern of network usage, cellular networks will continue to be challenged meeting bandwidth and latency requirements. A significant contributor to latency and overhead is cellular network's complex control-plane. We propose CleanG, a new packet core architecture and significantly more efficient control-plane protocol, that exploits the capabilities of modern-day Network Function Virtualization (NFV) platforms. CleanG is a single component NFV-based architecture. With the elastic scalability offered by NFV, the data and control sub-components of the core can scale, adapting to workload demand. CleanG eliminates the use of GTP tunnels for data packets and the associated complex protocol for coordination across multiple, distributed components for setting up and managing them. We carefully examine the use of each protocol message exchange (and the component fields of those messages) in developing a substantially simplified protocol, while retaining similar essential functionality for security, mobility, and air-interface resource management. We have implemented CleanG on the OpenNetVM platform and perform an apples-to-apples comparison with the existing 3GPP LTE architecture and an architecture that separates the control and user plane (the CUPS-based architecture like the 5G architecture). Measurements on our testbed show that CleanG substantially reduces both control and data plane latency, and significantly increases system capacity.

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

Software-based service chains in Network Function Virtualization (NFV) typically suffers high processing latency. This latency grows as chain lengths increase and possibly violates application requirements. Previous efforts focus on reducing latency while maintaining the perspective of each NF being an independent, isolated module. This results in processing redundancy that could eventually become the performance bottleneck. In this paper, we propose a low-latency NFV framework called SpeedyBox, that innovatively enables cross-NF runtime optimizations in a service chain to eliminate processing redundancy. SpeedyBox builds a fast data path for flows at runtime by consolidating the aggregate actions across diverse network functions (NFs) in a service chain. In SpeedyBox, each NF is instrumented with a stateful Local Match-Action Table (MAT), and leverages our easy-to-use APIs to record its per-flow behavior in the Local MAT. Next, SpeedyBox uses a Global MAT to build the fast data path by consolidating actions from each Local MAT, while providing the ability to express the stateful NF behaviors with an Event Table. We have implemented a prototype of SpeedyBox on the BESS and OpenNetVM NFV platforms. Our trace-driven evaluation on common NFs shows that SpeedyBox achieves significant latency reduction under real world scenarios.

Power management in data centers is challenging because of fluctuating workloads and strict task completion time requirements. Recent resource provisioning systems, such as Borg and RC-Informed, pack tasks on servers to save power. However, current power optimization frameworks based on packing leave very little headroom for spikes, and the task completion times are compromised. In this paper, we design Goldilocks, a novel resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm. Containers communicating frequently are placed together, which improves the task completion times. We also leverage new findings on power consumption of modern-day servers to ensure that their utilizations are in a range where they are power-proportional. Both testbed implementation measurements and large-scale trace-driven simulations prove that Goldilocks outperforms all the previous works on data center power saving. Goldilocks saves power by 11.7%-26.2% depending on the workload, whereas the best of the implemented alternatives, Borg, saves 8.9%-22.8%. The energy per request for the Twitter content caching workload in Goldilocks is only 33% of RC-Informed. Finally, the best alternative in terms of task completion time, E-PVM, has 1.17-3.29 times higher task completion times than Goldilocks across different workloads.

Effective communication among first responders during and in the aftermath of a disaster can affect outcomes dramatically. We seek to build a resilient architecture that allows first responders to communicate even with: 1) damage to infrastructure - civilian and / or specialized communication facilities may be damaged by the disaster, and 2) dynamically formed groups - first responder teams may be formed dynamically in response to a disaster and team member addresses (e.g., phone numbers, network addresses) may not be known to one another. We propose a resilient network architecture that allows efficient communication among first responders during and after a disaster [1]. We seek to support dynamically formed groups for incident response, allowing first responders to securely and conveniently communicate based on roles (names). The architecture supports communication in disasters by 1) building resilience into the framework across all the layers, 2) creating a framework that allows communication by role and identity, rather than addresses, 3) supporting multiple modalities (data, voice) for communication among dynamically formed first responder teams, and 4) providing robust and resilient communication and computing even when facilities are error- and disruption-prone.

Timely, flexible and accurate information dissemination can make a life-and-death difference in managing disasters. Complex command structures and information organization make such dissemination challenging. Thus, it is vital to have an architecture with appropriate naming frameworks, adaptable to the changing roles of participants, focused on content rather than network addresses. To address this, we propose POISE, a name-based and recipient-based publish/subscribe architecture for efficient content dissemination in disaster management. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking (ICN) solutions such as Named Data Networking (NDN) in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. To capture the complexity and dynamicity of disaster response command chains and information flows, POISE proposes a graph-based naming framework, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE allows load-sharing via multiple RPs each managing a subset of the namespace graph. However, excessive workload on one RP may turn it into a “hot spot”, thus impeding performance and reliability. To eliminate such traffic concentration, we propose an automatic load-splitting mechanism, consisting of a namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid partitioning solution, consisting of an initial and a refinement phase. Our simulation results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing.

The current Internet protocol suite, with its best-effort semantics, can result in potentially very different delivery characteristics for packets. Actually, no two paths (or even different packet flows on the same path) can be assumed to have identical properties in terms of bandwidth, delay and jitter. However, multi-site remote collaboration applications are highly inter-dependent and must remain consistent across multiple users. To this end, we introduce a new network capability, called coordinated communication service, and propose coordination points to support coordinated delivery of multiple flows in the network.

With the rapid increase in the number of users and changing pattern of network usage, cellular networks will continue to be challenged meeting bandwidth and latency requirements. A significant contributor to latency and overhead in cellular networks is the complex control-plane involving many message exchanges across multiple components in the packet core, base station, and user equipment. We propose CleanG, a new packet core architecture and significantly more efficient control-plane protocol, that exploits the capabilities of modern-day Network Function Virtualization (NFV) platforms. In CleanG, we have consolidated the core components into a set of virtual network functions on an NFV platform. With the elastic scalability offered by NFV, the data and control sub-components of the core functions can scale, adapting to workload demand. CleanG eliminates the use of GPRS Tunneling Protocol (GTP) Tunnels for data packets and the associated complex protocol for coordination across multiple, distributed components for setting up and managing them, as specified in the 3rd Generation Partnership Project (3GPP) architecture and protocol standard, while retaining similar essential functionality for security, mobility, and air-interface resource management. Measurements on our testbed show that CleanG substantially reduces both control and data plane latency, and significantly increases system capacity.

Modern mobile wireless networks have been demonstrated to be high-speed but lossy, while mobile applications have more strict requirements including reliability, goodput guarantee, bandwidth efficiency, and computation efficiency. Such a complicated combination of requirements and conditions in networks pushes the pressure to transport layer protocol design. We analyze and argue that few of existing network transport layer solutions are able to handle all these requirements. We design and implement Sphinx to satisfy the four requirements in high-speed and lossy networks. Sphinx has (1) a proactive coding-based method named semi-random LT codes for loss recovery, which estimates packet loss rate and adjusts the redundancy level accordingly, (2) a reactive retransmission method named Instantaneous Compensation Mechanism (ICM) for loss retransmission, which compensates the lost packets once actual loss exceeds the estimation, and (3) a parallel coding architecture, which leverages multi-core, shared memory and kernel-bypass DPDK. Prototype and evaluation show that Sphinx outperforms TCP and other coding solutions significantly in microbenchmarks across all four requirements, and improves the performance of applications such as video streaming and block data transfer.

Serverless computing platforms have gained popularity because they allow easy deployment of services in a highly scalable and cost-effective manner. By enabling just-in-time startup of container-based services, these platforms can achieve good multiplexing and automatically respond to traffic growth, making them particularly desirable for edge cloud data centers where resources are scarce. Edge cloud data centers are also gaining attention because of their promise to provide responsive, low-latency shared computing and storage resources. Bringing serverless capabilities to edge cloud data centers must continue to achieve the goals of low latency and reliability. The reliability guarantees provided by serverless computing however are weak, with node failures causing requests to be dropped or executed multiple times. Thus serverless computing only provides a best effort infrastructure, leaving application developers responsible for implementing stronger reliability guarantees at a higher level. Current approaches for providing stronger semantics such as “exactly once” guarantees could be integrated into serverless platforms, but they come at high cost in terms of both latency and resource consumption. As edge cloud services move towards applications such as autonomous vehicle control that require strong guarantees for both reliability and performance, these approaches may no longer be sufficient. In this paper we evaluate the latency, throughput, and resource costs of providing different reliability guarantees, with a focus on these emerging edge cloud platforms and applications.

As the next generation of cellular networks (5G) nears deployment, autonomous smart nodes, such as Internet of Things (IoT) and vehicular devices, are being developed to take advantage of increased throughput and improved resiliency offered by the new link layer protocols of 5G. Evaluating the impact of real-time 5G air-interface scheduling algorithms on the stringent real-time delivery needs of these devices and their new applications is no small feat. In this short paper, we expand upon the mmwave (5G) module for NS-3 and present DI5GUISE, a configurable and highly dynamic real-time simulated testbed framework, through which the impact of these smart nodes can be investigated. We utilise real-time video delivery as an example of applications with high throughput requirements. The experimental results illustrate that even on low cost commodity hardware, such as Raspberry Pis, over 80Mbps per client of real-time streaming of 4K content can be achieved.

Real-time applications such as autonomous and connected cars, surveillance, and online learning applications have to train on streaming data. They require low-latency, high throughput machine learning (ML) functions resident in the network and in the cloud to perform learning and inference. NFV on edge cloud platforms can provide support for these applications by having heterogeneous computing including GPUs and other accelerators to offload ML-related computation. GPUs provide the necessary speedup for performing learning and inference to meet the needs of these latency sensitive real-time applications. Supporting ML inference and learning efficiently for streaming data in NFV platforms has several challenges. In this paper, we present a framework, NetML, that runs existing ML applications on an heterogeneous NFV platform that includes both CPUs and GPUs. NetML efficiently transfers the appropriate packet payload to the GPU, minimizing overheads, avoiding locks, and avoiding CPU-based data copies. Additionally, NetML minimizes latency by maximizing overlap between the data movement and GPU computation. We evaluate the efficiency of our approach for training and inference using popular object detection algorithms on our platform. NetML reduces the latency for inferring images by more than 20% and increases the training throughput by 30% while reducing CPU utilization compared to other state-of-the-art alternatives.

Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NFs and NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN (or within the same node) within 10ms (100/μs), incurring less than 10% (1%) performance overhead, and adds average latency of only ~400/μs (5/μs), with a worst-case latency of less than 1ms (10/μs).

Achieving energy proportionality in data centers supporting latency-sensitive applications is challenging because of the strict Service Level Agreements. Previous works individually focus on making the server energy proportional or reducing the data center network's power consumption for latency-tolerant applications. In this paper, we propose EPRONS to minimize the overall data center's power consumption with latency-sensitive applications by trading-off network slack in favor of providing additional slack for computations. We utilize the linear programming model to consolidate latency-sensitive search queries and latency-tolerant background flows to a minimal subnet of the topology by turning off unused switches and links without violating the application deadlines. Servers take advantage of the additional 'network-provided' slack to allow slowing down request processing. For servers, we design a novel power saving technique using Dynamic Voltage and Frequency Scaling (DVFS) based on the average tail latency of a request. If needed, we turn on a minimal number of additional network links and switches to reduce network latency while still maximizing entire data center's power saving. Experimental results show that our scheme saves up to 31.25% of a data center's total power budget.

Traditional means for contacting emergency responders depend critically on the availability of the 911 service to request help. Large-scale natural disasters such as hurricanes and earthquakes often result in overloading and sometimes failure of communication facilities. Affected citizens are increasingly using social media to obtain and disseminate information. Social media is not only being used to communicate with first responders but also for people to organically volunteer and seek help from each other, complementing the role of first responders. In this paper, we examine the use of Twitter during two major hurricanes in the U.S. in 2017. We find that there exists a sizable number of people with access to the Internet even in areas where 911 services were down, and they tweet disaster-related information including requests for help. Our analysis indicates that social media can potentially help in disaster management and improve outcomes.

Constrained Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) are two IoT application layer protocols that are seeing increased attention and industry deployment. CoAP uses a request-response model and runs over UDP, while MQTT follows a publish-subscribe model running over TCP. For more constrained IoT devices, MQTTSensor Networks (MQTT-SN) provides a UDP-based transport between the sensor and an MQTT-SN gateway, while using TCP between that gateway and the MQTT broker. Quick UDP Internet Connections (QUIC) is a new protocol and although not originally designed for IoT devices, some design features such as reduced connection establishment time may be useful in an IoT environment. Each of these protocols seeks optimizations in features and implementation complexity based on application domains rather than having the full flexibility and adaptability of traditional transport protocols such as TCP. We investigate and analyze four protocols, namely, CoAP, MQTT, MQTT-SN and QUIC, to understand the overhead of obtaining data from an IoT device at a sink to potentially disseminate this data downstream. These constrained IoT devices often operate under challenging, varying network conditions, and it is important to understand the limitations of the protocols in such conditions. Thus, we evaluate the performance of these protocols under varying loss, delay and disruption conditions to identify the most effective environment for their operation and understand the limitations of their dynamic range. Results show that with non-confirmable CoAP a more adaptive wait timer is required; and a more streamlined QUIC protocol may be a potential alternative IoT protocol.

Existing network service chaining frameworks are based on a "packet-centric" model where each NF in a chain is given every packet for processing. This approach becomes both inefficient and inconvenient for more complex network functions that operate at higher levels of the protocol stack. We propose Microboxes, a novel service chaining abstraction designed to support transport- and application-layer middle-boxes, or even end-system like services. Simply including a TCP stack in an NFV platform is insufficient because there is a wide spectrum of middlebox types-from NFs requiring only simple TCP bytestream reconstruction to full endpoint termination. By exposing a publish/subscribe-based API for NFs to access packets or protocol events as needed, Microboxes eliminates redundant processing across a chain and enables a modular design. Our implementation on a DPDK-based NFV framework can double throughput by consolidating stack operations and provide a 51% throughput gain by customizing TCP processing to the appropriate level.

As the deployment of software-defined networks increases, so does the manageability of local and wide area networks. Designing intelligent solutions that respond to traffic changes automatically will soon become a mandatory requirement in production networks. In this paper, we focus on designing an intelligent control plane for the MobilityFirst Future Internet architecture. This architecture proposes novel mechanisms to replace the Internet Protocol to better support content delivery and mobility, such as hop-by-hop transfer, storage-aware routing and separation of identifiers and network addresses. In earlier work, we have argued that these mechanisms can be bypassed for certain data flows. Indeed, when there is no mobility involved, it is more convenient to implement cut-through switching at lower layers to bypass the routing mechanisms. In this paper, we propose an inter-domain framework capable of cut-through switching in MobilityFirst. The proposed framework is capable of adding and removing flows from tunnels automatically. It is also capable of creating inter-domain tunnels based on flow behavior and inter-domain latency. Our implementation experiments show that the control plane delay can be reduced by 75% when using inter-domain tunnels. Furthermore, the results also show how our framework needs fewer messages than current protocols such as label distribution protocol to setup intra-domain and inter-domain tunnels.

Network function virtualization (NFV) implements network middleboxes in software, enabling them to be more flexible and dynamic. NFV resource allocation methods can exploit the capabilities of virtualization to dynamically instantiate network functions (NFs) to adapt to traffic demand and network conditions. Deploying NFs requires decisions for NF placement, and routing of flows through these NFs in accordance with the sequence of NFs required to process each flow. The challenges in developing an NFV resource allocation scheme include the need to manage the dependency between flow-level (routing) and network-level (placement) decisions and to efficiently utilize resources that may be distributed network-wide, while fulfilling the performance requirements of flows. We propose a scalable resource allocation scheme, called ClusPR, that addresses these challenges. By elegantly capturing the dependency between flow routing and NF placement, ClusPR strikes a balance between multiple objectives including minimizing path stretch, balancing the load among NF instances, while maximizing the total network utilization by accommodating the maximum number of flows possible. ClusPR addresses the offline problem of NFV resource allocation. To address the online problem of dynamically placing and routing flows upon their arrival, we propose iClusPR. iClusPR is an online algorithm that performs dynamic scaling by adjusting the number of NF instances based on the traffic demand and the network state. Our experiments show that ClusPR achieves the near-optimal solution for a practical large-sized network in reasonable time. Compared to the state-of-the-art approaches, ClusPR decreases the average normalized delay by a factor of 1.2 - 1.6 × and the worst-case delay by more than 10 ×, with the same or slightly better network utilization and balances the load among NF instances. Furthermore, the performance of iClusPR, the online version, is comparable to the offline ClusPR algorithm.

