Mobile edge computing (MEC) integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. This paper investigates the task assignment problem for cooperative MEC networks in which a set of geographically distributed heterogeneous edge servers not only cooperate with remote cloud data centers but also help each other to jointly process user tasks. We introduce a novel stochastic MEC cooperation framework to model the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment optimization problem is formulated by taking into consideration dynamic network states, uncertain node computing capabilities and task arrivals, as well as the heterogeneity of the involved entities. We then develop and compare three task assignment algorithms, based on different deep reinforcement learning (DRL) approaches, value-based, policy-based, and hybrid approaches. In addition, to reduce the search space and computation complexity of the algorithms, we propose decomposition and function approximation techniques by leveraging the structure of the underlying problem. The evaluation results show that the proposed DRL-based task assignment schemes outperform the existing algorithms, and the hybrid actor-critic scheme performs the best under dynamic MEC network environments.

Cloud data centers are increasingly adopting the Software-Defined Networking (SDN) technologies for their underlying connection and communications. However, as a critical part of daily operations and management of such data centers, the network measurement is essential but has often been constrained by the available resources in the traditional network devices. Thus, how to properly balance the resource consumption while maintain timely and accurate measurement remains a challenge to data center systems. Recent advances in Software-Defined Networking (SDN) have enabled flexible and programmable network measurement, which is referred to as Software Defined Measurement (SDM). A promising trend for SDM is to conduct network traffic measurement on widely deployed Open vSwitches (OVS) in data centers. However, little attention has been paid to the design options for conducting traffic measurement on the OVS. In this study, we set to explore different designs and investigate the corresponding trade-offs among resource consumption, measurement accuracy, implementation complexity, and impact on switching speed. Through extensive experiments and comparisons, we quantitatively show the various trade-offs that the different schemes strike to balance, and demonstrate the feasibility of instrumenting OVS with monitoring capabilities. These results provide valuable insights into which design will best serve different measurement and monitoring needs.

Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, called Scotch , to enable the network to scale up its capability and handle the DDoS attack traffic. In our design, the congestion works as an indicator to trigger the mitigation mechanism. Scotch elastically scales up the control plane capacity by using an Open vSwitch-based overlay. Scotch takes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under abnormal (e.g., DDoS attacks) traffic surges. We have implemented a prototype and experimentally evaluated Scotch . Our experiments in the small-scale lab environment and large-scale GENI testbed demonstrate that Scotch can elastically scale up the control channel bandwidth upon attacks.

This paper investigates the task management for cooperative mobile edge computing (MEC), where a set of geographically distributed heterogeneous edge nodes not only cooperate with remote cloud data centers but also help each other to jointly process tasks and support real-time IoT applications at the edge of the network. Especially, we address the challenges in optimizing assignment of the tasks to the nodes under dynamic network environments when the task arrivals, node computing capabilities, and network states are nonstationary and unknown a priori. We propose a novel stochastic framework to model the interactions of the involved entities, including the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment problem is formulated and the algorithm is developed based on online reinforcement learning to optimize the performance for task processing while capturing various dynamics and heterogeneities of node computing capabilities and network conditions with no requirement for prior knowledge of them. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is proposed, which are incorporated with reinforcement learning to reduce the search space and computation complexity. The evaluation results demonstrate that the proposed online learning-based scheme outperforms the state-of-the-art benchmark algorithms.

Identifying heavy flows, i.e., flows with large packet counts during a pre-defined time window, is vital for many network applications. The task of real-time heavy flow detection in data plane is challenging due to high switching speed (100 Gbps), a large number of concurrent flows (millions of concurrent flows), and small memory footprint requirement. In this paper, we dissect the key factors that affect the existing detection scheme’s accuracy, and propose AccuPipe, a new detection scheme with intelligent flow entry replacement strategies. The simulation results show that the new scheme is able to efficiently utilize all flow entries in the detection pipeline, and detects more than 850 heavy flows (out of top 1,000) using a small amount of memory (1,000 flow entries, roughly equivalently to 18KB memory) with reasonable reporting overhead. This represents a 76% improvement over HashPIPE scheme, which detects on average 484 heavy flows (out of top 1,000) in the same setting. In addition, we investigate the performance of different flow entry replacement strategies, and report their pros and cons.

