Modern software development abstracts applications from the underlying infrastructure, enabling global-scale deployment with minimal concern about low-level networking details. However, when these infrastructure-agnostic software components need to communicate, they encounter significant networking limitations. This forces developers to either navigate complex, low-level networking constructs to achieve the desired connectivity or give up on truly flexible connectivity and limit their software to static connectivity patterns. In this paper, we focus on the evolving challenges of application connectivity in today's hyper-distributed reality. We propose to model connectivity around the notion of application services and have realized this proposal as ClusterLink, which exposes the app-level APIs for specifying communication policies at a very granular level and implements them efficiently. This paper shares details on ClusterLink design principles, APIs, architecture, and implementation, and shows that ClusterLink outperforms its closest competitor by 2.5x in throughput in a cloud-based experimental setting.

Enterprises often deploy their business applications in multiple clouds as well as in multiple traditional environments. This work focuses on the connectivity aspects of this new way of operating and consuming digital services. We define the related requirements, analyze the challenges, and present ClusterLink, our solution for interconnecting today's and future multi-cloud applications.

Flow-Logs Pipeline is an observability tool that consumes raw network flow-logs and transforms them from their original format (e.g. NetFlow or IPFIX) into numeric metrics format. FLP allows to define transformations of data to generate condensed metrics that encapsulate network domain knowledge.

Hash-based stateful load-balancers employ connection tracking to avoid per-connection-consistency (PCC) violations that lead to broken connections. In this paper, we propose Just Enough Tracking (JET), a new algorithmic framework that significantly reduces the size of the connection tracking tables for hash-based stateful load-balancers without increasing PCC violations.

Network slicing is an essential 5G innovation whereby the network is partitioned into logical segments, so that Communication Service Providers (CSPs) can offer differentiated services for verticals and use cases. In many 5G use cases, network requirements vary over time and CSPs must dynamically adapt network slices to satisfy the contractual network slice QoS, cooperating and using each others’ resources, e.g. when resources of a single CSP are not sufficient or suitable to maintain all it’s current SLAs. While this need for dynamic cross-CSP cooperation is widely recognized, realization of this need is not yet possible due to gaps both in business processes and in technical capabilities.In this paper, we present a 5GZORRO approach to dynamic cross-CSP slice scaling. Our approach both enables CSPs to collaborate, providing security and trust with smart multi-party contracts, and facilitates thus achieved collaboration to enable resource sharing across multiple administrative domains, either during slice establishment or when already existing slice needs to expand or shrink. Our approach allows automating both business and technical processes involved in dynamic lifecycle management of cross-CSP network slices, following ETSI’s Zero-Touch Network and Service Management (ZSM) closed-loop architecture, and relying on resource-sharing Marketplace, Distributed Ledger (DL), and Operational Data Lake. We show how this approach is realized in truly Cloud Naive way, with Kubernetes as both business and technical cross-domain orchestrator. We then showcase applicability of the proposed solution for dynamic scaling of Content Delivery Network (CDN) service.

Consistent hashing is a central building block in many networking applications, such as maintaining connection affinity of TCP flows. However, current consistent hashing solutions do not ensure full consistency under arbitrary changes or scale poorly in terms of memory footprint, update time and key lookup complexity. We present AnchorHash, a scalable and fully-consistent hashing algorithm. AnchorHash achieves high key lookup rate, low memory footprint and low update time. We formally establish its strong theoretical guarantees, and present an advanced implementation with a memory footprint of only a few bytes per resource. Moreover, evaluations indicate that AnchorHash scales on a single core to 100 million resources while still achieving a key lookup rate of more than 15 million keys per second.

Cloud computing gave rise to a Cloud-native[1] approach for operating application software in the cloud, whereby applications are segmented into micro-services that can be designed and deployed independently of each other. This significantly increases application maintainability, reduces time to market, and helps leveraging cloud computing model. On the other hand, this approach increases the system level complexity of the application and poses new challenges, such as how services discover each other, and how application handles individual service upgrades. To support cloud-native paradigm, new development, deployment, and orchestration tools are created. One of such tools is Istio [2] service mesh, built to connect, secure, control, and observe services. While immensely useful to application developers, Istio is an additional layer in cloud compute platform software stack and is thus prone to failure or misuse.