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.

Determining the location of a mobile user is central to several crowd-sensing applications. Using a Global Positioning System is not only power-hungry, but also unavailable in many locations. While there has been work on cellular-based localization, we consider an unexplored opportunity to improve location accuracy by combining cellular information across multiple mobile devices located near each other. For instance, this opportunity may arise in the context of public transport units having multiple travelers. Based on theoretical analysis and an extensive experimental study on several public transportation routes in two cities, we show that combining cellular information across nearby phones considerably improves location accuracy. Combining information across phones is especially useful when a phone has to use another phone’s fingerprint database, in a fingerprinting-based localization scheme. Both the median and 90 percentile errors reduce significantly. The location accuracy also improves irrespective of whether we combine information across phones connected to the same or different cellular operators. Sharing information across phones can raise privacy concerns. To address this, we have developed an id-free broadcast mechanism, using audio as a medium, to share information among mobile phones. We show that such communication can work effectively on smartphones, even in real-life, noisy-road conditions.

We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.

Monitoring of residences and businesses can be effectively performed using machine learning algorithms. As sensors and devices used for monitoring become more complex, having humans process the information to detect intrusions would be expensive and difficult to scale. We propose an automated home/business monitoring system which resides on edge servers performing online learning on streaming data coming from homes and businesses in the neighborhood. The edge servers run Open-NetVM, a Network Function Virtualization (NFV) platform, and host multiple machine learning applications instantiated on demand. This enables us to serve a set of customers in the neighborhood on a timely basis, permitting customization and learning of the behavior of each home. We combine the results of the multiple classifiers, with each classifier examining a distinct feature related to a distinct sensor, to finally infer whether the entry is a normal one or an intrusion. Our results show that our system is able to classify intrusions better than basing the decision on a single classifier, thus reducing false alarms. We have also shown that our system can effectively scale and monitor thousands of homes.

Vehicles are increasingly equipped with sensors for safety applications. Sharing information among vehicles can further improve the safety of the overall transportation environment. Enabling each vehicle to get the “right information at the right time” can be valuable to avoid dangerous situations. Information-Centric Networks (ICN) that use the notion of “named-objects” enable information dissemination regardless of location of the publisher or consumer. ICNs, especially supporting publish/subscribe capabilities, can provide timely delivery of the safety information. Our V-ICE architecture utilizes Roadside Units (RSUs) as infrastructure-based aggregators to communicate with vehicles generating notifications. RSUs disseminate information to vehicles that subscribe to the RSUs on their route. To evaluate the benefit of V-ICE, we demonstrate its use in propagating “black ice” warnings to vehicles that will likely be affected on their routes. The critical need is to deliver the information in a timely manner, providing other vehicles sufficient time to react. We build V-ICE's namespace and architecture using the roadways of Luxembourg as an example, and evaluate our approach with a trace-driven simulation using a 4-hour trace generated by SUMO. We show that V-ICE performs better than a server-based approach or even V2V broadcast, in terms of timeliness, relevance, and reduced network traffic.

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric.

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and loss) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.

The combination of Network Function Virtualization (NFV) and Software Defined Networking (SDN) allows flows to be flexibly steered through efficient processing pipelines. As deployment of NFV becomes more prevalent, the need to provide fine-grained customization of service chains and flow-level performance guarantees will increase, even as the diversity of Network Functions (NFs) rises. Existing NFV approaches typically route wide classes of traffic through pre-configured service chains. While this aggregation improves efficiency, it prevents flexibly steering and managing performance of flows at a fine granularity.

Future Internet projects such as MobilityFirst and Named Data Networking have proposed novel mechanisms to replace the Internet Protocol to better support content delivery and mobility. However, the problem of efficient data transfer across the network core has not been adequately investigated. We tackle the challenge of inter-domain cut-through switching using software-defined networking (SDN). First, we propose and solve an optimization problem that minimizes the total transfer time using inter-domain tunnels. Second, we propose an SDN-based routing framework for the MobilityFirst architecture capable of dynamically creating such tunnels. The main novelty of this framework is to name tunnels as network objects to simplify how tunnels are created and maintained. To validate our framework, we implement on the GENI (Global Environment for Network Innovations) testbed a prototype for the MobilityFirst architecture. Our experiments with the optimization problem show that the inter-domain latency between controllers plays a key role on how tunnels are setup. Furthermore, our implementation experiments show that the control plane delay can be reduced by 75% when using inter-domain tunnels. Finally, we show how our framework needs fewer messages than current protocols such as label distribution protocol (LDP) to setup intra-domain and inter-domain tunnels.

Summary form only given. Communication networks are changing: they are becoming more and more "software-based." The use of network function virtualization (NFV) to run network services in software enables software-defined networks (SDNs) to create a largely software-based network. To truly achieve the vision of a high-performance software-based network that is flexible, lower- cost, and agile, a fast and carefully designed network function virtualization platform along with a comprehensive SDN control plane is needed.Our high-performance NFV platform, OpenNetVM, enables high bandwidth network functions to operate at near line speed, while taking advantage of the flexibility and customization of low cost commodity servers. OpenNetVM exploits Intel's DPDK libraries to minimize the overhead of packet processing, and to provide high throughput, low latency networking in virtualized environments. OpenNetVM allows true zero-copy delivery of data to VMs, both for packet processing and high-speed inter-VM communication through shared huge pages within a trust boundary. We envision a dynamic and flexible network that can support a smarter data plane than just simple switches that forward packets. We build on our OpenNetVM NFV platform by developing our SDNFV network architecture that supports complex stateful routing of flows where processing by network functions (NFs) can dynamically modify the path taken by flows, without unduly burdening the centralized SDN controller. The tutorial will also briefly touch upon the problem of dynamic placement of network functions and routing of flows through a software based network, exploiting a mixture of centralized SDN control and NFV capabilities in the network. The problem can be formulated as a mixed integer linear programming problem, and heuristics can be used to solve the problem incrementally.As a case study, we examine the growing communication needs of 'Internet-of-Things' (IoT). With 'smart' sensing devices becoming ubiquitous, there is a need to support IoT communication at large scale, especially over cellular networks. The use of NFV platforms for the Evolved Packet Core, we see opportunities for supporting IoT communications at large scale in 5G cellular networks. We describe potential solutions in this direction.

As demand for wireless mobile connectivity continues to explode, cellular network infrastructure capacity requirements continue to grow. While 5G tries to address capacity requirements at the radio layer, the load on the cellular core network infrastructure (called Enhanced Packet Core (EPC)) stresses the network infrastructure. Our work examines the architecture, protocols of current cellular infrastructures and the workload on the EPC. We study the challenges in dimensioning capacity and review the design alternatives to support the significant scale up desired, even for the near future. We breakdown the workload on the network infrastructure into its components-signaling event transactions; database or lookup transactions and packet processing. We quantitatively show the control plane and data plane load on the various components of the EPC and estimate how future 5G cellular network workloads will scale. This analysis helps us to understand the scalability challenges for future 5G EPC network components. Other efforts to scale the 5G cellular network take a system view where the control plane is separated from the data path and is terminated on a centralized SDN controller. The SDN controller configures the data path on a widely distributed switching infrastructure. Our analysis of the workload informs us on the feasibility of various design alternatives and motivates our efforts to develop our clean-slate approach, called CleanG.

Information-Centric Networking (ICN) treats content as a first-class entity - each content has a unique identity and ICN routers forward traffic based on content identity rather than the locations of the content. This provides benefits like dynamic request routing, caching and mobility support. The choice of naming schema (flat vs. hierarchical) is a fundamental design choice in ICN which determines the functional separation between the network layer and the application layer. With hierarchical names, the network layer is cognizant of the semantics of hierarchical names. Name space management is also part of network layer. ICN architectures using flat names leave these to the application layer. The naming schema affects the performance and scalability of the network in terms of forwarding efficiency, routing table size and name space size. This paper provides both qualitative and quantitative comparison on the two naming schemas using these metrics, noting that they are interdependent. We seek to understand which naming schema would be better for a high-performance, scalable ICN architecture.

In this demonstration we present a platform that encompasses all of the components required to realistically evaluate the performance of Dynamic Adaptive Streaming over HTTP (DASH) over a real-time NS-3 simulated network. Our platform consists of a network-attached storage server with DASH video clips and a simulated LTE network which utilises the NS-3 LTE module provided by the LENA project. We stream to clients running an open-source player with a choice of adaptation algorithms. By providing a user interface that offers user parametrisation to modify both client and LTE settings, we can view the evaluated results of real-time interactions between the network and the clients. Of special interest is that our platform streams actual video clips to real video clients in real-time over a simulated LTE network, allowing reproducible experiments and easy modification of LTE and client parameters. The demonstration showcases how changes in LTE network settings (fading model, scheduler, client distance from eNB, etc.), as well as video-related decisions at the clients (streaming algorithm, quality selection, clip selection, etc.), can impact on the delivery and achievable quality.

MobilityFirst (MF), as a realization of Information Centric Network architecture, places intelligent functionality, such as storage and reliability, inside the network to assist with data delivery. The MF architecture requires effective congestion and flow control to efficiently support data delivery at scale. Traditional end-to-end, window-based congestion control like that used by TCP is unsuitable as it is unable to take advantage of such in-network functionality. We design network-layer assisted congestion control schemes tailored to MF. One approach that works well for hop-by-hop reliable networks is using per-flow queueing and backpressure to alleviate congestion. However, it could become impractical in the presence of a large number of flows, which leads to substantial memory consumption and computational complexity. Building on a more scalable per-interface queueing model, we design congestion control mechanism that embodies traffic source rate control and explicit congestion notification from routers. Sample results show that the proposed scheme is able to achieve similar link utilization and better fairness compared with a per-flow queueing scheme.

In the aftermath of natural disasters (e.g., earthquakes and hurricanes), information dissemination to and from citizens and authorities often involves the use of mobile network services. They may do so to seek help, distribute critical information, and possibly to confirm the safety of relatives and friends. The traffic on the cellular network typically increases substantially in the aftermath of such disasters. The situation is further compounded by disruptions caused by damage to the network infrastructure and the non-availability of power. Networks are fragmented, and communication is intermittent and disruption prone. In this paper, we propose a name-based many-to-many communication service for such fragmented networks. The key feature of this service is an integration of several communication methods, such as push-based and pull-based. The service effectively supports a variety of network situations by leveraging the features of information-centric networks.

Dynamic adaptive video over HTTP (DASH) is fast becoming the protocol of choice for content providers for their online video streaming delivery. Concurrently, dependence on cellular Long Term Evolution (LTE) networks is growing to serve user demands for bandwidth-hungry applications, especially video. Each LTE base station's (eNodeB) scheduler assigns wireless resources to individual clients. Several alternative schedulers have been proposed, especially to meet the user's desired quality of experience (QoE) with video. In this paper, we investigate the impact of the scheduler on DASH performance, motivated by the fact that video performance and the underlying traffic models are different from other HTTP/TCP applications. We use our laboratory testbed employing real video content and streaming clients, over a simulated ns-3 LTE network. We quantify the impact of the scheduler and show that it has a significant impact on key video streaming performance metrics such as stalls and QoE, for different client adaptation algorithms. Additionally, we show the impact of user mobility within a cell, which has the side-effect of improving performance by mitigating long-term fading effects. Our detailed assessment of four LTE schedulers in ns-3 shows that the proportional fair scheduler achieves the best overall user experience, although somewhat disadvantaging static cell-edge users.

Network Function Virtualization promises to enable dynamic management of software-based network functions. We envision a dynamic and flexible network that can support a smarter data plane than just simple switches that forward packets. This network architecture supports complex stateful routing of flows where processing by network functions (NFs) can transform packet data, customized on a per-flow basis, as it moves between end points. This demo will present OpenNetVM, a highly efficient packet processing framework that greatly simplifies the development of network functions, as well as their management and optimization. OpenNetVM runs network functions in lightweight Docker containers that start in less than a second. The OpenNetVM platform manager provides load balancing, flexible flow management, and service name abstractions. OpenNetVM uses DPDK for high performance I/O, and efficiently routes packets through dynamically created service chains. We will demonstrate how the research community can easily build new network functions and rapidly deploy them to see their effectiveness in high performance network environments.

In recent years, there has been a growing interest in the ability of embedded devices, sensors, and actuators to communicate, and create a ubiquitous cyber-physical world. The growth of the notion of the Internet of Things (IoT) and the rapid development of technologies such as short range mobile communication and improved energy-efficiency is expected to create a pervasive connection of “things.” This will inevitably result in the generation of enormous amount of data, which have to be stored, processed, and accessed. Cloud computing has long been recognized as a paradigm for big data storage and analytics. The combination of cloud computing and IoT can enable ubiquitous sensing services and powerful processing of sensing data streams beyond the capability of individual things, thus stimulating innovations in both fields. For example, cloud platforms allow the sensing data to be stored and used intelligently for smart monitoring and actuation with the smart devices. Novel data fusion algorithms, machine learning methods, and artificial intelligence techniques can be implemented and run centralized or distributed on the cloud to achieve automated decision making. These will boost the development of new applications, such as smart cities, grids, and transportation systems. New challenges, however, arise when IoT meets cloud—there is an urgent need for novel network architectures that seamlessly integrate them, and protocols that facilitate big data streaming from IoT to the cloud. QoS and QoE, as well as data security, privacy, and reliability, are critical concerns during the integration.

IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100 Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1%-2%) with orders of magnitude speedup relative to solving even the linear programming (LP) relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates. Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by least recently used (LRU) or least frequently used (LFU) cache replacement policies. We also investigate the tradeoff between disk space and network bandwidth.

Wireless spectrum is a precious resource and must be allocated and used efficiently. Conventional spectrum allocations let a government agency (e.g., FCC) sell a portion of spectrum to one provider. This is not only restrictive, but also limits spectrum reuse and may lead to significant under-utilization of spectrum. In this paper, we develop a novel truthful double-auction scheme to let any resource owner (e.g., a cellular provider), who has spare spectrum at a given time period, sell to one or more providers that need additional spectrum at that time. Spectrum auctions are fundamentally different from conventional auction problems since spectrum can be reused and competition among buyers is complex due to wireless interference. Our proposal is the first double-auction design for spectrum allocation that explicitly decouples the buyer-side and seller-side auction design while achieving: 1) truthfulness; 2) individual rationality; and 3) budget-balance. To accurately capture wireless interference and support spectrum reuse, we partition the conflict graph so that buyers with strong direct and indirect interference are put into the same subgraph, and buyers with no interference or weak interference are put into separate subgraphs. Then, we compute pricing independently within each subgraph. We then develop a scheme to combine spectrum allocation results from different subgraphs and resolve potential conflicts. We further extend our approach to support local sellers whose spectrum can only be sold to buyers within certain regions, instead of all buyers. Using conflict graphs generated from real cell tower locations, we extensively evaluate our approach and demonstrate that it achieves high efficiency, revenue, and utilization.

Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks (CCDNs), which exploits an innovative combination of content-based forwarding and location [Internet Protocol (IP)]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location (IP)-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server's storage as cache (e.g., 3%) and with a modest content forwarding information base size (e.g., 1000 entries) in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System (HDFS) system in a real data center.

Network topology plays a critical role while designing and evaluating network protocols. Most existing topology generators are insufficient to reflect the real world network demands to a topology or to capture the Internet topology evolution such as the "flattening" Internet. They focused on the graph properties of a topology, thus, lacking of ability to model engineering features of the network. Some state-of-art topology generators that consider engineering factors fail to capture trends in both intra-AS and inter-AS connections, which are equally important for evaluating future network protocols. We have developed a topology generator GeoTopo, which is to our best knowledge the first scalable topology generator modeling engineering factors for both intra-AS and inter-AS topology generation. The engineering factors that GeoTopo considers include demographic and geographic features as well as business interests of ASes. We use GeoTopo to create and study three classes of topologies: the topology characterized mainly by graph-properties (Status Quo topology), the topology driven by peering at Internet Exchange Points (IXP topology) and the topology characterized by country backbones (CB topology). The SQ topology follows the degree-based model and serves as a baseline for capturing topology features. The IXP and CB topologies model two major directions of the Internet "flattening". The three classes of topologies enable us to analyze the impact of engineering factors on topology generation such as AS peering policies, IXP deployment and AS geo-settings. GeoTopo's ability to generate projected future Internet topologies make it a valuable tool for the design and evaluation of Future Internet Architectures that is currently under consideration in the research community. We use the evaluation of Global Name Resolution Service (GNRS), a key component shared by name-based network architectures, as an example application to demonstrate GeoTopo's capability to capture the mobility of network entities, the locality of the traffic, and the impact of the evolving network.