We consider the auto-scaling problem for application hosting in a cloud, where applications are elastic and the number of requests changes over time. The application requests are serviced by Virtual Machines (VMs), which reside on Physical Machines (PMs) in a cloud. We aim to minimize the number of hosting PMs by intelligently packing VMs into PMs, while the VMs are auto-scaled, i.e., dynamically acquired and released, to accommodate varying application needs. We consider a shadow routing based approach for this problem. The proposed shadow algorithm employs a specially constructed virtual queueing system to dynamically produce an optimal solution that guides the VM auto-scaling and the VM-to-PM packing. The proposed algorithm runs continuously without the need to re-solve the underlying optimization problem “from scratch”, and adapts automatically to the changes in the application demands. We prove the asymptotic optimality of the shadow algorithm. The simulation experiments further demonstrate the algorithm's good performance and high adaptivity.

Edge computing is burgeoning along with the rapidly increasing adoption of the Internet of Things (IoT). While there are studies on various aspects of edge computing, we find there is a lack of network perspective. In this paper, we, thus, first present an overview of how software-defined networking (SDN) and related technologies are being investigated in edge computing. Our purpose is to survey the state of the art and discuss the potential (remaining) challenges for future research. For this, we survey how SDN and related technologies are integrated to facilitate the management and operations of edge servers and various IoT devices. For the former, we review how SDN has been utilized in the access network, the core network, and the wide area network (WAN) between the edge and the cloud. For the latter, we focus on how SDN is leveraged to provide unified and programmable interfaces to manage devices. Through our discussion, we suggest that the SDN-related network support for edge computing deserves more in-depth investigations. We also identify several challenges and open issues to be addressed in the future.

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the min-max-DC-load problem. Namely, we propose a combined VM-to-DC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.

While being critical to the network management, the current state of the art in network measurement is inadequate, providing surprisingly little visibility into detailed network behaviors and often requiring high level of manual intervention to operate. Such a practice becomes increasingly ineffective as the networks grow both in size and complexity. In this paper, we propose vPROM, a vSwitch enhanced SDN programmable measurement framework that automates the measurement process, minimizes the measurement resource usage, and addresses several significant technical challenges faced by early works. vPROM leverages the SDN programmability and extends the Pyretic runtime system and OpenFlow network interface to achieve the measurement automation. The required measurement resources are minimized by only acquiring the necessary statistics, made possible with instrumented Open vSwitches1with user defined monitoring capability. By decoupling monitoring from routing, vPROM reduces the interference between the measurement applications and other applications, and eliminates the frequent involvement of the controller. A vPROM prototype is implemented with DDoS and port-scan detection applications. The performance of vPROM is evaluated and the comparison results with other existing programmable measurement approaches are also presented.

We study how to provide fine-grained, flexible traffic monitoring in the Open vSwitch (OVS). We argue that the existing OVS monitoring tools are neither flexible nor sufficient for supporting many monitoring applications. We propose UMON, a mechanism that decouples monitoring from forwarding, and offers flexible and fine-grained traffic stats. We describe a prototype implementation of UMON that integrates well with the OVS architecture. Finally, we evaluate the performance using the prototype, and illustrate UMON's efficiency with the example use cases such as detecting port scans.