This research is done in the context of the SliceNet project [4] that aims to extend 5G infrastructure with cognitive management of cross-domain, cross-layer network slices [1], with emphasis on Quality of Experience (QoE) for vertical industries. The provisioning of network slices with proper QoE guarantees is seen as one of the key enablers of future 5G-enabled networks. The challenge is to assess the QoE experienced by the vertical application and its users without requiring the applications or the users to measure and report QoE related metrics back to the provider. To address this challenge, we propose a method for deriving application-level QoE from network-level Quality of Service (QoS) measurements, easily accessible by the provider. In particular, we describe a PoC where QoE, perceived by application users, is estimated from low level network monitoring data, by applying cognitive methods. Our main goal is enabling the cloud provider to support the desired E2E QoE-based Service Level Agreements (SLAs), e.g. by monitoring QoS metrics within the provider's domain to optimize resource allocation through provider's actuators. Additional benefit can be achieved by applying the same technique to troubleshoot issues in the provider's infrastructure. In this work, we employed classical statistical methods to assess the relationship between the application-level QoE and the network-level QoS.

Cloud computing technology enables uniform access to shared pools of configurable system resources and higher-level services, rapidly provisioned with minimal management effort. Cloud computing relies on sharing the resources to achieve coherence and economies of scale, through virtualizion. Cloud network, in particular, is virtualized through multiple logical constructs and SW layers, making cloud connectivity complex to configure, debug, and visualize. In this work, we show how to detect cloud network operational issues through monitoring and analytics, using and enhancing open source network analyzer, Skydive [2]. In particular, we focus on Noisy Neighbor Effect, a situation in which a common resource is monopolized by a noisy tenant, resulting in performance degradation experienced by other tenants.

No abstract available.

Operating a cloud-scale service is a huge challenge. There are millions of users worldwide and millions of requests per seconds. For example, Amazon's Simple Storage Service (S3) in 2013 contained two trillion objects and its logs contained 1.1 million log lines per second, which are approximately 10 PB of log records per year (see [1]). Cloud scale implies thousands of servers and network elements, and hundreds of services from multiple cross-regional data centers. Cloud service operation data is scattered over various types of semi-structured and unstructured logs (e.g., application, error, debug), telemetry and network data, as well as customer service records. It is therefore extremely difficult for the multiple owners and administrators in such systems, coming from different units of the organization, to follow the possible paths and system alternatives in order to detect problems, solve issues and understand the service operation.

Hybrid switching combines a high-bandwidth optical circuit switch in parallel with a low-bandwidth electronic packet switch. It presents an appealing solution for scaling datacenter architectures. Unfortunately, it does not fit many traffic patterns produced by typical datacenter applications, and in particular the skewed traffic patterns that involve highly intensive one-to-many and many-to-one communications.

Over the recent years we witness a massive growth of cloud usage, accelerated by new types of 'born-to-the-cloud' workloads. These new types of workloads are increasingly multi-component, dynamic and often present highly intensive communication patterns. Massive innovation of Data Center Network (DCN) technologies is required to support the demand, giving raise to new network topologies, new network control paradigms, and management models. One particularly promising technology candidate for improving the DCN efficiency is Optical Circuit Switching (OCS).

Seamless cloud interoperability is highly desired but not yet easily attainable in the current cloud solutions market. This work tackles one aspect of achieving cloud interoperability, namely, inter-cloud networking. We list the requirements and propose an inter-cloud networking architecture for a case of independent clouds owned by different entities and powered by different cloud management and network virtualization technologies. Then we validate the proposed architecture by describing an example of working implementation for Open Stack cloud powered by Open Daylight Open DOVE SDN solution. Finally, we compare our architecture to the existing solutions.

Network services, such as security, load-balancing, and monitoring, are an indisputable part of modern networking infrastructure and are traditionally realized as specialized appliances or middleboxes. Middleboxes complicate the management, the deployment, and the operations of the entire network. Moreover, they induce network performance issues and scalability limitations by requiring huge amounts of traffic to be, often sub-optimally redirected, and sometimes redundantly processed. Recent trends of server virtualization and Network Function Vir-tualization (NFV) exacerbate these scalability and performance issues. In this paper, we present EnforSDN — a new management approach that exploits SDN principles to decouple the policy resolution layer from the policy enforcement layer in network service appliances. Our approach improves the enforcement management, network utilization and communication latency, without compromising the policy and the functionality of the network. Using emulated SDN-based data center environment, we demonstrate higher throughput and lower latency achieved with EnforSDN, as compared to a baseline SDN network. In addition, we show that EnforSDN reduces the overall network appliances load, as well as the forwarding tables size.

Datacenter-based Cloud computing has induced new disruptive trends in networking, key among which is network virtualization. Software-Defined Networking overlays aim to improve the efficiency of the next generation multitenant datacenters. While early overlay prototypes are already available, they focus mainly on core functionality, with little being known yet about their impact on the system level performance. Using query completion time as our primary performance metric, we evaluate the overlay network impact on two representative datacenter workloads, Partition/Aggregate and 3-Tier. We measure how much performance is traded for overlay's benefits in manageability, security and policing. Finally, we aim to assist the datacenter architects by providing a detailed evaluation of the key overlay choices, all made possible by our accurate cross-layer hybrid/mesoscale simulation platform.