Today's interdomain routing is traffic agnostic when determining the single, best forwarding path. Naturally, as it does not adapt to congestion, the path chosen is not always optimal. In this paper, we focus on designing a multi-path interdomain forwarding (MIFO) mechanism, where AS border routers adaptively forward outbound traffic from a congested default path to an alternative path, without touching the interdomain routing protocols. Different from previous efforts which enable multi-path on control plane, MIFO achieves multi-path on data plane. The multiple alternative forwarding paths are obtained by exploring local BGP RIB. Multi-path forwarding on data plane can create a loop even within a stable network. MIFO solves this problem with a simple and practical approach. Several other challenges are also addressed including preventing cycling packet between iBGP peers and choosing the best alternative path from among multiple candidates. Our evaluations show that MIFO significantly improves the end-to-end throughput at the AS level, compared to traditional BGP and MIRO. For example, with only 50% of the ASes being MIFO capable, a significant percentage of the flows (about 40%) can use at least 50% of the inter-AS link capacity. In contrast, BGP and MIRO routing make less effective use of the inter-AS links, with only 7% and 17% of the flows can be so. Finally, we have developed a prototype implementation of MIFO on Linux with the forwarding engine in the kernel, with the routing daemon developed on XORP platform. The experiments on a test bed built with prototypes show that MIFO can improves the aggregate throughput by 81% compared with BGP routing.

With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Cellular operators still seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions among a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership, all of which we consider in this work. We determine the optimal allocation of wireless resources for users to maximize proportional fair utility. To handle the heterogeneity of user channel conditions, we efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength. Numerical simulations are performed to compare our scheme to practical heuristics and state-of-the-art schemes. We demonstrate the tradeoff between improving unicast user rates and improving spectrum efficiency through multicast. Finally, we analyze the interaction between the globally fair solution and individual user's desire to maximize its rate. We show that even if the user deviates from the global solution in a number of scenarios, we can bound the number of selfish users that will choose to deviate.

Information Centric Networks (ICN) enable accessing data oblivious of its location, by allowing end-systems to retrieve content based on names. But, architectures such as Named Data Networking (NDN) and Content Oriented Publish/Subscribe System (COPSS) do not yet provide a mechanism for end-system applications to obtain these names. There is a need for an object resolution system that addresses a most important and as yet unimplemented component of obtaining a name in ICN. In this paper, we propose ORICE, an architectural design for Object Resolution services in Information-Centric Environment that satisfies this need. The architecture enables intelligent resolution service by placing the service in the application layer and allows for the service diversity by separating the name space management from resolution service. Through preliminary evaluation, we show that with the help of ORICE, the states stored in the network can be dramatically reduced while ensuring complete data delivery.

The integration of network function virtualization (NFV) and software defined networks (SDN) seeks to create a more flexible and dynamic software-based network environment. The line between entities involved in forwarding and those involved in more complex middle box functionality in the network is blurred by the use of high-performance virtualized platforms capable of performing these functions. A key problem is how and where network functions should be placed in the network and how traffic is routed through them. An efficient placement and appropriate routing increases system capacity while also minimizing the delay seen by flows. In this paper, we formulate the problem of network function placement and routing as a mixed integer linear programming problem. This formulation not only determines the placement of services and routing of the flows, but also seeks to minimize the resource utilization. We develop heuristics to solve the problem incrementally, allowing us to support a large number of flows and to solve the problem for incoming flows without impacting existing flows.

HTTP streaming currently dominates Internet traffic. It is increasingly common that video players employ adaptive bitrate (ABR) streaming strategies to maximise the user experience by selecting the highest video representation while targeting stall-free playback. Our interest lies in the common situation where a set of video flows are competing for access to a shared bottleneck link, such as in a cellular radio access network. We observe that ISPs (e.g. cellular operators) are considering innetwork techniques for resource allocation and sharing among different users. Buoyed by the ability of software defined networks (SDN) to offer flow-specific control and traffic shaping, we focus on traffic shaping techniques, and experimentally analyse the effect on ABR video flows when sharing a bottleneck link. We conduct experiments using the GPAC video player operating over a Mininet virtual network. We conclude that traffic shaping can allow a balance of fairness, efficiency and quality. Traffic shaping ABR videos reduce the number of stalls and quality switches, while also reducing the peaks for the aggregate network traffic.

The prevalence of mobile devices especially smartphones has attracted research on mobile content delivery techniques. In this paper, we propose to take advantage of the storage available at wireless access points to bring content closer to mobile devices, hence improving the downloading performance. Specifically, we propose to have a separate popularity based cache and a prefetch buffer at the network edge to capture both long-term and short-term content access patterns. Further, we point out that it is insufficient to rely on a device's past history to predict when and where to prefetch, especially in urban settings; instead, we propose to derive a prediction model based on the aggregated network-level statistics. We discuss the proposed mobile content caching/prefetching method in the context of the MobilityFirst future Internet architecture. In MobilityFirst, when mobile clients move between network attachment points (e.g., Wi-Fi access points), their network association records are logged by the network, which then naturally facilitates the network-level mobility prediction. Through detailed simulations with real taxi mobility traces, we show that such a strategy is more effective than earlier schemes in satisfying content requests at the edge (higher cache hit ratios), leading to shorter content download latencies. Specifically, the fraction of requests satisfied at the edge increases by a factor of 2.9 compared to a caching only approach, and by 45% compared to individual user-based prediction and prefetching.

NetVM brings virtualization to the Network by enabling high bandwidth network functions to operate at near line speed, while taking advantage of the flexibility and customization of low cost commodity servers. NetVM allows customizable data plane processing capabilities such as firewalls, proxies, and routers to be embedded within virtual machines, complementing the control plane capabilities of Software Defined Networking. NetVM makes it easy to dynamically scale, deploy, and reprogram network functions. This provides far greater flexibility than existing purpose-built, sometimes proprietary hardware, while still allowing complex policies and full packet inspection to determine subsequent processing. It does so with dramatically higher throughput than existing software router platforms. NetVM is built on top of the KVM platform and Intel DPDK library. We detail many of the challenges we have solved such as adding support for high-speed inter-VM communication through shared huge pages and enhancing the CPU scheduler to prevent overheads caused by inter-core communication and context switching. NetVM allows true zero-copy delivery of data to VMs both for packet processing and messaging among VMs within a trust boundary. Our evaluation shows how NetVM can compose complex network functionality from multiple pipelined VMs and still obtain throughputs up to 10 Gbps, an improvement of more than 250% compared to existing techniques that use SR-IOV for virtualized networking.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). In this paper, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over 4 months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on Web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify negative interactions between the protocols used for Web access (HTTP/SPDY over TCP) and cellular radio resource management as the underlying cause. Overall performance suffers when devices go through a cellular radio state promotion after an idle period, and the consequent increase in latency. This impacts SPDY more because of the use of a single TCP connection. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved Web performance over cellular networks.

Virtualization technology and the ease with which virtual machines (VMs) can be migrated within the LAN have changed the scope of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration of virtual machines to likewise transform the scope of provisioning resources from a single data center to multiple data centers spread across the country or around the world. In this paper, we present the CloudNet architecture consisting of cloud computing platforms linked with a virtual private network (VPN)-based network infrastructure to provide seamless and secure connectivity between enterprise and cloud data center sites. To realize our vision of efficiently pooling geographically distributed data center resources, CloudNet provides optimized support for live WAN migration of virtual machines. Specifically, we present a set of optimizations that minimize the cost of transferring storage and virtual machine memory during migrations over low bandwidth and high-latency Internet links. We evaluate our system on an operational cloud platform distributed across the continental US. During simultaneous migrations of four VMs between data centers in Texas and Illinois, CloudNet's optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 19 GB, a 50% reduction.

Cellular providers are rapidly deploying multiple technologies like cell biasing, carrier aggregation, co-ordinated interference control/scheduling to improve capacity and coverage. In this paper, we explore a complementary transport layer approach based on multipath TCP that can concurrently use multiple interfaces to boost throughput of users with poor coverage and improve fairness. Multipath TCP has been recently standardized by IETF and requires no modifications to applications. It has been shown to improve fairness and throughput in wire line environments and individual user throughputs in wireless networks. However, in a wireless multi-user environment, it is not clear that it is always beneficial, as we show in this paper. Therefore, we examine if it is indeed beneficial for a service provider to judiciously decide whether to enable multiple cellular interfaces on a smart phone based on a global centralized view of its network. Alternatively, should a device decide independently based only on a local view? To quantify the network wide impact in a system where users have multiple cellular interfaces, we have developed centralized and distributed heuristic algorithms to evaluate this, particularly in the context of fairness across all the users. Our simulations and numerical models show that there are potential gains in fairness (15-30%) to be realized by judiciously enabling multipath connections at the cell edge. These gains diminish as the number of users in a cell increases or users behave greedily. We also quantify the delicate balance between throughput and fairness. Our analysis provides an intuition on which user(s) in a cellular network stand to benefit the most by enabling multiple interfaces. We also discuss LTE protocol mechanisms to enforce associations of specific interfaces to specific cells.

Wireless spectrum is a precious resource and must be allocated and used efficiently. The conventional spectrum allocation lets a government (e.g., FCC) sell a given portion of spectrum to one provider. This is not only restrictive, but also limits spectrum reuse and may lead to significant under-utilization of spectrum. In this paper, we develop a novel truthful double auction scheme to let any resource owner (e.g., a cellular provider), who has spare spectrum at a given time, sell to one or more providers that need additional spectrum at that time. Spectrum auction is fundamentally different from conventional auction problems since spectrum can be re-used and competition pattern is complex due to wireless interference. We propose the first double auction design for spectrum allocation that explicitly decouples the buyer side and seller side auction design while achieving (i) truthfulness, (ii) individual rationality, and (iii) budget balance. To accurately capture wireless interference and support spectrum reuse, we partition the conflict graph so that buyers with strong direct and indirect interference are put into the same subgraph and buyers with no or weak interference are put into separate subgraphs and then compute pricing independently within each subgraph. We develop a merge scheme to combine spectrum allocation results from different subgraphs and resolve potential conflicts. Using conflict graphs generated from real cell tower locations, we extensively evaluate our approach and demonstrate that it achieves high efficiency, revenue, and utilization.

Accurately determining the geographic location of an Internet host is important for location-aware applications such as location-based advertising and network diagnostics. Despite their fast response time, widely used database-driven geolocation approaches provide only inaccurate locations. Delay measurement based approaches improve the estimation accuracy but still suffer from a limited precision (about 10 km) and a long response time (tens of seconds) to localize a single PC, which cannot meet the demand of precise and real-time geolocation for location-aware applications. In this paper, we propose a new geolocation approach, Checkin-Geo, which exploits geolocation resources fundamentally different from existing database-driven (using DNS, Whois, etc.) or network delay measurement based approaches. In particular, we leverage the location data that users are willing to share in location-sharing services and logs of user logins from PCs for real-time and accurate geolocation. Experimental results show that compared to existing geolocation techniques, Checkin-Geo achieves 1) a median estimation error of 799 meters (an order of magnitude smaller than existing approaches), and 2) a negligible response time, which are promising for accurate location-aware applications.

Disasters result in disruptions to the communication infrastructure, typically forming a multitude of fragmented networks. This makes it difficult to exchange even simple while critical messages with large portions of the affected population, including first-responders and government authorities. In this paper, we focus on a content driven data retrieval model and propose an enhanced information-centric network (ICN) approach to provide communication resilience to such disruption-prone, delay-tolerant networks. The message exchange between communities and mules takes into account the limited available resources and the fact that the interest and the data might traverse completely different paths, unlike the assumption in many of the existing ICN solutions. Moreover, we argue for the separation of the the logical faces from the actual physical interfaces taking into account the fact that the data mules behave as mobile routers and are connected to different nodes at different time periods, unlike the assumptions made in existing ICN solutions. Our preliminary evaluations show that our solution is able to outperform other approaches such as “Epidemic” and “SprayAndWait” with respect to the latency, response probability and overall performance while improving the performance (latency and response probability) for high priority messages.

With the increasing interest in connected vehicles, it is useful to evaluate the capability of delivering large content over a WiFi infrastructure to vehicles. The throughput achieved over WiFi channels can be highly variable and also rapidly degrades as the distance from the access point increases. While this behavior is well understood at the data link layer, the interactions across the various protocol layers (data link and up through the transport layer) and the effect of mobility may reduce the amount of content transferred to the vehicle, as it travels along the roadway. This paper examines the throughput achieved at the TCP layer over a carefully designed outdoor WiFi environment and the interactions across the layers that impact the performance achieved, as a function of the receiver mobility. The experimental studies conducted reveal that impairments over the WiFi link (frame loss, ARQ and increased delay) and the residual loss seen by TCP causes a cascade of duplicate ACKs to be generated. This triggers large congestion window reductions at the sender, leading to a drastic degradation of throughput to the vehicular client. To ensure outdoor WiFi infrastructures have the potential to sustain reasonable downlink throughput for drive-by vehicles, we speculate that there is a need to adapt how WiFi and TCP (as well as mobility protocols) function for such vehicular applications.

Cloud datacenters typically require tenants to specify the resource demands for the virtual machines (VMs) they create using a set of pre-defined, fixed configurations, to ease the resource allocation problem. Unfortunately, this leads to low resource utilization of cloud datacenters as tenants are obligated to conservatively predict the maximum resource demand of their applications. We argue that instead of such a static VM resource allocation, a finer-grained dynamic resource allocation and scheduling can substantially improve the utilization of the datacenter resources by increasing the number of jobs accommodated and correspondingly, the cloud datacenter provider's revenue. The dynamic real-time scheduling of jobs can also ensure that the performance goals for the tenant VMs are achieved. Examining a typical publicly available cluster data center trace, we observe that a large number of jobs are short. Only a small proportion of jobs are long and which require substantial compute or memory resources. We propose an optimization based approach that exploits this division between the short and long jobs to dynamically allocate a cloud datacenter's resources to achieve significantly better utilization by increasing the number of jobs accommodated by the datacenter. We use a constraint programming solution to schedule the long jobs, and use simple heuristics to quickly, yet quite accurately schedule the short jobs. Using trace-driven simulations based on public traces collected on provider cluster we show that the overall revenue for the cloud provider can be improved by 30% over the traditional static VM resource allocation based on the coarse granularity specifications. We are able to increase the number of jobs accommodated using dynamic scheduling by 18%. We also compare the performance of our approach to multi-resource (CPU and memory) first-fit and best-fit algorithms and to the optimal offline solution, and demonstrate that our solution achieves within 76% of the offline optimal solution.

Distributed clouds offer a choice of data center locations to application providers to host their applications. In this paper we consider distributed clouds that host virtual desktops(VDs) which are then accessed by their users through remote desktop protocols. VDs have different sensitivities to latency, primarily determined by the types of applications running (games or video players are more sensitive to latency) and the end users' locations. We design VMShadow, a system to automatically optimize the location and performance of latency-sensitive VDs in the cloud. VMShadow performs black-box fingerprinting of a VM's network traffic to infer its latency-sensitivity and employs a greedy heuristic based algorithm to move highly latency-sensitive VMs to cloud sites that are closer to their end users. VMShadow employs WAN-based live migration and a new network connection migration protocol to ensure that the VM migration and subsequent changes to the VM's network address are transparent to end-users. We implement a prototype of VMShadow in a nested hypervisor and demonstrate its effectiveness for optimizing the performance of VM-based desktops in the cloud. Our experiments on a private and the public EC2 cloud show that VMShadow is able to discriminate between latency-sensitive and insensitive desktop applications and judiciously move only those VMs that will benefit the most. For desktop VMs with video activity, VMShadow improves VNC's refresh rate by 90%. Further our connection migration proxy, which utilizes dynamic rewriting of packet headers, imposes a rewriting overhead of only 13μs per packet. Trans-continental VM migrations take about 4 minutes.

Information Centric Networking (ICN), a novel network paradigm, places the focus on the content instead of the end-hosts. ICN addresses content by names instead of locations and can ease content retrieval and improve network efficiency. Ongoing work attempts to extend the use of ICN to scenarios such as real time communication, group communication, push services, and addresses the issue of migration from the current network and coexistence of different network paradigms. In this work, we argue that by extending the current design of ICN from a “host-to-name” to a “name-to-name” architecture, the utility and efficiency of ICN could be further increased. We propose Internames, an architectural framework in which names are used to identify all entities involved in communication: content, users, devices, logical points, and services. Internames is envisioned to be an overarching name-to-name communication primitive that is fully compatible with ICN principles, accommodates the coexistence (or gradual migration) of different network realms (e.g., IP, ICN, VANET) and is suitable for application scenarios where ICN is somehow limited by its reliance on a “host-to-name” approach. In this paper, we provide early insights into the Internames architecture by leveraging on the work done by the research community and identify components and challenges that require more detailed investigation.