Netflix and Hulu are leading Over-the-Top (OTT) content service providers in the US and Canada. Netflix alone accounts for 29.7% of the peak downstream traffic in the US in 2011. Understanding the system architectures and performance of Netflix and Hulu can shed light on the design of such large-scale video streaming platforms, and help improving the design of future systems. In this paper, we perform extensive measurement study to uncover their architectures and service strategies. Netflix and Hulu bear many similarities. Both Netflix and Hulu video streaming platforms rely heavily on the third-party infrastructures, with Netflix migrating that majority of its functions to the Amazon cloud, while Hulu hosts its services out of Akamai. Both service providers employ the same set of three content distribution networks (CDNs) in delivering the video contents. Using active measurement study, we dissect several key aspects of OTT streaming platforms of Netflix and Hulu, e.g., employed streaming protocols, CDN selection strategy, user experience reporting, etc. We discover that both platforms assign the CDN to a video request without considering the network conditions and optimizing the user-perceived video quality. We further conduct the performance measurement studies of the three CDNs employed by Netflix and Hulu. We show that the available bandwidths on all three CDNs vary significantly over the time and over the geographic locations. We propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy that can significantly increase users' average available bandwidth.

Software Defined Networks use logically centralized control due to its benefits in maintaining a global network view and in simplifying programmability. However, the use of centralized controllers can affect network performance if the control path between the switches and their associated controllers becomes a bottleneck. We find from measurements that the software control agents on some of the switches have very limited throughput. This can cause performance degradation if the switch has to handle a high traffic load, as for instance due to flash crowds or DDoS attacks. This degradation can occur even when the data plane capacity is under-utilized. The goal of our paper is to design new mechanisms to enable the network to scale up its ability to handle high control traffic loads. For this purpose, we design, implement, and experimentally evaluate Scotch, a solution that elastically scales up the control plane capacity by using a vSwitch based overlay. Scotch takes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under normal (e.g., flash crowds) or abnormal (e.g., DDoS attacks) traffic surge.

We develop shadow routing based online algorithms for the joint problem of application-to-VM and VM-to-PM assignments in a cloud environment. The asymptotic optimality of the shadow algorithm is proved and the performance is evaluated by simulations.

On-path caching has been proved to be effective in shortening the content retrieval time and educing the traffic workload imposed on the underlying network. In this paper, we study the effectiveness of on-path caching for the content distribution in the wireless network environment with GPSR as the underlying routing protocol. We extend the general cache network model introduced in [14] to account for GPSR's loopy paths. We further develop the performance models to take into account the effect of wireless link loss, different error recovery strategies, and the GPSR routing induced long and loopy paths. The performance models allow us to assess the average content availability, the average number of messages per content retrieval, and the average content retrieval latencies for an on-path caching enabled content distribution network over MANETs. The results show that on-path caching can significantly improve the content availability, reduce the average number of required messages to acquire a content object, and shorten the content retrieval latency. In addition, the study shows that the on-path caching is able to mitigate the negative impact caused by the long and loopy paths due to the GPSR routing.

As wireless nodes become more powerful, a large volume of diverse content, such as images or videos, become available in a mobile ad hoc network (MANET). It follows that a MANET can largely benefit from the support for "range queries", i.e., a query for content whose attributes are within a requested range. Supporting range queries over MANET is challenging due to node mobility and adverse medium conditions. In this work, we argue that Information- Centric Networking (ICN), a novel networking paradigm that decouples resources from the host they reside on, can enable efficient support for range queries in MANET. Accordingly, we design and evaluate ERMINE, a solution for "Enabling Range queries in MANET with Information-centric NEtworking".

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.

In this paper, we propose a Bayesian-inference-based recommendation system for online social networks. In our system, users share their content ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a content rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. We further propose to use Prior distribution to cope with cold start and rating sparseness. The proposed algorithm is evaluated using two different online rating data sets of real users. We show that the proposed Bayesian-inference-based recommendation is better than the existing trust-based recommendations and is comparable to Collaborative Filtering (CF) recommendation. It allows the flexible tradeoffs between recommendation quality and recommendation quantity. We further show that informative Prior distribution is indeed helpful to overcome cold start and rating sparseness.