One of the prevalent trends in emerging large scale multi-tenant datacenters is network virtualization using overlays. Here we investigate application performance degradation in such an overlay applied to commodity 10 Gigabit Ethernet networks. We have adopted partition/aggregate as a representative commercial workload that today is deployed on bare metal servers and is notoriously sensitive to latency and TCP incast congestion. Using query completion time as the primary metric, we evaluate the degree to which a software-defined network (SDN) overlay impacts this application's behavior, the performance bounds of partition/aggregate with an SDN overlay, and whether active queue management (AQM) such as random early detection (RED) can benefit this environment. We introduce a generic SDN overlay framework, which we measure in hardware and simulate using a real TCP stack extracted from FreeBSD v9, running over a detailed Layer 2 commodity 10G Ethernet fabric network simulator. To further alleviate TCP incast congestion and support legacy congestion control, we propose an AQM translation scheme called v-RED. Finally, we report results concerning SDN's benefits in addressing TCP incast. Contrary to our expectations, we found that latency-sensitive applications do not necessarily suffer from performance degradation when deployed over SDN overlays.

The pervasiveness of multiprocessor and multicore hardware and the rising level of available parallelism are radically changing the computing landscape. Can software deal with tomorrow's potential higher parallelism? In this paper we study this issue from the garbage collection perspective. In particular, we investigate the scalability of parallel heap tracing, which stands at the core of the garbage collection activity. Heap shapes can be sequential in nature, and prevent the collector from scaling the trace. We start by proposing the idealized trace utilization as a scalability measure for evaluating the scalability of a given heap shape. We then examine standard Java benchmarks and evaluate the existence of non-scalable object-graph shapes in their execution. Next, we propose and implement a prototype of garbage collection techniques that attempt to ameliorate the object-graph shape problem. Finally, we measure and report their efficacy.

Multithreaded applications with multigigabyte heaps running on modern servers provide new challenges for garbage collection (GC). The challenges for “server-oriented” GC include: ensuring short pause times on a multigigabyte heap while minimizing throughput penalty, good scaling on multiprocessor hardware, and keeping the number of expensive multicycle fence instructions required by weak ordering to a minimum.We designed and implemented a collector facing these demands building on the mostly concurrent garbage collector proposed by Boehm et al. [1991]. Our collector incorporates new ideas into the original collector. We make it parallel and incremental; we employ concurrent low-priority background GC threads to take advantage of processor idle time; we propose novel algorithmic improvements to the basic mostly concurrent algorithm improving its efficiency and shortening its pause times; and finally, we use advanced techniques, such as a low-overhead work packet mechanism to enable full parallelism among the incremental and concurrent collecting threads and ensure load balancing.We compared the new collector to the mature, well-optimized, parallel, stop-the-world mark-sweep collector already in the IBM JVM. When allowed to run aggressively, using 72% of the CPU utilization during a short concurrent phase, our collector prototype reduces the maximum pause time from 161 ms to 46 ms while only losing 11.5% throughput when running the SPECjbb2000 benchmark on a 600-MB heap on an 8-way PowerPC 1.1-GHz processors. When the collector is limited to a nonintrusive operation using only 29% of the CPU utilization, the maximum pause time obtained is 79 ms and the loss in throughput is 15.4%.

The mostly concurrent garbage collection was presented in the seminal paper of Boehm et al. With the deployment of Java as a portable, secure and concurrent programming language, the mostly concurrent garbage collector turned out to be an excellent solution for Java's garbage collection task. The use of this collector is reported for several modern production Java Virtual Machines and it has been investigated further in academia.In this paper, we present a modification of the mostly concurrent collector, which improves the throughput, the memory footprint, and the cache behavior of the collector without foiling the other good qualities (such as short pauses and high scalability). We implemented our solution on the IBM production JVM and obtained a performance improvement of up to 26.7%, a reduction in the heap consumption by up to 13.4%, and no substantial change in the (short) pause times. The modified algorithm was subsequently incorporated into the IBM production JVM.

Java uses garbage collection (GC) for the automatic reclamation of computer memory no longer required by a running application. GC implementations for Java Virtual Machines (JVM) are typically designed for single processor machines, and do not necessarily perform well for a server program with many threads running on a multiprocessor. We designed and implemented an on-the-fly GC, based on the algorithm of Doligez, Leroy and Gonthier [13, 12] (DLG), for Java in this environment. An on-the-fly collector, a collector that does not stop the program threads, allows all processors to be utilized during collection and provides uniform response times. We extended and adapted DLG for Java (e.g., adding support for weak references) and for modern multiprocessors without sequential consistency, and added performance improvements (e.g., to keep track of the objects remaining to be traced). We compared the performance of our implementation with stop-the-world mark-sweep GC. Our measurements show th...