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive for a cellular service provider to provision enough cellular resources to support the peak traffic demands. In this paper, we propose iDEAL, a novel auction-based incentive framework that allows a cellular service provider to leverage resources from third-party resource owners on demand by buying capacity whenever needed through reverse auctions. iDEAL has several distinctive features: 1) iDEAL explicitly accounts for the diverse spatial coverage of different resources and can effectively foster competition among third-party resource owners in different regions, resulting in significant savings to the cellular service provider. 2) iDEAL provides revenue incentives for third-party resource owners to participate in the reverse auction and be truthful in the bidding process. 3) iDEAL is provably efficient. 4) iDEAL effectively guards against collusion. 5) iDEAL effectively copes with the dynamic nature of traffic demands. In addition, iDEAL has useful extensions that address important practical issues. Extensive evaluation based on real traces from a large US cellular service provider clearly demonstrates the effectiveness of our approach. We further demonstrate the feasibility of iDEAL using a prototype implementation.

To address reliability challenges due to failures and planned outages, Internet Service Providers (ISPs) typically use two backbone routers (BRs) at each central office. Access routers (ARs) are connected to these BRs in a dual-homed configuration. To provide reliability through node and path diversity, redundant backbone routers and redundant transport equipment to interconnect them are deployed. However, deploying such redundant resources increases the overall cost of the network. Hence, to avoid such redundant resources, a fundamental redesign of the backbone network leveraging the capabilities of an agile optical transport network is highly desired. In this paper, we propose a fundamental redesign of IP backbones. Our alternative design uses only a single router at each office. To survive failures or outages of a single local BR, we leverage the agile optical transport layer to carry traffic to remote BRs. Optimal mapping of local ARs to remote BRs is determined by solving an Integer Linear Program (ILP). We describe how our proposed design can be realized using current optical transport technology. We evaluate network designs for cost and performability, the latter being a metric combining performance and availability. We show significant reduction in cost for approximately the same level of reliability as current designs.

We present VMShadow, a system that automatically optimizes the location and performance of applications based on their dynamic workloads. We prototype VMShadow and demonstrate its efficacy using VM-based desktops in the cloud as an example application. Our experiments on a private cloud as well as the EC2 cloud, using a nested hypervisor, show that VMShadow is able to discriminate between location-sensitive and location-insensitive desktop VMs and judiciously moves only those that will benefit the most from the migration. For example, VMShadow performs transcontinental VM migrations in ~ 4 mins and can improve VNC's video refresh rate by up to 90%.

Despite its widespread adoption and popularity, the Hypertext Transfer Protocol (HTTP) suffers from fundamental performance limitations. SPDY, a recently proposed alternative to HTTP, tries to address many of the limitations of HTTP (e.g., multiple connections, setup latency). With cellular networks fast becoming the communication channel of choice, we perform a detailed measurement study to understand the benefits of using SPDY over cellular networks. Through careful measurements conducted over four months, we provide a detailed analysis of the performance of HTTP and SPDY, how they interact with the various layers, and their implications on web design. Our results show that unlike in wired and 802.11 networks, SPDY does not clearly outperform HTTP over cellular networks. We identify, as the underlying cause, a lack of harmony between how TCP and cellular networks interact. In particular, the performance of most TCP implementations is impacted by their implicit assumption that the network round-trip latency does not change after an idle period, which is typically not the case in cellular networks. This causes spurious retransmissions and degraded throughput for both HTTP and SPDY. We conclude that a viable solution has to account for these unique cross-layer dependencies to achieve improved performance over cellular networks.

This work demonstrates the benefit brought by COPSS for an efficient notification service, including the convenience of hierarchical group management, network efficiency and timeliness in delivering the notification. The work will also demonstrate a simple first-step authorization. Future work will include a more complete authorization, authentication, encryption and mobility support for a scalable notification service.

We propose Joint-Family, a protocol that combines peer-to-peer (P2P) and adaptive bitrate (ABR) streaming for video-on-demand (VoD). While P2P for VoD and ABR have been proposed previously, they have not been studied together because they attempt to tackle problems with seemingly orthogonal goals. We motivate our approach through analysis that overcomes a misconception resulting from prior analytical work, and show that the popularity of a P2P swarm and seed staying time has a significant bearing on the achievable per-receiver download rate. Specifically, our analysis shows that popularity affects swarm efficiency when seeds stay “long enough”. We also show that ABR in a P2P setting helps viewers achieve higher playback rates and/or fewer interruptions. We develop the Joint-Family protocol based on the observations from our analysis. Peers in Joint-Family simultaneously participate in multiple swarms to exchange chunks of different bitrates. We adopt chunk, bitrate, and peer selection policies that minimize occurrence of interruptions while delivering high quality video and improving the efficiency of the system. Using traces from a large-scale commercial VoD service, we compare Joint-Family with existing approaches for P2P VoD and show that viewers in Joint-Family enjoy higher playback rates with minimal interruption, irrespective of video popularity.

Large events like the Super Bowl, where almost 75K attendees congegrate for several hours, poses a significant challenge in the planning, design and deployment of wireless networks. This was one of the first events where the LTE cellular network was available widely, in addition to almost 700 WiFi free hotspots. The Super Bowl in 2013 was also unprecedented because of a stadium-wide power outage for over half an hour. This study is the first to look in-depth at the user behaviours and traffic demand of a large ISP's celluar network at such an unique event.

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive for a cellular service provider to provision enough cellular resources to support the peak traffic demands. In this paper, we propose iDEAL, a novel auction-based incentive framework that allows a cellular service provider to leverage resources from third-party resource owners on demand by buying capacity whenever needed through reverse auctions. iDEAL has several distinctive features: (i) iDEAL explicitly accounts for the diverse spatial coverage of different resources and can effectively foster competition among third-party resource owners in different regions, resulting in significant savings to the cellular service provider. (ii) iDEAL provides revenue incentives for third-party resource owners to participate in the reverse auction and be truthful in the bidding process. (iii) iDEAL is provably efficient. (iv) iDEAL effectively guards against collusion. (v) iDEAL effectively copes with the dynamic nature of traffic demands. In addition, iDEAL has useful extensions that address important practical issues. Extensive evaluation based on real traces from a large US cellular service provider clearly demonstrates the effectiveness of our approach. We further demonstrate the feasibility of iDEAL using a prototype implementation.

Peer-to-Peer (P2P) systems have evolved from being used for file sharing to delivering streaming video on demand (VoD). The policies adopted in P2P VoD, however, have not taken user viewing behavior - that users abandon videos - into account. We show that abandonment can result in increased interruptions and wasted resources. As a result, we reconsider the set of policies to use in the presence of abandonment. Our goal is to balance the conflicting needs of delivering videos without interruptions while minimizing wastage. We find that an Earliest-First chunk selection policy in conjunction with the Earliest-Deadline peer selection policy allows us to achieve high download rates. We take advantage of abandonment by converting peers to “partial seeds”; this increases capacity. We minimize wastage by using a playback lookahead window. We use analysis and simulation experiments using real-world traces to show the effectiveness of our approach.

Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings. However, achieving similar savings with real-time services can be a challenge. In this paper, we seek to lower a provider's costs for real-time IPTV services through a virtualized IPTV architecture and through intelligent time-shifting of selected services. Using Live TV and Video-on-Demand (VoD) as examples, we show that we can take advantage of the different deadlines associated with each service to effectively multiplex these services. We provide a generalized framework for computing the amount of resources needed to support multiple services, without missing the deadline for any service. We construct the problem as an optimization formulation that uses a generic cost function. We consider multiple forms for the cost function (e.g., maximum, convex and concave functions) reflecting the cost of providing the service. The solution to this formulation gives the number of servers needed at different time instants to support these services. We implement a simple mechanism for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network. Our results show that we are able to reduce the load by ~24%(compared to a possible ~31.3% as predicted by the optimization framework).

Content-Centric Networking (CCN) seeks to meet the content-centric needs of users. In this paper, we propose hybrid-COPSS, a hybrid content-centric architecture. We build on the previously proposed Content-Oriented Publish/Subscribe System (COPSS) to address incremental deployment of CCN and elegantly combine the functionality of content-centric networks with the efficiency of IP-based forwarding including IP multicast. Furthermore, we propose an approach for incremental deployment of caches in generic query/response CCN environments that optimizes latency and network load. To overcome the lack of inter-domain IP multicast, hybrid-COPSS uses COPSS multicast with shortcuts in the CCN overlay. Our hybrid approach would also be applicable to the Named Data Networking framework.

Information-Centric Networking provides substantial flexibility for users to obtain information without knowing the source of information or its current location. With users increasingly focused on an online world, an emerging challenge for the network infrastructure is to support Massively Multiplayer Online Role Playing Game (MMORPG). Currently, MMORPG is built on IP infrastructure with the primary responsibility resting on servers for disseminating control messages and predicting/retrieving objects belonging to each player's view. Scale and timeliness are major challenges of such a server-oriented gaming architecture. Limited server resources significantly impair the user's interactive experience, requiring game implementations to limit the number of players in a single game instance. We propose Gaming over COPSS (G-COPSS), a distributed communication infrastructure using a Content-Oriented Pub/Sub System (COPSS) to enable efficient decentralized information dissemination in MMORPG, jointly exploiting the network and end-systems for player management and information dissemination. G-COPSS aims to scale well in the number of players in a single game, while still meeting users' response time requirements. We have implemented G-COPSS on top of the open-source CCNx implementation. We use a simple game with a hierarchical map to carefully micro benchmark the implementation and the processing involved in managing game dynamics. We have also micro benchmarked the game based on NDN and a server with an IP infrastructure. We emulate an application that is particularly emblematic of MMORPG -- Counter-Strike -- but one in which all players share a hierarchical structured map. Using trace-driven simulation, we demonstrate that G-COPSS can achieve high scalability and tight timeliness requirements of MMORPG. The simulator is parameterized based on micro benchmarks of our implementation. Our evaluations show that G-COPSS provides orders of magnitude improvement in update latency and a factor of two reduction in aggregate network load compared to a server-based implementation.

Modern day enterprises have a large IT infrastructure comprising thousands of applications running on servers housed in tens of data centers geographically spread out. These enterprises periodically perform a transformation of their entire IT infrastructure to simplify, decrease operational costs and enable easier management. However, the large number of different kinds of applications and data centers involved and the variety of constraints make the task of data center transformation challenging. The state-of-the-art technique for performing this transformation is simplistic, often unable to account for all but the simplest of constraints. We present eTransform, a system for generating a transformation and consolidation plan for the IT infrastructure of large scale enterprises. We devise a linear programming based approach that simultaneously optimizes all the costs involved in enterprise data centers taking into account the constraints of applications groups. Our algorithm handles the various idiosyncrasies of enterprise data centers like volume discounts in pricing, wide-area network costs, traffic matrices, latency constraints, distribution of users accessing the data etc. We include a disaster recovery (DR) plan, so that eTransform, thus provides an integrated disaster recovery and consolidation plan to transform the enterprise IT infrastructure. We use eTransform to perform case studies based on real data from three different large scale enterprises. In our experiments, eTransform is able to suggest a plan to reduce the operational costs by more than 50% from the "as-is" state of these enterprise to the consolidated enterprise IT environment. Even including the DR capability, eTransform is still able to reduce the operational costs by more than 25% from the simple "as-is" state. In our experiments, eTransform is able to simultaneously optimize multiple parameters and constraints and discover solutions that are 7x cheaper than other solutions.

Cloud computing platforms such as Amazon EC2 provide customers with flexible, on demand resources at low cost. However, while existing offerings are useful for providing basic computation and storage resources, they have not provided the transparency, security and network controls that many enterpise customers would like. While cloud computing has a great potential to change how enterprises run and manage their IT systems, a more comprehensive control over network resources and security needs to be provided for such users. Towards this goal, we propose a Virtual Cloud Pool abstraction to logically unify cloud and enterprise data center resources, and present the vision behind CloudNet, a cloud platform architecture which utilizes virtual private networks to securely and seamlessly link cloud and enterprise sites. It also enables the pooling of resources across data centers to provide enterprises the capability to have cloud resources that are dynamic and adaptive to their needs. We describe several usage scenarios for virtual cloud pools and discuss the benefits of using this abstraction in enterprise settings.

Wireless networks (including wireless mesh networks) provide opportunities for using multiple paths. Multihoming of hosts, possibly using different technologies and providers, also makes it attractive for end-to-end transport connections to exploit multiple paths. In this paper, we propose a multipath transport protocol, based on a carefully crafted set of enhancements to TCP, that effectively utilizes the available bandwidth and diversity provided by heterogeneous, lossy wireless paths. Our Multi-Path LOss-Tolerant (MPLOT) transport protocol can be used to obtain significant goodput gains in wireless networks, subject to bursty, correlated losses with average loss rates as high as 50%. MPLOT is built around the principle of separability of reliability and congestion control functions in an end-to-end transport protocol. Congestion control is performed separately on individual paths, and the reliability mechanism works over the aggregate set of paths available for an end-to-end session. MPLOT distinguishes between congestion and link losses through Explicit Congestion Notification (ECN), and uses Forward Error Correction (FEC) coding to recover from data losses. MPLOT uses a dynamic packet mapping based on the current path characteristics to choose a path for a packet. Use of erasure codes and block-level recovery ensures that in MPLOT the receiving transport entity can recover all data as long as a necessary number of packets in the block are received, irrespective of which packets are lost. We present a theoretical analysis of the different design choices of MPLOT and show that MPLOT chooses its policies and parameters such that a desirable tradeoff between goodput with data recovery delay is attained. We evaluate MPLOT, through simulations, under a variety of test scenarios and demonstrate that it effectively exploits path diversity in addition to efficiently aggregating path bandwidths while remaining fair to a conventional TCP flow on each path.

Content-Centric Networks (CCN) provide substantial flexibility for users to obtain information without regard to the source of the information or its current location. Publish/subscribe (pub/sub) systems have gained popularity in society to provide the convenience of removing the temporal dependency of the user having to indicate an interest each time he or she wants to receive a particular piece of related information. Currently, on the Internet, such pub/sub systems have been built on top of an IP-based network with the additional responsibility placed on the end-systems and servers to do the work of getting a piece of information to interested recipients. We propose Content-Oriented Pub/Sub System (COPSS) to achieve an efficient pub/sub capability for CCN. COPSS enhances the heretofore inherently pull-based CCN architectures proposed by integrating a push based multicast capability at the content-centric layer. We emulate an application that is particularly emblematic of a pub/sub environment - Twitter - but one where subscribers are interested in content (e.g., identified by keywords), rather than tweets from a particular individual. Using trace-driven simulation, we demonstrate that our architecture can achieve a scalable and efficient content centric pub/sub network. The simulator is parameterized using the results of careful micro benchmarking of the open source CCN implementation and of standard IP based forwarding. Our evaluations show that COPSS provides considerable performance improvements in terms of aggregate network load, publisher load and subscriber experience compared to that of a traditional IP infrastructure.

Disaster Recovery (DR) is a desirable feature for all enterprises, and a crucial one for many. However, adoption of DR remains limited due to the stark tradeoffs it imposes. To recover an application to the point of crash, one is limited by financial considerations, substantial application overhead, or minimal geographical separation between the primary and recovery sites. In this paper, we argue for cloud-based DR and pipelined synchronous replication as an antidote to these problems. Cloud hosting promises economies of scale and on-demand provisioning that are a perfect fit for the infrequent yet urgent needs of DR. Pipelined synchrony addresses the impact of WAN replication latency on performance, by efficiently overlapping replication with application processing for multi-tier servers. By tracking the consequences of the disk modifications that are persisted to a recovery site all the way to client-directed messages, applications realize forward progress while retaining full consistency guarantees for client-visible state in the event of a disaster. PipeCloud, our prototype, is able to sustain these guarantees for multi-node servers composed of black-box VMs, with no need of application modification, resulting in a perfect fit for the arbitrary nature of VM-based cloud hosting. We demonstrate disaster failover to the Amazon EC2 platform, and show that PipeCloud can increase throughput by an order of magnitude and reduce response times by more than half compared to synchronous replication, all while providing the same zero data loss consistency guarantees.