Netflix is the leading provider of on-demand Internet video streaming in the US and Canada, accounting for 29.7% of the peak downstream traffic in US. Understanding the Netflix architecture and its performance can shed light on how to best optimize its design as well as on the design of similar on-demand streaming services. In this paper, we perform a measurement study of Netflix to uncover its architecture and service strategy. We find that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution. We also perform active measurements of the three CDNs employed by Netflix to quantify the video delivery bandwidth available to users across the US. Finally, as improvements to Netflix's current CDN assignment strategy, we propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy, and demonstrate their potentials in significantly increasing user's average bandwidth.

HTTP adaptive streaming (HAS) is quickly becoming a popular mechanism for delivering on-demand video content over the Internet. The chunked transmission and application-layer adaptation create a very different traffic pattern than traditional progressive video downloads where the entire video is downloaded with a single request.

Recommendation accuracy can be improved by incorporating trust relationships derived from social networks. Most recent work on social network based recommendation is focused on minimizing the root mean square error (RMSE). Social network based top-k recommendation, which recommends to a user a small number of items at a time, is not well studied. In this paper, we conduct a comprehensive study on improving the accuracy of top-k recommendation using social networks. We first show that the existing social-trust enhanced Matrix Factorization (MF) models can be tailored for top-k recommendation by including observed and missing ratings in their training objective functions. We also propose a Nearest Neighbor (NN) based top-k recommendation method that combines users' neighborhoods in the trust network with their neighborhoods in the latent feature space. Experimental results on two publicly available datasets show that social networks can significantly improve the top-k hit ratio, especially for cold start users. Surprisingly, we also found that the technical approach for combining feedback data (e.g. ratings) with social network information that works best for minimizing RMSE works poorly for maximizing the hit ratio, and vice versa.

Delivery of high-quality streaming services over multi-hop wireless mesh networks (WMNs) is a challenging research problem because of quality fluctuation and interference of wireless links in WMNs, as well as strict throughput, delay, and reliability requirements of streaming applications. In this paper, we propose a Network Assisted Peer-to-Peer (NAP2P) system for file-based media streaming services such as video-on-demand in WMNs. In NAP2P, mesh routers dynamically cache content and form a P2P network with end user devices. This architecture enables several efficient and scalable communication mechanisms, for example, automatic caching and multi-source multi-path streaming for optimizing system performance. We address the design issues in NAP2P. Especially we design a multi-source multi-path routing mechanism to meet the QoS requirements of streaming sessions. A mathematical formulation for such source selection and routing optimization problem is presented, which determines the optimal content sources and streaming paths for multiple concurrent flows in the presence of wireless interference and subject to flow QoS constraints. By leveraging the unique architecture of NAP2P, we investigate the performance gain by applying mesh cache routers as network peers in P2P networks, as well as the benefits of allowing end user devices to provide content to their peers.

In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.

P2P streaming applications are very popular on the Internet today. However, a mobile device in P2P streaming not only needs to continuously receive streaming data from other peers for its playback, but also needs to continuously exchange control information (e.g., buffermaps and file chunk requests) with neighboring peers and upload the downloaded streaming data to them. These lead to excessive battery power consumption on the mobile device.

Recent advances in scalable video coding (SVC) make it possible for users to receive the same video with different qualities. To adopt SVC in P2P streaming, two key design questions need to be answered: 1) layer subscription: how many layers each peer should receive, and 2) layer scheduling: how to deliver to peers the layers they subscribed. From the system point of view, the most efficient solution is to maximize the aggregate video quality on all peers, i.e., the social welfare. From individual peer point of view, the solution should be fair. Fairness in P2P streaming should additionally take into account peer contributions to make the solution incentive-compatible. In this paper, we first develop utility maximization models to understand the interplay between efficiency, fairness and incentive in layered P2P streaming. We show that taxation mechanisms can be devised to strike the right balance between social welfare and individual peer welfare. We then develop practical taxation-based P2P layered streaming designs, including layer subscription strategy, chunk scheduling policy, and mesh topology adaptation. Extensive trace-driven simulations show that the proposed designs can effectively drive layered P2P streaming systems to converge to the desired operating points in a distributed fashion.

