1,309 research outputs found

    Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks

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    We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against H-infinity optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and H-infinity optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure

    Incast mitigation in a data center storage cluster through a dynamic fair-share buffer policy

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    Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission—resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern—Incast—in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device

    Scalable Bandwidth Management in Software-Defined Networks

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    There has been a growing demand to manage bandwidth as the network traffic increases. Network applications such as real time video streaming, voice over IP and video conferencing in IP networks has risen rapidly over the recently and is projected to continue in the future. These applications consume a lot of bandwidth resulting in increasing pressure on the networks. In dealing with such challenges, modern networks must be designed to be application sensitive and be able to offer Quality of Service (QoS) based on application requirements. Network paradigms such as Software Defined Networking (SDN) allows for direct network programmability to change the network behavior to suit the application needs in order to provide solutions to the challenge. In this dissertation, the objective is to research if SDN can provide scalable QoS requirements to a set of dynamic traffic flows. Methods are implemented to attain scalable bandwidth management to provide high QoS with SDN. Differentiated Services Code Point (DSCP) values and DSCP remarking with Meters are used to implement high QoS requirements such that bandwidth guarantee is provided to a selected set of traffic flows. The theoretical methodology is implemented for achieving QoS, experiments are conducted to validate and illustrate that QoS can be implemented in SDN, but it is unable to implement High QoS due to the lack of implementation for Meters with DSCP remarking. The research work presented in this dissertation aims at the identification and addressing the critical aspects related to the SDN based QoS provisioning using flow aggregation techniques. Several tests and demonstrations will be conducted by utilizing virtualization methods. The tests are aimed at supporting the proposed ideas and aims at creating an improved understanding of the practical SDN use cases and the challenges that emerge in virtualized environments. DiffServ Assured Forwarding is chosen as a QoS architecture for implementation. The bandwidth management scalability in SDN is proved based on throughput analysis by considering two conditions i.e 1) Per-flow QoS operation and 2) QoS by using DiffServ operation in the SDN environment with Ryu controller. The result shows that better performance QoS and bandwidth management is achieved using the QoS by DiffServ operation in SDN rather than the per-flow QoS operation

    NUMFabric: Fast and Flexible Bandwidth Allocation in Datacenters

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    We present xFabric, a novel datacenter transport design that provides flexible and fast bandwidth allocation control. xFabric is flexible: it enables operators to specify how bandwidth is allocated amongst contending flows to optimize for different service-level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc. xFabric is also very fast, it converges to the specified allocation one-to-two order of magnitudes faster than prior schemes. Underlying xFabric, is a novel distributed algorithm that uses in-network packet scheduling to rapidly solve general network utility maximization problems for bandwidth allocation. We evaluate xFabric using realistic datacenter topologies and highly dynamic workloads and show that it is able to provide flexibility and fast convergence in such stressful environments.Google Faculty Research Awar
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