To accommodate the increasing demands for bandwidth, Internet Service Providers (ISPs) have deployed higher-speed links and reconfigurable optical add drop multiplexers (ROADMs) in their backbone networks. To address the reliability challenges due to failures and planned outages, ISPs typically use two backbone routers at each central office in a dual-home configuration. Thus at the IP layer, redundant backbone routers as well as redundant transport equipment to interconnect them are deployed to provide reliability through node and path diversity. However, adding such redundant resources increases the overall cost of the network. Hence, a fundamental redesign of the backbone network which avoids such redundant resources by leveraging the capabilities of an intelligent optical transport network is a highly desirable objective. It is clear that such a redesign must lower costs without compromising on the reliability achieved by today's backbone networks. Modeling the costs and reliability of the network at all layers is an important step in achieving this objective. In this paper, we undertake an in-depth investigation of the cost and reliability considerations involved in designing the next-generation backbone network. Our work includes a detailed analysis of the operation, cost and reliability of the network at the IP layer and the multiple layers below it. We discuss alternative backbone network designs which use only a single router at each central office but use the optical transport layer to carry traffic to routers at other offices in order to survive failures or outages of the single local router. We discuss trade-offs involved in using these designs.

Supporting Massively Multiplayer Online Role Playing Games (MMORPG) is a significant challenge. MMORPGs have become very popular because of their attractive structuring and incorporation of realistic and creative scenarios. World of Warcraft and Counter-Strike are examples of such games and are characterized by high interactivity (need very low network latency). Every action an individual player performs typically needs to be communicated to all the related players and the players need to react according to the 'current' environment and the cumulative actions of all the players. Games like Second Life involve a large number of players and require a persistent view of the world that is usually managed by a dedicated server (e.g., one that is hosted by the game's publisher). The load on such a server for player management and communication can be significant, and is likely to be a source of substantial latency.

Cellular networks have witnessed tremendous traffic growth recently, fueled by smartphones, tablets and new high speed broadband cellular access technologies. A key application driving that growth is video streaming. Yet very little is known about the characteristics of this traffic class. In this paper, we examine video traffic generated by three million users across one of the world's largest 3G cellular networks. This first deep dive into cellular video streaming shows that HLS, an adaptive bitrate streaming protocol, accounts for one third of the streaming video traffic and that it is common to see changes in encoding bitrates within a session. We also observe that most of the content is streamed at less than 255 Kbps and that only 40% of the videos are fully downloaded. Another key finding is that there exists significant potential for caching to deliver this content.

We investigate how consumers view content using Video on Demand (VoD) in the context of an IP-based video distribution environment. Users today can use interactive stream control functions such as skip, replay, fast-forward, pause, and rewind to control their viewing. The use of these functions can place additional demands on the distribution infrastructure (servers, network, and set top boxes) and can be challenging to manage with a large subscriber base. A model of user interaction provides insight into the impact of stream control on server and bandwidth requirements, client responsiveness, etc.

In this work, we are interested in improving the performance of constructive network coding schemes in lossy wireless environments. We propose I2NC - an approach that combines inter-session and intra-session network coding and has two strengths. First, the error-correcting capabilities of intra-session network coding make our scheme resilient to loss. Second, redundancy allows intermediate nodes to operate without knowledge of the decoding buffers of their neighbors. Based only on the knowledge of the loss rates on the direct and overhearing links, intermediate nodes can make decisions for both intra-session (i.e., how much redundancy to add in each flow) and inter-session (i.e., what percentage of flows to code together) coding. Our approach is grounded on a network utility maximization (NUM) formulation of the problem. We propose two practical schemes, I2NC-state and I2NC-stateless, which mimic the structure of the NUM optimal solution. We also address the interaction of our approach with the transport layer. We demonstrate the benefits of our schemes through simulation in GloMoSim.

The end to end system data performance over a 3G cellular network depends on many factors such as the number of users, interference, multipath propagation, radio resource management techniques as well as the interaction between these mechanisms and the transport protocol's flow and congestion mechanisms. Using controlled experiments in a public cell site, we investigate the interaction between TCP and the 3G UMTS/HSPA network's resource allocation, and its effect on fairness in the throughput achieved across multiple (up to 26) TCP flows in a loaded cell sector. Our field measurement results indicate that TCP fairness fluctuates significantly when the air interface (radio link) is the bottleneck. We also observe that TCP fairness is substantially better when the backhaul link (a fixed wired link) is the bottleneck, instead of the air interface. We speculate that the fairness of TCP flows is adversely impacted by the mismatch between the resource allocation mechanisms of TCP's flow and congestion control and that of the Radio Access Network (RAN).

With users increasingly focused on an online world, an emerging challenge for the network infrastructure is the need to support Massively Multiplayer Online Role Playing Games (MMORPG). This is an application domain that is attracting more players than ever before, very often with players distributed over a metropolitan area. Currently, MMORPG are built on an IP infrastructure with the primary responsibility on servers to do the work of disseminating control messages and having to predict/retrieve objects in each player's view. Limited server resources significantly impair the user's interactive experience. Modern fast-paced action games that run on a client/server architecture limit the number of players who can interact simultaneously since the server needs to handle the frequent updates and disseminate them. Scale and timeliness are major challenges of such a server-oriented gaming architecture. We propose Gaming over COPSS (G-COPSS), a communication infrastructure using a Content-Oriented Pub/Sub System (COPSS) to enable efficient decentralized information dissemination in MMORPG, exploiting the network and the end-systems for player management and information dissemination. We emulate an application that is particularly emblematic of MMORPG - Counter-Strike - but one in which all the players share a hierarchical structured map. Using trace-driven simulation, we demonstrate that G-COPSS can achieve high scalability and tight timeliness requirements of MMORPG. The simulator is parameterized using the results of careful microbenchmarking of the open-source CCN implementation and of standard IP-based forwarding. Our evaluations show that G-COPSS provides considerable performance improvement in terms of aggregate network load and update latency compared to that of a traditional IP server-based infrastructure.

As content providers adopt peer-to-peer approaches for content sharing and distribution, they face new challenges in guaranteeing privacy to their clients. Participating peers can glean information from their communication with other peers, such as their identities or the shared data and use this information for malicious purposes. We present Nemor, a protocol that allows a requesting peer and a corresponding serving peer to communicate anonymously with each other and from other participating peers, while protecting the identity of the content being exchanged. Nemor relies on a trusted intermediary, such as a provider-managed tracker, to identify a potential serving peer. A peer in Nemor joins one or more trees. Using a combination of a random walk, a probabilistic jump from one tree to another and constrained flooding, the requesting and serving peer dynamically construct an overlay path between them. A key differentiator of Nemor is the integrated design of a congestion avoidance mechanism that yields significant performance benefits without compromising on anonymity. Using experimental results from PlanetLab and simulations with traces from an operational VoD system, we demonstrate that Nemor outperforms state of the art approaches like TOR and OneSwarm. Our results confirm that Nemor, while being resilient to attacks on anonymity, achieves high performance and scalability and is suitable for a range of applications, including distribution of large volume content, such as streaming video.

Virtual machine technology and the ease with which VMs can be migrated within the LAN, has changed the scope of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration of virtual machines to likewise transform the scope of provisioning compute resources from a single data center to multiple data centers spread across the country or around the world. In this paper we present the CloudNet architecure as a cloud framework consisting of cloud computing platforms linked with a VPN based network infrastructure to provide seamless and secure connectivity between enterprise and cloud data center sites. To realize our vision of efficiently pooling geographically distributed data center resources, CloudNet provides optimized support for live WAN migration of virtual machines. Specifically, we present a set of optimizations that minimize the cost of transferring storage and virtual machine memory during migrations over low bandwidth and high latency Internet links. We evaluate our system on an operational cloud platform distributed across the continental US. During simultaneous migrations of four VMs between data centers in Texas and Illinois, CloudNet's optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 19GB, a 50% reduction.

IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1-2%) with orders of magnitude speedup relative to solving even the LP relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates. Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by LRU or LFU cache replacement policies. We also investigate the trade-off between disk space and network bandwidth.

The benefit of Class-of-Service (CoS) is an important topic in the "Network Neutrality" debate. Proponents of network neutrality suggest that over-provisioning is a viable alternative to CoS. We quantify the extra capacity requirement for an over-provisioned classless (i.e., best-effort) network compared to a CoS network providing the same delay or loss performance for premium traffic. We first develop a link model that quantifies this Required Extra Capacity (REC). For realistic traffic distributions (e.g., long-range dependent), we find the REC using ns-2 simulations of the CoS and classless links. Our primary contribution is in using these link models to quantify the REC for realistic network topologies under various scenarios including "closed loop" environments with traffic generated by TCP sources that adapt to the available capacity. We show that REC can be significant even when the proportion of premium traffic requiring performance assurances is small, a situation often considered benign for the over-provisioning alternative.

We investigate the user viewing activity for broadcast TV, pre-recorded content using Digital Video Recording (DVR) and video on demand (VoD) in an IP-based content distribution environment. Advanced stream control functions (play, pause, skip, rewind, etc.) provide users with a high level of interactivity, but place demands on the distribution infrastructure (servers, network, home-network) that can be difficult to manage at large scale. To support system design as well as network capacity planning, it is necessary to have a good model of user interaction. Using traces from a well-provisioned operational environment with a large user population, we first characterize interactivity for broadcast TV, DVR and VoD. We then develop parametric models of individual users stream control operations for VoD. Our analysis shows that interactive behavior is adequately characterized by two semi-Markov models, one for weekdays and another for weekends. We propose a parametric model for the underlying sojourn time distributions and show that it results in a superior fit compared to well known distributions (generalized Pareto and Weibull). In order to validate that our models faithfully capture user behavior, we compare the workload that a VoD server experiences in response to actual traces and synthetic data generated from our proposed models.

Delay-insensitive applications such as P2P file sharing, data center backups and software updates generate substantial amounts of traffic. This traffic, transported potentially over multiple TCP connections, competes with traffic from other possibly interactive applications. Today, with TCP, they compete on an equal level for each individual connection. In this paper, we propose a new TCP variant for such delay-insensitive applications, which we call Network Friendly TCP (NF-TCP). NF-TCP is responsive to available bandwidth, seeking to quickly and efficiently utilize the same in a congestion-free situation, while backing-off more aggressively than standard TCP on encountering competing traffic in a congested network. NF-TCP uses a novel combination of utilizing measurement of available bandwidth and ECN-based congestion avoidance techniques to ensure that it is truly friendly to existing TCP connections. We evaluate the performance of NF-TCP through ns-2 simulations and present the initial results enlightening the friendly nature of NF-TCP compared to standard TCP.

In this extended abstract we explore the architectural components of a Cloud Control Architecture with the aid of a number of cloud computing use cases. We specifically consider cloudbursting and follow-the-sun and focus on the mechanisms and user/provider interactions that would make these scenarios real. We are particularly concerned with the coordination of cloud and networking resources and mechanisms that would be applicable to cloud providers that are also network service providers.

We propose a novel privacy-preserving distributed infrastructure in which data resides only with the publishers owning it. The infrastructure disseminates user queries to publishers, who answer them at their own discretion. The infrastructure enforces a publisher k-anonymity guarantee, which prevents leakage of information about which publishers are capable of answering a certain query. Given the virtual nature of the global data collection, we study the challenging problem of efficiently locating publishers in the community that contain data items matching a specified query. We propose a distributed index structure, UQDT, that is organized as a union of Query Dissemination Trees (QDTs), and realized on an overlay (i.e., logical) network infrastructure. Each QDT has data publishers as its leaf nodes, and overlay network nodes as its internal nodes; each internal node routes queries to publishers, based on a summary of the data advertised by publishers in its subtrees. We experimentally evaluate design tradeoffs, and demonstrate that UQDT can maximize throughput by preventing any overlay network node from becoming a bottleneck.

Multicast is an approach that uses network and server resources efficiently to distribute information to groups. As networks evolve to become information-centric, users will increasingly demand publish-subscribe based access to fine-grained information, and multicast will need to evolve to (i) manage an increasing number of groups, with a distinct group for each piece of distributable content; (ii) support persistent group membership, as group activity can vary over time, with intense activity at some times, and infrequent (but still critical) activity at others. These requirements raise scalability challenges that are not met by today's multicast techniques. In this paper, we propose the MAD (multicast with adaptive dual-state) architecture to provide efficient multicast service at massive scale. MAD can scalably support a vast number of multicast groups, with varying activity over time, based on two key novel ideas: (i) decouple group membership from forwarding information, and (ii) apply an adaptive dual-state approach to optimize for the different objectives of active and inactive groups. We focus on the scalability characteristics of MAD and demonstrate through analysis, simulation and implementation that the architecture achieves high performance and efficiency.

We present CPM, a unified approach that exploits server multicast, assisted by peer downloads, to provide efficient video-on-demand (VoD) in a service provider environment. We describe our architecture and show how CPM is designed to dynamically adapt to a wide range of situations including highly different peer-upload bandwidths, content popularity, user request arrival patterns, video library size, and subscriber population. We demonstrate the effectiveness of CPM using simulations (based on an actual implementation codebase) across the range of situations described above and show that CPM does significantly better than traditional unicast, different forms of multicast, as well as peer-to-peer schemes. Along with synthetic parameters, we augment our experiments using data from a deployed VoD service to evaluate the performance of CPM.

As more and more video content is made available and accessed on-demand, content and service providers face challenges of scale. Today's delivery mechanisms, especially unicast, require resources to scale linearly with the number of receivers and library sizes. Unlike these mechanisms, with multicast, the load on a server is relatively independent of the number of receivers. Adopting multicast for on-demand access, however, is challenging because of the need to temporally aggregate requests. In this paper, we investigate the importance of an intelligent scheduler and a good data model for achieving good aggregation of requests into multicast groups. We examine the use of an Earliest Deadline First (EDF)-like scheduler that aims to schedule the transmission of chunks of video according to their deadlines using multicast. We show through analysis that this approach is optimal in terms of the data transmitted by the server. Using trace data from an operational service, we show that our approach reduces server bandwidth by as much as 65% compared to traditional techniques such as unicast and cyclic multicast. Finally, our approach achieves good aggregation even when 50% of the users use a typical VoD stream-control function like skip, to view different parts of the video.

Multicast is attracting a resurgence of interest because it has a potential to address the explosively growing need for efficient streaming of large-volume Internet content. However, to realize the potential, large-scale multicast distribution requires effective congestion control. In this paper, we design SIM, a protocol that combines three distinct mechanisms (Selective participation, Intra-group transmission adjustment, and Menu adaptation) to provide a general solution for efficient fair scalable privacy-preserving multicast congestion control with heterogeneous receivers. Whereas the individual mechanisms have appeared in earlier multicast protocols, our main contribution lies in the cohesive integration of the techniques. SIM achieves such integration by operating the three mechanisms at different timescales and distributing the responsibility for control to different entities in the network. Besides, to instantiate and integrate the three control mechanisms, SIM employs several novel techniques of independent interest. Our extensive evaluation confirms the salient properties of SIM in diverse settings where receiving capabilities are highly heterogeneous, bottleneck capacities fluctuate, bottlenecks migrate, and session membership is dynamic.

We propose a novel transport protocol that effectively utilizes available bandwidth and diversity gains provided by heterogeneous, highly lossy paths. Our Multi-Path LOss-Tolerant (MPLOT) protocol can be used to provide significant gains in the goodput of wireless mesh networks, subject to bursty, correlated losses with average loss-rates as high as 50%, and random outage events. MPLOT makes intelligent use of erasure codes to guard against packets losses, and a Hybrid-ARQ/FEC scheme to reduce packet recovery latency, where the redundancy is adaptively provisioned into both proactive and reactive FECs. MPLOT uses dynamic packet mapping based on current path characteristics, and does not require packets to be delivered in sequence to ensure reliability. We present a theoretical analysis of the different design choices of MPLOT and show that MPLOT makes an optimal trade-off between goodput and delay constraints. We test MPLOT, through simulations, under a variety of test scenarios and show that it effectively exploits path diversity in addition to aggregating path bandwidths. We also show that MPLOT is fair to single-path protocols like TCP-SACK.