Mobile video broadcasting service, or mobile TV, is expected to become a popular application for 3G wireless network operators. Most existing solutions for video Broadcast Multicast Services (BCMCS) in 3G networks employ a single transmission rate to cover all viewers. The system-wide video quality of the cell is therefore throttled by a few viewers close to the boundary, and is far from reaching the social-optimum allowed by the radio resources available at the base station. In this paper, we propose a novel scalable video broadcast/multicast solution, SV-BCMCS, that efficiently integrates scalable video coding, 3G broadcast, and ad-hoc forwarding to balance the system-wide and worst-case video quality of all viewers at 3G cell. We solve the optimal resource allocation problem in SV-BCMCS and develop practical helper discovery and relay routing algorithms. Moreover, we analytically study the gain of using ad-hoc relay, in terms of users' effective distance to the base station. Through extensive real video sequence driven simulations, we show that SV-BCMCS significantly improves the system-wide perceived video quality. The users' average PSNR increases by as much as 1.70 dB with slight quality degradation for the few users close to the 3G cell boundary.

Recent advance in scalable video coding (SVC) makes it possible for users to receive the same video with different qualities. To adopt SVC in P2P streaming, two key design questions need to be answered: 1) layer subscription: how many layers each peer should receive? 2) layer scheduling: how to deliver to peers the layers they subscribed? From the system point of view, the most efficient solution is to maximize the aggregate video quality on all peers, i.e., the social welfare. From individual peer point of view, the solution should be fair. Fairness in P2P streaming should additionally take into account peer contributions to make the solution incentive-compatible. In this paper, we show that taxation mechanisms can be devised to strike the right balance between social welfare and individual peers' welfare. We develop practical taxation-based P2P layered streaming designs, including layer subscription strategy, chunk scheduling policy, and mesh topology adaptation. Extensive trace-driven simulations show that the proposed designs can effectively drive layered P2P streaming systems to converge to the desired operating points in a distributed fashion.

Mobile video broadcasting service, or mobile TV, is a promising application for 3G wireless network operators. Most existing solutions for video broadcast/multicast services in 3G networks employ a single transmission rate to cover all viewers. The system-wide video quality of the cell is therefore throttled by a few viewers close to the boundary, and is far from reaching the social-optimum allowed by the radio resources available at the base station. In this paper, we propose a novel scalable video broadcast/multicast solution, SV-BCMCS, that efficiently integrates scalable video coding, 3G broadcast and adhoc forwarding to balance the system-wide and worst-case video quality of all viewers in a 3G cell. We study the optimal resource allocation problem in SV-BCMCS and develop practical helper discovery and relay routing algorithms. Through analysis and extensive OPNET simulations, we demonstrate that SV-BCMCS can significantly improve the system-wide video quality at the price of slight quality degradation of a few viewers close to the boundary.

Multi-hop wireless mesh networks (WMNs) are emerging as a promising technology with applications in last mile Internet access, public safety, disaster response, battlefield, etc. The unique characteristics of WMNs such as limited and dynamic bandwidth, error prone nature, interferences, and user mobility, however, pose a number of significant challenges for high quality video streaming. In this paper, we discuss the major challenges, review some general approaches, and present a Unified Cache and Peer-to-Peer (UNICAP) framework we developed recently to support high quality streaming over WMNs. Issues addressed in UNICAP include the architecture, cache server/peer/route selection with cross-layer optimization, and admission control. Some future research directions are also discussed.