Broadcast TV distribution over an IP network requires stringent QoS constraints, such as low latency and loss. The main challenge to achieving these QoS objectives is how to design the network to respond to network failures. Streaming content in IPTV is typically delivered to the distribution points on the IP backbone using IP multicast, and in the case being considered, with protocol independent multicast source specific mode (PIM-SSM). A proven failure restoration technique at the IP layer is link-restoration using MPLS or layer-2 fast reroute (FRR). Link-based FRR creates a pseudo-wire or tunnel in parallel to the IP adjacencies (links) along the forwarding path used by the PIM tree. For each such tunnel both a primary and backup path are defined. The backup path is Layer-1-disjoint from the physical link and when the link fails, the pseudo-wire can be rapidly restored. Thus, single link failures are transparent to the interior gateway protocol (IGP). Although one may choose the back-up pathpsilas IGP link weights to avoid traffic overlap during any single link failure, multiple failures may still cause traffic overlap with FRR. We present a cross-layer restoration approach that combines both FRR-based restoration for single link failure and ldquohitlessrdquo (i.e., without loss) PIM tree reconfiguration algorithms to prevent traffic overlap when multiple failures occur.

The benefit of Class-of-Service (CoS) is an important topic in the "Network Neutrality" debate. Proponents of network neutrality suggest that over-provisioning is a viable alternative to CoS. We quantify the extra capacity requirement for an over-provisioned classless (i.e., best-effort) network compared to a CoS network providing the same delay or loss performance for premium traffic. We first develop a link model that quantifies this Required Extra Capacity (REC). For bursty and realistic traffic distributions, we find the REC using ns-2 simulation comparisons of the CoS and classless link cases. We use these link models to quantify the REC for realistic network topologies. We show that REC can be significant even when the proportion of premium traffic is small, a situation often considered benign for the over-provisioning alternative.

We describe XTreeNet, a distributed query dissemination engine which facilitates democratization of publishing and efficient data search among members of online communities with powerful full-text queries. This demonstration shows XTreeNet in full action. XTreeNet serves as a proof of concept for democratic community search by proposing a distributed novel infrastructure in which data resides only with the publishers owning it. Expressive user queries are disseminated to publishers. Given the virtual nature of the global data collection (e.g., the union of all local data published in the community) our infrastructure efficiently locates the publishers that contain matching documents with a specified query, processes the complex full-text query at the publisher and returns all relevant documents to querier.

Multimedia distribution, especially broadcast TV distribution over an IP network requires high bandwidth combined with tight latency and loss constraints, even under failure conditions. Due to the high bandwidth requirements of broadcast TV distribution, use of IP-based multicast to distribute TV content is needed for efficient use of capacity. The protection and restoration mechanisms currently adopted in IP backbones use either IGP re-convergence or some form of fast reroute. The IGP re-convergence mechanism is too slow for real-time multimedia distribution while a drawback of fast reroute is that traffic is re-routed on a link-basis (instead of end-to-end) so there can be traffic overlap during failures. By this we mean traffic passing through the same link along the same direction more than once; this requires more link capacity or it will result in congestion. We propose a routing method that interacts with Fast Reroute and multicast to minimize traffic overlap during failures. We also present an algorithm for link weight setting that avoids traffic overlap due to any single link failure. Performance analysis shows that our methods improve network service availability and significantly reduce the impact of failures.

The desire or ability of an ISP to provide differentiated service is a current hotly debated topic. In this paper, we quantify the value of having differentiated service (i.e., class-of-service (CoS)) support in an IP backbone. We compare the capacity requirements of a Diffserv environment providing service for applications that require delay or loss assurances in comparison to a network that provides classless (i.e., best-effort) service and still has to meet the same performance assurances. Our modeling framework first develops a link model that quantifies the required extra capacity (REC) in order for a classless link to provide the same level of performance as experienced by premium class traffic passing through a fixed capacity CoS link. We develop the REC calculations for the cases when average delay or the average loss probability is the target performance goal with Poisson or Markov modulated Poisson process (MMPP) input traffic. Our primary contribution is in quantifying the value of the CoS support in a network setting.

Broadband technologies have made multi-hop wireless communications a reality. Loss-prone multi-hop networks pose challenges to link and transport layer protocols. Wireless links need to export low link-latencies, high goodputs and low residual loss rates to effectively enable interactive applications. Current link protocols with high ARQ persistence incur high latencies that impair such applications. We propose LL-HARQ, a link protocol that meets these goals. However, under high and bursty loss rates even LL-HARQ exports a small residual loss rate that could accumulate over multiple hops. Since TCP-SACK cannot handle error rates greater than 5%, a transport protocol (LT-TCP) designed for loss tolerance can be used under such cases. We provide insights into the structuring of the building blocks and balance between error-protection functions at the two layers and examine the case for cross-layer co-operation. Finally, we demonstrate that the combination achieves improved end-end performance (delay, loss and goodput) over traditional approaches.

Virtual private networks (VPNs) feature notable characteristics in structure and traffic patterns that can be exploited by the service provider to achieve significant capacity savings. Efficient provisioning of point-to-point connections using statistical admission control is well understood. However, provisioning a VPN involves provisioning a set of point-to-multipoint connections and features an additional dimension in the form of a traffic matrix. Consequently we have multiple network mechanisms that are important for efficient operation: a) admission control, b) signaling-based per-link reservations, c) traffic matrix estimation. In this paper we examine the relative importance of mechanisms that positively affect the operational efficiency in the context of VPN provisioning. Using insights from our extensive measurement based study on the structural properties usually observed in VPNs, we build a simulation framework to quantify the trade-offs in opting for one mechanism over the other. We arrive at our conclusions with the help of simulations featuring a variety of VPN structures and network topologies. We find that the structural characteristics of VPNs cause traffic matrix estimation to be a dominant factor in determining the utilization gains. Consequently, we find that deploying statistical techniques might not be worth the effort if the traffic matrix is not incorporated. While signaling-based reservation mechanisms lead to higher utilization, edge-based techniques prove to be lot more scalable and simpler to realize. We explore the means to reduce the performance penalty associated with such simpler techniques.

Congestion control in ring based packet networks is challenging due to the fact that every node in the network runs both a rate adaptation algorithm, analogous to an endpoint algorithm in other network architectures, and a rate allocation algorithm, analogous to switch-based algorithms in other network architectures. This work describes a congestion control algorithm for IEEE 802.17 resilient packet rings called the enhanced conservative mode algorithm that aims to avoid congestion and achieve a fair rate allocation for fairness eligible traffic in the case of a single bottleneck. We first present analysis to show that existing approaches for RPR congestion control (aggressive and conservative mode) have deficiencies. We present simulation results showing that the proposed enhanced conservative mode congestion control algorithm is a significant improvement. In conjunction with other mechanisms specified in the IEEE 802.17 MAC, the proposed algorithm achieves high utilization on the ring with minimal starvation and oscillations, allows sources to fast start, and provides quality of service for multiple classes of service that require rate, delay and jitter guarantees.

Virtual Private Networks provide secure and reliable communication between customer sites. With increase in number and size of VPNs, providers need efficient provisioning techniques that adapt to customer demand by leveraging a good understanding of VPN properties.In this paper we analyze two important properties of VPNs that impact provisioning - (a) structure of customer endpoint (CE) interactions and (b) temporal characteristics of CE-CE traffic. We deduce these properties by computing traffic matrices from SNMP measurements. We find that existing traffic matrix estimation techniques are not readily applicable to the VPN scenario due to the scale of the problem and limited measurement information. We begin by formulating a scalable technique that makes the most out of existing measurement information and provides good estimates for common VPN structures.We then use this technique to analyze SNMP measurement from a large IP VPN service provider. We find that even with limited measurement information we can realize adaptive provisioning for a significant fraction of VPNs, namely, those constituting the Hub-and-Spoke category. In addition, the ability to infer the structure of VPNs holds special significance for provisioning tasks arising from topology changes, link failures and maintenance. We are able to provide a classification of VPNs by structure and identify CEs that act as hubs of communication and hence require prioritized treatment during restoration and provisioning.

Today's metro networks have evolved from the need to support traditional voice and private line services and were optimized to support time division multiplexing (TDM) services. However, the growth of private line services is dominated by the access (also called "backhaul") to packet switches that provides frame relay, ATM, IP and Ethernet services. This paper provides a coherent architectural vision for metro access networks, which the author calls the packet-aware transport network (PATN). The PATN is designed to support the packet and TDM transport within metro areas in a scalable manner, and interfaces the inter-city packet and TDM transport backbone networks. Since the PATN supports the IETF pseudo-wire encapsulation (PWE) schemes for all packet access traffic, it is synergistic with the move towards a converged MPLS-based multi-service packet backbone network.

Virtual Private Networks (VPNs) feature notable characteristics in structure and traffic patterns that allow for efficient resource allocation. A strategy that exploits the underlying characteristics of a VPN can result in significant capacity savings to the service provider. There are a number of admission control and bandwidth provisioning strategies to choose from. We examine tradeoffs in design choices in the context of distinctive characteristics of VPNs. We examine the value of signaling-based mechanisms, traffic matrix information and structural characteristics of VPNs in the way they impact resource utilization and service quality. We arrive at important conclusions which could have an impact on the way VPNs are architected. We show that the structure of VPNs profoundly influences achievable resource utilization gains with various admission control and provisioning schemes.

A network running OSPF takes several tens of seconds to recover from a failure, using the current default parameter settings. The main component of this delay is the time required to detect a failure using the hello protocol. Reducing the value of the hellointerval can speed up the failure detection time. However, too small a value of the hellointerval can result in an increase in network congestion, potentially causing multiple consecutive hellos to be lost. This can lead to a false breakdown of adjacencies between routers. Such false alarms not only disrupt network traffic by causing unnecessary routing changes, but also increase the processing load on the routers, which may potentially lead to routing instability. In this paper, we investigate the following question - what is the optimal value for the hellointerval that will lead to fast failure detection in the network, while keeping occurrences of false alarms within acceptable limits? We examine the impact of both network congestion and the network topology on the optimal value for the hellointerval. Additionally, we investigate the effectiveness of faster failure detection in achieving fast failure recovery in OSPF networks.

In large scale, operational Internet protocol networks, creating timely, accurate and network-wide views of the intradomain topology is a fundamental problem. Topical network backbones consist of hundreds of routers, which establish routing adjacencies with one another through static configuration and dynamic routing protocols, such as open shortest path first (OSPF). We describe the design of an OSPF topology server which tracks intradomain topology, by passively and safely listening into OSPFs reliable flooding mechanism, or by pushing and pulling information from the routers via the simple network management protocol. We provide a detailed evaluation and comparison of the two approaches in terms of operational issues, reliability and timeliness of information.

As IP technologies providing both tremendous capacity and the ability to establish dynamic security associations between endpoints emerge, virtual private networks (VPNs) are going through dramatic growth. The number of endpoints per VPN is growing and the communication pattern between endpoints is becoming increasingly hard to predict. Consequently, users are demanding dependable, dynamic connectivity between endpoints, with the network expected to accommodate any traffic matrix, as long as the traffic to the endpoints does not overwhelm the capacity of the respective ingress and egress links. We propose a new service interface, termed a hose, to provide the appropriate performance abstraction. A hose is characterized by the aggregate traffic to and from one endpoint in the VPN to a set of other endpoints in the VPN, and by an associated performance guarantee. Hoses provide important advantages to a VPN customer: (1) flexibility to send traffic to a set of endpoints without having to specify the detailed traffic matrix, and (2) reduction in the size of access links through multiplexing gains obtained from the natural aggregation of the flows between endpoints. As compared with the conventional point-to-point (or customer pipe) model for managing quality of service (QoS), hoses provide reduction in the state information a customer must maintain. On the other hand, hoses would appear to increase the complexity of the already difficult problem of resource management to support QoS. To manage network resources in the face of this increased uncertainty, we consider both conventional statistical multiplexing techniques, and a new resizing technique based on online measurements. To study these performance issues, we run trace-driven simulations, using traffic derived from AT&T's voice network and from a large corporate data network. From the customer's perspective, we find that aggregation of traffic at the hose level provides significant multiplexing gains. From the provider's perspective, we find that the statistical multiplexing and resizing techniques deal effectively with uncertainties about the traffic, providing significant gains over the conventional alternative of a mesh of statically sized customer pipes between endpoints.

We study the performance of TCP in an internetwork consisting of both rate-controlled and nonrate-controlled segments. A common example of such an environment occurs when the end systems are part of IP datagram networks interconnected by a rate-controlled segment, such as an ATM network using the available bit rate (ABR) service. In the absence of congestive losses in either segment, TCP keeps increasing its window to its maximum size. Mismatch between the TCP window and the bandwidth-delay product of the network results in accumulation of large queues and possibly buffer overflows in the devices at the edges of the rate-controlled segment, causing degraded throughput and unfairness. We develop an explicit feedback scheme, called explicit window adaptation, based on modifying the receiver's advertised window in TCP acknowledgments returning to the source. The window size indicated to TCP is a function of the free buffer in the edge device. Results from simulations with a wide range of traffic scenarios show that this explicit window adaptation scheme can control the buffer occupancy efficiently at the edge device, and results in significant improvements in packet loss rate, fairness, and throughput over a packet discard policy such as random early detection (RED).

We present a formal specification of the selective acknowledgment (SACK) mechanism that is being proposed as a new standard option for TCP. The formal specification allows one to reason about the SACK protocol; thus, we are able to formally prove that the SACK mechanism does not violate the safety properties (reliable, at most once, and in order message delivery) of the acknowledgment (ACK) mechanism that is currently used with TCP. The new mechanism is being proposed to improve the performance of TCP when multiple packets are lost from one window of data. The proposed mechanism for implementing the SACK option for TCP is sufficiently complicated that it is not obvious that it is indeed safe, so we think it is important to formally verify its safety properties. In addition to safety, we are also able to show that SACK can improve the time it takes for the sender to recover from multiple packet losses. With the additional information available at a SACK sender, the round-trip time that a cumulative ACK sender waits before retransmitting each subsequent packet lost after the very first loss can be saved. We also show that SACK can improve performance even with window sizes as small as four packets and in situations where acknowledgment packets are lost.

An increasing number of communication services are moving to an IP-based infrastructure. Packet telephony is probably the first important real-time service that must be supported well over an IP network. The use of IP presents a tremendous opportunity for service providers to exploit endpoint intelligence to offer creative new services going far beyond the current telephony service model. However, to support telephony, signaling protocols are needed that allow the service provider to offer network-layer service differentiation and, at the same time, to control access to both the enhanced network-layer quality of service as well as other services. This paper describes the distributed open signaling architecture (DOSA), which incorporates protocols that meet these needs. A key contribution of our work is a recognition of the need for coordination between call signaling, which controls access to telephony-specific services, and resource management, which controls access to network-layer resources.

We discuss a hardware implementation of an explicit rate allocation algorithm for support of available bit rate (ABR) service in ATM switches. We then demonstrate the effectiveness of the algorithm at the network-level through measurements on the actual implementation in a network testbed. The rate allocation algorithm has several desirable properties, such as exact computation of the max-min rates, O(1) computations per resource management (RM) cell received, and the ability to provide minimum cell rate (MCR) guarantees. We show that the algorithm can be implemented with a modest amount of hardware (64,000 gates in an Altera 10K100 programmable logic device and 16 bytes of SRAM storage per VC), and that even a slow FPGA-based implementation with a 20 MHz internal clock rate can process RM cells within one cell time at an OC-3 port. We also outline the design for supporting OC-12 and OC-48 links. We present results from measurements of ABR traffic in network configurations with up to four bottlenecks and 100 connections. The results show that the algorithm is able to converge to the exact max-min fair allocations with no oscillations after convergence, maintains minimum rate guarantees, and is also able to maintain high link utilization in the presence of on-off traffic.

As communications networks grow in both speed and scale, there is a need to switch packets at higher speeds. One approach is to use fast switches that operate at the datalink layer (layer 2) to build a high-performance "cloud" for interconnecting network layer routers. The use of ATM for the layer 2 network provides a number of advantages when the network is shared among multiple services and when additional capabilities that are supported well at layer 2, such as fine-grained per-flow QoS, are needed. This paper describes a robust and efficient solution to the problem of supporting IP over a large non-broadcast multiple-access (NBMA) network, such as ATM. The solution, known as SUBMARINE, calculates loop-free shortcut routes across the NBMA network. SUBMARINE builds on existing IP routing protocols, is designed to scale to multiple areas within a single autonomous system, and makes minimal changes to the IP forwarding process.

Cut-through switching has been used as a way to reduce network latency. In particular, with ATM, packets are broken up into fixed length cells, and each cell is forwarded without having to wait for the remaining cells of the packet. However, with the interest in VC-merging, packets from multiple virtual circuits are merged into a single virtual circuit on an output link. In this case, it is critical to retain the fundamental characteristic of ATM to not interleave cells of a packet with that of another. VC-merging arises often, as in the case of a multipoint-to-multipoint or multipoint-to-point connection. We examine the stability of policies for cut-through switching when VCs are merged. We consider a queueing model of a single VC merge point employing cut-through switching. We show that, if subunits of packets cannot be interleaved on the output link, a simple round-robin polling service discipline may make the merge point unstable. Instability means that the input queues have a tendency to build up infinitely even though the total input data rate is less than the output link capacity. We prove that the round-robin discipline is stable if the merge point is symmetric in that packet rates on all input VCs are equal (or at least "almost equal"). We also prove that two simple modifications of the round-robin discipline make the merge point always stable. Simulation results of one of the modifications show improved performance over "pure" cut-through and store-and-forward, at least in some cases.

As IP technologies providing both tremendous capacity and the ability to establish dynamic secure associations between endpoints emerge, Virtual Private Networks (VPNs) are going through dramatic growth. The number of endpoints per VPN is growing and the communication pattern between endpoints is becoming increasingly hard to forecast. Consequently, users are demanding dependable, dynamic connectivity between endpoints, with the network expected to accommodate any traffic matrix, as long as the traffic to the endpoints does not overwhelm the rates of the respective ingress and egress links. We propose a new service interface, termed a hose, to provide the appropriate performance abstraction. A hose is characterized by the aggregate traffic to and from one endpoint in the VPN to the set of other endpoints in the VPN, and by an associated performance guarantee.Hoses provide important advantages to a VPN customer: (i) flexibility to send traffic to a set of endpoints without having to specify the detailed traffic matrix, and (ii) reduction in the size of access links through multiplexing gains obtained from the natural aggregation of the flows between endpoints. As compared with the conventional point to point (or customer-pipe) model for managing QoS, hoses provide reduction in the state information a customer must maintain. On the other hand, hoses would appear to increase the complexity of the already difficult problem of resource management to support QoS. To manage network resources in the face of this increased uncertainty, we consider both conventional statistical multiplexing techniques, and a new resizing technique based on online measurements.To study these performance issues, we run trace driven simulations, using traffic derived from AT&T's voice network, and from a large corporate data network. From the customer's perspective, we find that aggregation of traffic at the hose level provides significant multiplexing gains. From the provider's perspective, we find that the statistical multiplexing and resizing techniques deal effectively with uncertainties about the traffic, providing significant gains over the conventional alternative of a mesh of statically sized customer-pipes between endpoints.

Packet telephony is of increasing interest in both the telecommunications and Internet communities. The emergence of packet telephony will create new services, and presents an opportunity to rethink how conventional telephony services are implemented. In this paper, we present an architecture for telephony over packet networks (TOPS). TOPS allows users to move between terminals or to use mobile terminals while being reachable by the same name. TOPS users can have multiple terminals and control how calls are routed to them. TOPS allows for terminals with a range of capabilities such as support for video, whiteboard, and other media with a variety of coding formats. TOPS retains the necessary information on terminal capabilities to determine the appropriate type of communication to be established with the remote terminal. The architecture assumes that the underlying network supports the establishment of end-to-end connectivity between terminals, with an appropriate quality of service. The components of TOPS are a directory service, an application layer signaling protocol, and a logical channel abstraction for communication between end-systems. The directory service maps a user's name to a set of terminals where the user may be reached. A user can control the translation operation by specifying profiles that customize how his name is mapped to a set of terminals where he can be reached. Terminal capabilities are also stored in the directory service. The application layer signaling protocol establishes and maintains call state between communicating terminals. The logical channel abstraction provides a shared end-to-end context for a call's constituent media and control streams, while isolating the applications from the details of the network transport mechanisms. In addition to supporting simple point-to-point calls, the architecture supports both centralized and decentralized conferencing. We also introduce a simple encapsulation format for voice.

We have proposed a smoothing and rate adaptation algorithm-SAVE (Smoothed Adaptive Video over Explicit rate networks)-for transport of compressed video over rate-controlled networks. SAVE attempts to preserve quality as much as possible, and exercises control over the source rate only when essential to prevent unacceptable delay. In order to understand the impact on quality of rate adaptation, we have evolved the quality metrics typically used to evaluate the efficacy of mechanisms to transport video. We investigate the dynamic nature of rate reduction: any prolonged impairment is likely to be noticeable. We study the sensitivity of SAVE to its parameters and network characteristics. Finally, the utility of the proposed scheme is measured by its ability to multiplex a large number of streams effectively. Our evaluations are based on experiments with 20 traces of entertainment videos using different compression algorithms.

We propose a scheme for transmission of variable bit rate (VBR) compressed video for interactive applications using the explicit-rate congestion-control mechanisms proposed for the available bit rate (ABR) service in asynchronous transfer mode networks. Compressed video is inherently bursty, with rate fluctuations over both short and long time scales. This source behavior can be accommodated by the ABR service, since the explicit-rate scheme allows sources to request varying amounts of bandwidth over time. Moreover, when the bandwidth demand cannot be met, the network provides feedback indicating the bandwidth currently available to a connection. In our scheme, the video source rate is matched to the available bandwidth by modifying the quantization level used during compression. We use trace-driven simulations to examine how effective the enhanced explicit-rate scheme is in "rate matching" between the network and the source and the effect on end-to-end delay. We also look at the sensitivity of the proposed scheme to the estimates of the network round-trip times and to inaccuracies in the rate requests made by sources.

Supporting compressed video efficiently on networks is a challenge because of its burstiness. Although a large number of applications using compressed video are rate adaptive, it is also important to preserve quality as much as possible. We propose a smoothing and rate adaptation algorithm, called SAVE, that the compressed video source uses in conjunction with explicit rate based control in the network. SAVE smoothes the demand from the source to the network, thus helping achieve good multiplexing gains. SAVE maintains the quality of the video and ensures that the delay at the source buffer does not exceed a bound. We examine the effectiveness of SAVE across 28 different traces (entertainment and teleconferencing videos) using different compression algorithms.

Networks support a wide range of applications with diverse service quality requirements. This demands distinct identification of flows according to their requirements, and the ability to service them differently. Different networking technologies have been evolving towards this common goal. ATM is cost-efficient by exploiting switching. But signaling is slow making ATM unattractive for bursty data traffic. One difficulty with ATM signaling is the inextricable connection between basic connectivity and QoS management. Consequently, every flow, even a best-effort flow suffers the overhead of end-to-end connection establishment. We propose a new lightweight architecture for ATM signaling called UNITE. The fundamental philosophy of UNITE is the separation of connectivity from QoS control. This eliminates the round-trip connection setup delay before initiating data transmission. Using a single cell with proper encoding, we avoid the overhead of reassembly and segmentation on the signaling channel, and enable hardware implementation. Performing QoS negotiation in-band, allows the switches in the path to process QoS-requests in parallel, facilitates connection specific control policies, supports both sender and receiver initiated QoS, and allows for uniform treatment of unicast and multicast connections. UNITE supports variegated multicast trees.

We study the performance of the TCP in an internetwork consisting of both rate-controlled and non-rate-controlled segments. A common example of such an environment occurs when the end systems are part of IP datagram networks interconnected by a rate-controlled segment, such as an ATM network using the ABR service. In the absence of congestive losses in either segment, the TCP keeps increasing its window to its maximum size. Mismatch between the TCP window and the bandwidth-delay product of the network will result in an accumulation of large queues and possibly buffer overflows in the devices at the edges of the rate-controlled segment, causing degraded throughput and unfairness. We develop an explicit feedback scheme, called explicit window adaptation based on modifying the receiver's advertised window in the TCP acknowledgments returning to the source. The window size indicated to the TCP is a function of the free buffer in the edge device. Results from simulations with a wide range of traffic scenarios show that this explicit window adaptation scheme can control the buffer occupancy efficiently at the edge device, and results in significant improvements in packet loss rate, fairness, and throughput over a packet discard policy such as drop-from-front or random early detection.

The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.

Supporting compressed video efficiently on networks is a challenge because of its burstiness. Although a large number of applications using compressed video allow adaptive rates, it is also important to preserve quality as much as possible. We propose a smoothing and rate adaptation algorithm for compressed video, called SAVE, that is used in conjunction, with explicit rate based control in the network. SAVE smooths the demand from the source to the network, thus helping achieve good multiplexing gains. SAVE maintains the quality of the video and ensures that the delay at the source buffer does not exceed a bound. We show that SAVE is effective by demonstrating its performance across 28 different traces (entertainment and teleconferencing videos) that use different compression algorithms.

We study the performance of bidirectional TCP/IP connections over a network that uses rate-based flow and congestion control. An example of such a network is an asynchronous transfer mode (ATM) network using the available bit rate (ABR) service. The sharing of a common buffer by TCP packets and acknowledgment (acks) has been known to result in an effect called ack compression, where acks of a connection arrive at the source bunched together, resulting in unfairness and degraded throughput. It has been the expectation that maintaining a smooth flow of data using rate-based flow control would mitigate, if not eliminate, the various forms of burstiness experienced with the TCP window flow control. However, we show that the problem of TCP ack compression appears even while operating over a rate-controlled channel. The degradation in throughput due to bidirectional traffic can be significant. For example, even in the simple case of symmetrical connections with adequate window sizes, the throughput of each connection is only 66.67% of that under one-way traffic. By analyzing the periodic bursty behavior of the source IP queue, we derive estimates for the maximum queue size and arrive at a simple predictor for the degraded throughput, for relatively general situations. We validate our analysis using simulation on an ATM network using the explicit rate option of the ABR service. The analysis predicts the behavior of the queue and the throughput degradation in simple configurations and in more general situations.

This paper proposes a multipoint-to-multipoint multicast architecture for ATM networks. The necessity for such an architecture stems from the scalability requirements, both in terms of state to be maintained in the network and in terms of the group population dynamics, of a wide range of networking applications. We argue that approaches of using multicast servers or meshes of point-to-multipoint virtual circuits (VCs) may be inadequate solutions to this problem. We propose a true multipoint-to-multipoint architecture called SEAM, which uses a single VC for a multicast group consisting of multiple senders and receivers. We achieve this without changes to ATM's AAL5. SEAM relies on an additional switching feature we call cut-through forwarding, which enables the mapping of several incoming VCs into outgoing VCs. We believe that SEAM is both an important and necessary step in the evolution of ATM. It will enable applications relying on group multicast to benefit directly from ATM's quality of service support and scalable bandwidth and the resulting performance advantages. Also, it considerably simplifies the problem of supporting IP multicast over large ATM networks.

We examine the performance of bidirectional TCP/IP connections over asynchronous transfer mode (ATM) networks using the available bit rate (ABR) service. The problem of "ack-compression" re-appears, although the queues are primarily at the end-systems. We further the understanding of the problem by quantitatively analyzing the periodic bursty behavior of the source IP queue. We are able to predict the peak values for the queue and arrive at a simple robust predictor for the degraded throughput, applicable for relatively general situations. The degradation in throughput due to bidirectional traffic can be significant. For example, even in the simple case of symmetrical connections with adequate window sizes, the throughput of each connection is only 66.67% of that under one-way traffic. We validate our analysis using simulation, where the ATM network uses the explicit rate option. We show that the analysis predicts the behavior of the queue and the throughput degradation. We observe the need to separate the flow of acknowledgments and data for the bidirectional TCP connection and for inter-leaving their processing at the end-systems to overcome the problem of ack compression.

We propose a scheme for transmission of variable-bit-rate compressed video over ATM networks using the explicit-rate congestion control mechanisms proposed for the available bit rate (ABR) service. Compressed video is inherently bursty with rate fluctuations over both short and long time scales. We feel that this source behavior can naturally take advantage of the ABR service, since the ABR explicit-rate schemes allow sources to request varying amounts of bandwidth over time, while reserving a minimum for the entire duration of the connection. Moreover when the bandwidth demand cannot be met, the network provides feedback indicating the bandwidth currently available to a connection. This information can be used to match the video source rate to the available bandwidth by modifying the quantization level used during compression. We use trace driven simulations to examine how effective the enhanced explicit rate scheme is in "rate matching" between the network and the source and the effect on end-end delay. We also look at the sensitivity of the proposed scheme to the estimates of the network round-trip times and to inaccuracies in the rate requests made by sources.

We study both the correctness and performance of the source/destination protocol of the available bit rate (ABR) service in asynchronous transfer mode (ATM) networks. Although the basic source/destination protocol for congestion management is relatively simple, the protocol specification has to cope with several "real-world" cases such as failures and delayed/lost feedback which may introduce complexity. Rigorous proofs of the correct functioning of the protocol based on a formal specification is necessary. We use a formal extended finite state machine (EFSM) model to show that the ABR source/destination protocol is free of live-locks, so that under all conditions both resource management (RM) and data cells will be transmitted. We also show that the network options of explicit forward congestion indication (EFCI) and explicit rate (ER) interoperate correctly. We use the understanding of the informal English description of the source/destination behavior and of our EFSM model to derive conditions that ensure that the source transmission rate is stable in the presence of delayed or lost feedback RM cells, especially under the operation of a source rule that requires the reduction of the source rate under these conditions. We arrive at bounds on the number of consecutive RM cell losses tolerated while the rate remains stable. We also provide a worst-case analysis of the delay in turning around RM cells at the destination station and the worst-case inter-departure time of forward RM cells from the source.

Rate based congestion control has been considered desirable, both to deal with the high bandwidth-delay products of today's high speed networks, and to match the needs of emerging multimedia applications. Explicit rate control achieves low loss because sources transmit smoothly at a rate adjusted through feedback to be within the capacity of the resources in the network. However, large feedback delays, presence of higher priority traffic, and varying transient situations make it difficult to ensure feasibility (i.e., keep the aggregate arrival rate below the bottleneck resource's capacity) while also maintaining high resource utilization. These conditions along with the "fast start" desired by data applications often result in substantial queue buildups.We describe a scheme that manages the queue buildup at a switch even under the most aggressive patterns of sources, in the context of the Explicit Rate option for the Available Bit Rate (ABR) congestion control scheme. A switch observes the buildup of its queue, and uses it to reduce the portion of the link capacity allocated to sources bottlenecked at that link. We use the concept of a "virtual" queue, which tracks the amount of queue that has been "reduced", but has not yet taken effect at the switch. We take advantage of the natural timing of "resource management" (RM) cells transmitted by sources. The scheme is elegant in that it is simple, and we show that it reduces the queue buildup, in some cases, by more than two orders of magnitude and the queue size remains around a desired target. It maintains max-min fairness even when the queue is being drained. The scheme is scalable, and is as responsive as can be expected: within the constraints of the feedback delay. Finally, no changes are needed to the ATM Forum defined source/destination policies.

Most operating systems use interface interrupts to schedule network tasks. Interrupt-driven systems can provide low overhead and good latency at low offered load, but degrade significantly at higher arrival rates unless care is taken to prevent several pathologies. These are various forms of receive livelock , in which the system spends all of its time processing interrupts, to the exclusion of other necessary tasks. Under extreme conditions, no packets are delivered to the user application or the output of the system. To avoid livelock and related problems, an operating system must schedule network interrupt handling as carefully as it schedules process execution. We modified an interrupt-driven networking implementation to do so; this modification eliminates receive livelock without degrading other aspects of system performance. Our modifications include the use of polling when the system is heavily loaded, while retaining the use of interrupts ur.Jer lighter load. We present measurements demonstrating the success of our approach.

Formal specifications are indispensible for computer-aided verification and testing of communication protocols. However, a large number of the practical protocols, including ATM, have only informal specifications mostly in English. There an no general procedures to derive formal specifications from such informal specifications. As a case study, we consider an important protocol specification-ATM's available bit rate (ABR) service specification. The ABR source/destination policies have been specified using an English description in the main body of the ATM Forum's draft traffic management specification from which it is hard to conduct a formal analysis. Furthermore, while considerable energy has been spent in providing a reasonably precise specification, while allowing for appropriate implementation latitude, an English description still has the potential for different interpretations. We model the protocol by parametrized communicating extended finite state machines with timers, which is often called a transitions system, and present a formal specification by transitions of the system. We also provide insights gained in the derivation of the formal specification. Furthermore, we introduce a scheduler involved in transmitting queued cells at the allowed cell rate to meet the minimal requirements from the source and destination protocols. We present the transitions for the source/destination/scheduler machines, primarily for transmitting cells in-rate.

Rate based congestion management for computer communication networks has been the focus of considerable research. We examine some of the fundamental properties of such end-to-end rate based algorithms. The first is the requirement for a policy to ensure the feasibility of the transmission rates by the sources. The feasibility implies that the capacity of any of the resources is not exceeded at any time. We show that a source policy which defers increases in the explicit rate for a period of time, while allowing decreases to take effect immediately maintains the requirement of feasibility. We also examine the convergence properties of the end-to-end rate based schemes in general, and give an upper bound on the convergence time of the scheme presented.