Peer-to-peer (P2P) technology has recently been employed to deliver large scale video multicast services on the Internet. Considerable efforts have been made by both academia and industry on P2P streaming design. While academia mostly focus on exploring design space to approach the theoretical performance bounds, our recent measurement study on several commercial P2P streaming systems indicates that they are able to deliver good user quality of experience with seemingly simple designs. One intriguing question remains:how elaborate should a good P2P video streaming design be?Towards answering this question, we developed and implemented several representative P2P streaming designs, ranging from theoretically proved optimal designs to straightforward “naive” designs. Through an extensive comparison study on PlanetLab, we unveil several key factors contributing to the successes of simple P2P streaming designs, including system resource index, server capacity and chunk scheduling rule, peer download buffering and peering degree. We also identify regions where naive designs are inadequate and more elaborate designs can improve things considerably. Our study not only brings us better understandings and more insights into the operation of existing systems, it also sheds lights on the design of future systems that can achieve a good balance between the performance and the complexity.

Supporting user interactivity such as VCR operations is desirable however it introduces extra complexity and overhead into the VoD system. VCR operations such as random seek, rewind, and fast forward change the location of viewing point, the video playback direction, and the video playback speed. In traditional client-server service model, the streaming server adjusts the streaming schedule on the fly and allocates extra server bandwidth to accomodate these interactive operations. In P2P VoD system, an overlay is established among server and users/peers. Peers pull the video content from the server and neighboring peers. The change of viewing location, direction, and speed not only affects the server but also other peers. The neighboring peers may not have the data at the new playback point, or sufficient uplink capacity to support faster playback rate. The available data at the requesting peer is changed due to VCR operations, which may affect neighboring peers' data downloading. In this paper, we investigate the feasibility of supporting VCR operations in PONDER, a mesh-based P2P VoD system. PONDER employs a dual approach that incorporates the mesh-based p2p downloading into the unicast-based VoD. To accommodate VCR operations, PONDER dynamically adjusts the downloading priority based on the interactivity requirements. The measurement-based admission control admits as many users as possible yet achieves good performance level in face of users' interactivities. Initial results show that PONDER can support VCR operations without degrading users' viewing quality.

In this paper, we present UPAC, a unified peer-to-peer (P2P) and cache framework for high quality video-on-demand services over infrastructure multi-hop wireless mesh networks. Streaming video in multi-hop wireless networks faces many challenges, e.g., the varying available path bandwidth, interference due to shared medium, the impact of multiple relay nodes, etc. To increase the capacity of streaming services and ensure high video quality, in UPAC, the video content is cached at selected wireless mesh access points (MAPs) inside the mesh network. Furthermore peers help each other on video downloading in a best effort manner to reduce the workload imposed on the servers and networks. This unified framework has the advantages of both the content distribution network approach and peer-to-peer network approach. To obtain optimal video quality, a user device can form the P2P relationship with MAP content cache servers and other user devices. Meanwhile, the client-server relationship is established between the device and the MAP content cache servers. In addition, we propose and compare several methods to select the content cache servers for serving a device and to establish the end-to-end routes between the device and the selected servers. Our preliminary simulation results show that the proposed framework increases the number of users that can be concurrently served in the infrastructure wireless mesh network and improves the received video quality.

Peer-to-Peer (P2P) technology has recently been employed to deliver large scale video multicast services on the Internet. Considerable efforts have been made by both academia and industry on P2P streaming design. While academia mostly focus on exploring design space to approach the theoretical performance bounds, our recent measurement study on several commercial P2P streaming systems indicates that they are able to deliver good user Quality of Experience with seemingly simple designs. One intriguing question remains: how elaborate should a good P2P video streaming design be? Towards answering this question, we developed and implemented several representative P2P streaming designs, ranging from theoretically proved optimal designs to straight forward "naive" designs. Through an extensive comparison study on PlanetLab, we unveil several key factors contributing to the successes of simple P2P streaming designs, including system resource index, sever capacity and chunk scheduling rule, peer download buffering and peering degree. We also identify regions where naive designs are inadequate and more elaborate designs can improve things considerably. Our study not only brings us better understandings and more insights into the operation of existing systems, it also sheds lights on the design of future systems that can achieve a good balance between the performance and the complexity.

Peer-to-peer video streaming has been gaining popularity. However, it is still challenging to manage a P2P system efficiently to support high video playback rate. In this paper, we propose HCPS - Hierarchically Clustered P2P Streaming system that can support a streaming rate approaching the optimum upper bound with short delay, yet is simple enough to be implemented in practice. In HCPS, the peers are grouped into clusters and a hierarchy is formed among clusters to retrieve video data from the source server. By actively balancing the uploading capacities among clusters, and executing the perfect scheduling algorithm [1] within each cluster, the system resource can be fully utilized. The simulation experiments driven by the traces collected from a real P2P streaming system demonstrate the effectiveness of HCPS.

There is increasing interest in concurrent passive monitoring of IP flows at multiple locations within an IP network. The common objective of such a distributed monitoring system is to sample packets belonging to a large fraction of IP flows in a cost-effective manner by carefully placing monitors and controlling their sampling rates. In this paper, we consider the problem of where to place monitors within the network and how to control their sampling. To address the tradeoff between monitoring cost and monitoring coverage, we consider minimum cost and maximum coverage problems under various budget constraints. We show that all of the defined problems are NP-hard. We propose greedy heuristics, and show that the heuristics provide solutions quite close to the optimal solutions through experiments using synthetic and real network topologies. In addition, our experiments show that a small number of monitors is often enough to monitor most of the traffic in an entire IP network.

In this paper, we compare two basic approaches towards providing peer-to-peer file-sharing (or more generally, information search) in mobile ad-hoc networks (MANET). The flooding approach broadcasts a query (e.g., to locate a node holding a given file) to all network nodes. The index-server approach adds additional servers (known as index servers) that cache directory information about which nodes have which files. With index servers, a node wishing to locate a file first queries its local index server, which then queries other index servers, as needed. The use of index servers presents the possibility of locating a file index quickly in an index server cache, but requires additional overhead to maintain cache consistency. We compare the performance of the flooding approach to two index-server caching approaches: consistent caching and local caching. We quantify the reduction in search overhead using the index-server scheme rather than flooding in MANET, and study how the optimal number of index servers varies according to network size, query rate, and index generation rate. We compare the flooding scheme and the consistent caching and local caching schemes, for two types of queries: history queries and latest queries. Numerical results show how one can choose between the alternatives of consistent caching and local caching depending on network size, index generation rate and query rate.

We present the design, implementation, and performance evaluation of AMPS --- a flexible, scalable proxy testbed that supports a wide and extensible set of next-generation proxy streaming services. AMPS employs a modular architecture and is built on top of a commodity Linux system. We study the performance of AMPS proxy using a server-proxy-client configuration in a switched-Gigabit LAN environment. We identify the CPU to be the system bottleneck. Through profiling study, we further identify the kernel network protocol processing and the Network Reception Module inside the proxy to be the most CPU-intensive components. We also quantify the maximum achievable throughput for two of the principal components of the proxy - the control plane and data plane, and characterize the end-to-end performance along the server-to-proxy-to-client path. We discuss lessons learned and the various optimizations made in the course of our study to improve system performance.

The high-bandwidth requirements and long-lived characteristics of digital video make transmission bandwidth usage a key limiting factor in the widespread streaming of such content over the Internet. A challenging problem is to develop bandwidth-efficient techniques for delivering popular videos to a large, asynchronous client population with time-varying demand characteristics. In this paper, we propose smooth workload adaptive broadcast to address the above issues. A key component of our scheme is Flexible Periodic Broadcast (FPB). By introducing a feedback control loop into FPB, and enhancing FPB using techniques such as parsimonious transmission, smooth workload adaptive broadcast provides instantaneous or near-instantaneous playback services and can smoothly adapt to workload changes. Furthermore, FPB, as proposed in this paper, is bandwidth efficient and exhibits the periodic smooth channel transition property.