One of the challenges for rate based congestion management for computer communication networks is that of scalability. This is true especially in the context of ATM, when reducing the amount of state maintained and the computation performed can have significant advantages in making switches fast as well as low-cost. We explore ways of achieving better scalability of explicit rate algorithms for maxmin fair rate allocation in the presence of a large number of flows/virtual connections. We seek ways to reduce the complexity in both the amount of state that individual switches have to maintain, and the computation performed, by introducing the idea of discrete source rates. We develop algorithms with O(k) complexity of the calculation of fair allocation at the switch, where k is the number of distinct rates. We reduce the amount of state information to one bit per flow without performance degradation. We suggest a further approximation requiring only O(k) state information which performs quite well in our simulations, although it is shown to be vulnerable to certain conditions which may lead to somewhat lower link utilization.

End-to-end rate based congestion management has been extensively studied for control of computer communication networks. We examine some of the properties of end-to-end rate based algorithms for congestion management using explicit rate feedback, to achieve maxmin fair rate allocation in the network. We formulate a requirement on a switch's local rate allocation computation sufficient to ensure convergence of the distributed algorithm to the global maxmin fair rate allocation. We provide an upper bound on the convergence time when the switch algorithm satisfies this requirement. We propose a requirement of feasibility of the transmission rates, maintaining which results in oscillation-free operation of the algorithm while maintaining small queues at the switches. Feasibility implies that the capacity of any of the resources is not exceeded at any time. We suggest a source policy to ensure feasibility of transmission rates, the essence of which is to delay a rate increase by a certain time, while decreasing the transmission rate immediately upon receiving the appropriate feedback. Finally, we seek ways of improving scalability of the computation of maxmin fair rates in the presence of a large number of flows. We suggest using a discrete set of rates, which as we show, reduces the computational complexity and the state requirement at the switches.

Network speeds have been increasing rapidly. The higher bandwidth communication links available now, ranging from 100 Mbits/sec to Gbits/sec, present considerable potential for distributed applications. Processor speeds have also increased relentlessly. However, until now the ultimate throughput delivered to the user application has not increased as rapidly. The paper describes the implementation of the TCP/IP and UDP/IP protocol suite on Digital's Alpha AXP systems with the DEC OSF/1 operating system over FDDI. User applications are able to achieve almost the full FDDI bandwidth of 100 Mbits/sec, thus essentially eliminating the end-system as a bottleneck for network I/O bottleneck. Included in the TCP/IP implementation are extensions to TCP recently adopted by the IETF such as support for large transport windows for higher performance. This is particularly desirable for high speed networks and/or large delay networks. Incremental work for data movement and checksums are often the most expensive operations of protocol processing. These have been optimized to take advantage of the Alpha XP workstation architecture including 64 bit support, wider cache lines and the coherence of cache blocks with DMA. We show, via measurement results that TCP achieves a throughput of 95 Mbits/sec. We also show that UDP performance is comparable. In addition, and unlike typical BSD-derived systems, the UDP receive throughput to user applications is also maintained at high load.>

Ethernet at 10 Mb/s and other legacy LANs such as 4 and 16 Mb/s Token Ring have matured and proliferated widely over the last decade. Now, applications have started to exceed the capabilities of these low speed LANs. A new generation of high speed networks are being deployed to meet with increasing expectations of users. This paper compares four such high speed LANs which operate at or above 100 Mb/s: 100BASE-T, 100VG-AnyLAN, FDDI and ATM. The attributes and performance of these four LANs are described and contrasted. The implications of migration to these new technologies are discussed.>

We analyze the behavior of the Ethernet in networks with a small number of active stations, and describe what is known as the Ethernet capture effect. Where a station transmits consecutive packets exclusively for a prolonged period despite other stations contending for access. The capture effect causes transient unfairness, which results in substantial performance degradation. We report measurements using TCP/IP that show the performance degradation. A solution is proposed that effectively overcomes the capture effect. The proposed algorithm, which we call the capture avoidance binary exponential backoff (CABEB), uses the standard binary exponential backoff (BEB) with enhancements for collision resolution in the special case when a station attempts to capture the channel subsequent to an uninterrupted consecutive transmit. Using a detailed simulation, we show the efficacy of the CABEB algorithm over the standard BEB in overcoming the unfairness resulting from stations capturing the channel. The CABEB improves throughput for protocols like TCP/IP, reduces variability in the channel access latency and eliminates packet discards due to excessive collisions in a 2-node network. The algorithm is a modification that is compliant with the Ethernet/802.3 standards. For networks with a large number of active stations, the CABEB performs as well as the standard BEB algorithm. Our study placer emphasis on the workload and network configuration that is the worst case relative to the Ethernet capture effect to show that the proposed algorithm is a substantial improvement over the existing backoff algorithm.>

We address issues in determining FIFO sizes necessary for high-performance, in an integrated high-speed network interface, using a 100 Mbps Fast Ethernet controller as an example. A detailed analytical model is developed which accounts for system design choices, in addition to network parameters such as packet size and rate. The model yields insight into the impact of system parameters, such memory latency and maximum DMA transfer size, on the size of FIFOs required. The model also shows that the worst-case, in terms of receive-FIFO required, is not necessarily when back-to-back minimum size packets are received, but depends on the system parameters such as maximum DMA transfer size. We also study the possibility of FIFO underflows for the transmit direction.>

Abstract We study the use of non‐volatile memory for caching in distributed file systems. This provides an advantage over traditional distributed file systems in that the load is reduced at the server without making the data vulnerable to failures. We propose the use of a small non‐volatile cache for writes, at the client and the file server, together with a larger volatile read cache to keep the cost of the caches reasonable. We use a synthetic workload developed from analysis of file I/O traces from commercial production systems and use a detailed simulation of the distributed environment. The service times for the resources of the system were derived from measurements performed on a typical workstation. We show that non‐volatile write caches at the clients and the file server reduce the write response time and the load on the file server dramatically, thus improving the scalability of the system. We examine the comparative benefits of two alternative writeback policies for the non‐volatile write cache. We show that a proposed threshold based writeback policy is more effective than a periodic writeback policy under heavy load. We also investigate the effect of varying the write cache size and show that introducing a small non‐volatile cache at the client in conjunction with a moderate sized non‐volatile server write cache improves the write response time by a factor of four at all load levels.

The authors study the use of non-volatile memory for caching in distributed file systems. This provides an advantage over traditional distributed file systems in that the load is reduced at the server without making the data vulnerable to failures. They show that small non-volatile write caches at the clients and the server are quite effective. They reduce the write response time and the load on the file server dramatically, thus improving the scalability of the system. They show that a proposed threshold based writeback policy is more effective than a periodic writeback policy. They use a synthetic workload developed from analysis of file I/O traces from commercial production systems. The study is based on a detailed simulation of the distributed environment. The service times for the resources of the system were derived from measurements performed on a typical workstation.>

The I/O subsystem in a computer system is becoming the bottleneck as a result of recent dramatic improvements in processor speeds. Disk caches have been effective in closing this gap but the benefit is restricted to the read operations as the write I/Os are usually committed to disk to maintain consistency and to allow for crash recovery. As a result, write I/O traffic is becoming dominant and solutions to alleviate this problem are becoming increasingly important. A simple solution which can easily work with existing tile systems is to use non-volatile disk caches together with a write-behind strategy. In this study, we look at the issues around managing such a cache using a detailed trace driven simulation. Traces from three different commercial sites are used in the analysis of various policies for managing the write cache.We observe that even a simple write-behind policy for the write cache is effective in reducing the total number of writes by over 50%. We further observe that the use of hysteresis in the policy to purge the write cache, with two thresholds, yields substantial improvement over a single threshold scheme. The inclusion of a mechanism to piggyback blocks from the write cache with read miss I/Os further reduces the number of writes to only about 15% of the original total number of write operations. We compare two piggybacking options and also study the impact of varying the write cache size. We briefly looked at the case of a single non-volatile disk cache to estimate the performance impact of statically partitioning the cache for reads and writes.

Design issues that affect the performance of network input/output (I/O) are examined by analyzing the design and performance of a workstation's network interface to the 100-Mb/s FDDI token ring. Several design alternatives for partitioning functions between the network interface and the host software are evaluated. A simple model is proposed for looking at the performance of network I/O, and an effective analysis approach for predicting user-perceived throughput is demonstrated. The analysis reveals that, particularly for network interfaces that reside on an I/O bus, providing a DMA engine for data movement provides significant improvements in throughput. However, the designs for the receive and transmit sides are not necessarily symmetrical, and it is shown that host architecture considerations influence the design of each direction differently. The analysis is used to show the potential benefits of having all protocol functions on the network interface and also to point out the potential processing power needed on that network interface.>

The authors describe the implementation of the binary feedback congestion avoidance (CA) policies and report measurements of the CA scheme in an OSI testbed at MITRE. The goal is to experiment with network configurations and traffic loads varied and complex enough to allow realistic performance evaluation of CA. These measurement experiments present evidence that the congestion avoidance policy operates effectively with a variety of traffic loads and configurations. The conditions used for the measurement experiments with CA include the coexistence of traffic participating in the CA scheme with intermittent traffic that does not participate in CA; and use of CA with realistic traffic such as bulk data file transfer type traffic and remote login traffic. These results show that there is a dramatic reduction in the mean and standard deviation of the response time as well as the number of retransmissions for all classes of traffic, even when some of the sources of traffic do not participate in the CA policy. The behavior of the CA policies with bidirectional traffic was also studied.>

Improving the performance of the file system is becoming increasingly important to alleviate the effect of I/O bottlenecks in computer systems. To design changes to an existing file system or to architect a new file system it is important to understand current usage patterns. In this paper we analyze file I/O traces of several existing production computer sytems to understand file access behavior.

A syntax-oriented model for naming feature-based specification of services is provided. The model allows a service to evolve or reconfigure in functionality by adding and removing features and still coexist with its previous versions. The model's two aspects are examined. With this model for specifying services, name server functions may be factorized from service specific functions and implemented in a generic fashion in terms of parse and match operations and function invocations. This model can provide significant extension to such naming schemes as X.500 and the Universal Naming Protocol in supporting feature-based service interfaces.>

The fiber distributed data interface (FDDI) is a 100 Mb/s token ring which includes extensive reliability and robustness mechanisms to provide fault detection, isolation, monitoring, and recovery functions. The authors examine two well-known problems on token rings and their potential impact on FDDI. The two problems are no-owner frame (NOF) and duplicate tokens. FDDI effectively resolves these problems only when either the NOF or the duplicate token collides into a transmitting station. The impact of NOFs and duplicate tokens during the period from the occurrence of the NOF and the duplicate token until they are removed by a transmitting station can be severe. This paper presents a ring purger that transparently and continuously removes the NOFs and detects and removes duplicate tokens. A ring purger is a designated station on a ring that performs its normal functions as a station and, in addition, performs the removal of NOFs and duplicate tokens. A distributed election algorithm is used to elect a single station on a given ring to be the ring purger. The authors also present performance and robustness considerations used in the design of the ring purger algorithms. They study the performance effect of continuously purging the ring and show that the effect is negligible even in the worst case.>

A comprehensive analysis of file access behavior of several commercial production VAX/VMS environments, based on detailed I/O traces, is presented. The characterization focuses on file control operations (such as opens and closes) which are often expensive in both processing and I/O requirements. The motivation for this analysis is the need to design good data management algorithms for distributed file systems. These algorithms are particularly important for managing client caches. Most of the operations for managing client caches are performed on file opens and closes. The authors present quantitative data on the number of file opens and closes, their timing characteristics, and their declared intent to allow sharing or have exclusive access. The precise dynamic sharing of files is examined.>

A frame content independent stripping (FCIS) algorithm for token rings is described that can be used by source-routing bridges due to its simplicity and the fact that the resulting size of the fragments remaining on the ring is very small. The FCIS algorithm counts the number of outstanding frames that a station has transmitted but not yet stripped from the ring and in addition places a global delimiter such as a void frame at the end of the transmission of the information frames. The station then stops stripping when either the count goes to zero or when the void frame or token is received by the station. The algorithm is simple to implement and does not place any topological restrictions on the network. It also allows for interoperation with stations which do not implement the FCIS algorithm. With the FCIS algorithm, frames are stripped at the earliest possible moment. It is shown that the combination of both the count and void is necessary for an FCIS algorithm by demonstrating that the inclusion of either just the void or the count could result in considerable overstripping of frames on the ring due to errors on the ring. The void frame adds a total of 28 bytes of overhead to every transmission opportunity at a participating station.>

Token rings have the property that a station that transmits a frame on the ring is responsible for removing the frame after it has been delivered to the destination stations. The algorithm to perform the frame removal is called 'Frame Stripping'. Most existing algorithms strip frames based on their content. This is not always adequate. The need for a new algorithm arises from the fact that frames transmitted by a station need not have the station's own address as the source address for a variety of reasons - such as when a bridge transmits a frame or when another address is used as the source address by a station instead of its original station address. This paper discusses a new frame content independent stripping (FCIS) algorithm for token rings.

We propose a scheme for congestion avoidance in networks using a connectionless protocol at the network layer. The scheme uses a minimal amount of feedback from the network to the users, who adjust the amount of traffic allowed into the network. The routers in the network detect congestion and set a congestion-indication bit on packets flowing in the forward direction. The congestion indication is communicated back to the users through the transport-level acknowledgment. The scheme is distributed, adapts to the dynamic state of the network, converges to the optimal operating point, is quite simple to implement, and has low overhead. The scheme maintains fairness in service provided to multiple sources. This paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. We also address the performance of the scheme under transient changes in the network and pathological overload conditions.

The authors' premise is that applications have the ability to tolerate certain types of failures under certain situations. This may, in turn, relax the top-level constraint on failure recovery algorithms in the interprocess communication (IPC) layer and allow exploitation of the inherent tolerance of applications to failures in a systematic way to simplify failure recovery. Using this application-driven approach, the authors present a model of a distributed program based on the well-known client-server relationship among the processes of the program. The model reflects certain generic properties of the application layer that can be exploited by the IPC layer during failure recovery. New techniques as to how the properties can be used for recovery are outlined. The authors use remote procedure call and shared variable as communication abstractions for the client and the server processes in the program to communicate with one another. The simplicity and generality of the failure recovery renders the application-driven approach particularly useful in contemporary distributed systems which are large and heterogeneous and hence have complex failure modes.>

The authors consider the performance of alternative mass-storage services for a client-server-style distributed system. Some qualitative arguments are presented on the ramifications of implementations of mass-storage services at various levels of the storage semantics hierarchy. The authors concentrate, in particular, on contrasting disk and file services. The functionalities of disk and file services are distinguished by their primitive operations: individual disk-block access for the disk service, and individual file-block access for the file service. This difference results in different partitionings of the computation between the client and server, as well as different network communication requirements. To understand the ramifications of such differences between the services, the authors present performance estimates for basic disk and file services. Performance estimates for several design alternatives are presented.>

The partitioning of operating system services in a distributed system and its impact on performance are discussed. An examination is made of the tradeoff between partitioning a service at a higher layer, which could potentially result in greater computation at the server, and partitioning at a lower layer, which could result in less service-related computation at the server but may require more communication with the server. As a case study, the performance implications of providing alternate types of distributed mass storage services are considered, focusing on those partitionings that result in a file and a disk service. A detailed multiclass closed queuing network model of the remote service with users on workstations is used in the study.>

We propose a scheme for congestion avoidance in networks using a connectionless protocol at the network layer. The scheme uses feedback from the network to the users of the network. The interesting challenge for the scheme is to use a minimal amount of feedback (one bit in each packet) from the network to adjust the amount of traffic allowed into the network. The servers in the network detect congestion and set a congestion indication bit on packets flowing in the forward direction. The congestion indication is communicated back to the users through the transport level acknowledgement.

In this paper, we study the performance characteristics of a client-server style distributed system by a queueing network model. The system being modeled was based on an experimental distributed system currently being prototyped. As a specific detailed case study, we have evaluated the performance of a file server. A file server is a key component to achieve the data sharing necessary in a distributed system. The file server is probably the most heavily used resource of the distributed system and as a result, its performance is critical to the success of the system. The primary goal of the study was to identify and quantify the effects of the bottlenecks in the server and assess design alternatives to enhance performance. In particular, the improvements due to file caching, outboarding transport functions and the effect of multiple network interfaces were studied. We model in detail the various stages of processing of a request to a file server. Parameterization of the model was achieved by initial measurements made on the prototype system. An important characteristic modeled was the buffer overflow from the network interfaces. This required the approximate solution of a non-product-form queueing network.