Providing on-demand video streaming service over the Internet is a challenging task. In this paper, we propose DirectStream, a directory based peer-to-peer video streaming service that efficiently and cost-effectively provides video on-demand service with VCR operation support. We analytically and experimentally examine the system performance, and show that the proposed scheme can significantly reduce the workload posed on the server, and that it scales extremely well as the popularity of the video increases even if participating clients behave non-cooperatively. We propose a QoS parent selection algorithm to construct the appropriate peer-to-peer networks, and discuss how to provide continuous playback in the face of clients' early departures. Our study suggests that peer-to-peer networking is a promising technique to address scalability in on-demand streaming service.

Providing video on demand (VoD) service over the Internet in a scalable way is a challenging problem. In this paper, we propose P2Cast - an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key technical issues in P2Cast: (1) constructing an application overlay appropriate for streaming; and (2) providing continuous stream playback (without glitches) in the face of disruption from an early departing client. Our simulation experiments show that P2Cast can serve many more clients than traditional client-server unicast service, and that it generally out-performs multicast-based patching if clients can cache more than of a stream's initial portion. We handle disruptions by delaying the start of playback and applying the shifted forwarding technique. A threshold on the length of time during which arriving clients are served in a single session in P2Cast serves as a knob to adjust the balance between the scalability and the clients' viewing quality in P2Cast.

The bandwidth-intensive and long-lived nature of high quality digital video makes it a challenging problem to transmit such video over the Internet. In this paper, we propose a scalable and flexible framework integrating proxy-based prefix caching with periodic broadcast of the suffix of a video from the server, for efficiently streaming a set of popular videos to a large number of asynchronous clients. We develop a methodology for (i) determining appropriate prefix and suffix transmission schemes based on a principle of decoupling the two transmissions from each other, and (ii) optimally allocating the proxy buffer space among the set of videos. A buffer allocation algorithm is presented that minimizes the aggregate bandwidth usage on the server-proxy path. Our studies show that our approach yields a buffer allocation close to the optimal solution minimizing both server-proxy and proxy-client path bandwidth usage for practical settings where the proxy-client path bandwidth is much cheaper than the long-haul server-proxy path bandwidth. When the proxy buffer is allocated to a set of videos using our scheme, a total buffer space of just 5-20% of the video repository is adequate to realize substantial reductions in the aggregate bandwidth usage on the server-proxy path.

Network performance evaluation through traditional packet-level simulation is becoming increasingly difficult as today's networks grow in scale along many dimensions. As a consequence, fluid simulation has been proposed to cope with the size and complexity of such systems. This study focuses on analyzing and comparing the relative efficiencies of fluid simulation and packet-level simulation for several network scenarios. We use the "simulation event" rate to measure the computational effort of the simulators and show that this measure is both adequate and accurate. For some scenarios, we derive analytical results for the simulation event rate and identify the major factors that contribute to the simulation event rate. Among these factors, the "ripple effect" is very important since it can significantly increase the fluid simulation event rate. For a tandem queueing system, we identify the boundary condition to establish regions where one simulation paradigm is more efficient than the other. Flow aggregation is considered as a technique to reduce the impact of the "ripple effect" in fluid simulation. We also show that WFQ scheduling discipline can limit the "ripple effect", making fluid simulation particularly well suited for WFQ models. Our results show that tradeoffs between parameters of a network model determines the most efficient simulation approach.

Data communication networks have been experiencing tremendous growth in size, complexity, and heterogeneity over the last decade. This trend poses a significant challenge to the design of scalable performance evaluation methodologies. In this paper we propose time-stepped hybrid simulation (TSHS) to deal with the scalability issue faced by traditional packet-level discrete event simulation methods. TSHS is a framework that offers the user the flexibility to choose the simulation time scale so as to trade off the computational cost of the simulation with its fidelity. Simulation speedup is achieved by evaluating the system at coarser time-scales. The potential loss of simulation accuracy when fine-time-scale behavior is evaluated at a coarser time-scale is studied both analytically and experimentally.