1 / 21

On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks

On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks. Meng ZHANG with Yongqiang XIONG, Qian ZHANG, Shiqiang YANG Globecom 2006. Outline. Background Related Work Problem Statement and Formulation Global Optimal Solution Distributed Algorithm Performance Evaluation

kuper
Download Presentation

On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. On the Optimal Scheduling for Media Streaming in Data-driven Overlay Networks Meng ZHANG with Yongqiang XIONG, Qian ZHANG, Shiqiang YANG Globecom 2006

  2. Outline • Background • Related Work • Problem Statement and Formulation • Global Optimal Solution • Distributed Algorithm • Performance Evaluation • Conclusion & Future Work

  3. Background • The Internet has witnessed a rapid growth in deployment of data-driven (swarming based) overlay/peer-to-peer network based IPTV systems during recent years. • These products are based on data-driven protocol • Facts of concurrent online users • GridMedia: over 230,000, rate 310kbps (achieved by one server) (developed by our lab) • PPLive: 500,000, rate 300-500kbps • QQLive: 1,460,000, rate 300-500kbps (not one server)

  4. Background - Data-Driven Protocol Review • Aiming to enable large-scale live broadcasting in the Internet environment • Very simple and very similar to that of Bit-Torrent • Two steps in data-driven protocol • The overlay construction • The block scheduling

  5. Background - Data-Driven Protocol Review • The second step – block scheduling • The streaming is divided into blocks • Each node has a sliding window containing all the blocks it is interested in currently • The first step – overlay construction • All the nodes self-organize into a random graph I have block 1,2,3 Request block 3 Send block 3 I have block 1,2,4 Request block 4 Send block 4 I have block 1,2 Request block 1 Send block 1 I have block 2,3 Request block 2 Send block 2

  6. Related Work • To improve data-driven protocol, most recent efforts focus on optimizing overlay construction (i.e. the first step ): • Vishnumurthy & Francis (INFOCOM2006): random graph building under heterogeneous overlay • Liang & Nahrstedt (INFOCOM2006): propose RandPeer, a peer-to-peer QoS-sensitive membership management protocol

  7. Related Work • An problem not well addressed is how to optimize the second step, that is, • how to do optimal block scheduling and maximize the throughput of data-driven protocol under a constructed overlay • Most existent methods are straight forward and ad hoc • Chainsaw: pure random way • DONet: greedy local rarest-first • PALS: round-robin method

  8. Problem Statement and Formulation Local Rarest First (LRF) strategy Throughput is 4 Optimal scheduling, throughput gain is 25% Some requests congestion at node 1 • How to do optimal scheduling to maximize the throughput of the whole overlay? • The real situation is more complicated because different blocks may have different importance and the bottlenecks are not only at the last mile. • Our basic approach: • Define priority to different blocks due to their importance • Maximize the sum of priorities of all requested blocks

  9. Problem Statement and Formulation - Priority Definition • We use two factors to represent the significance of a block: • rarity factor • emergency factor • We define the priority of block j∈Ai for node i∈R as follow: • Pji = βPR(Σk∈Nbr(i)hkj)+(1-β)PE(Ci+WT-dji), • Where 0≤β≤1, functions PR(*) (rarity factor) and PE(*) (emergency factor) are both monotonously non-increasing ones

  10. Problem Statement and Formulation - Formulation • Decision variable • Global block scheduling problem: • s.t.

  11. Global Optimal Solution • Convert the global block scheduling formulation into an equivalent Min-Cost Flow Problem

  12. Global Optimal Algorithm • Proposition: • The optimal goal of global block scheduling problem has the same absolute value as the minimum flow amount of its corresponding min-cost network flow problem. The flow amount on arc (vkin, vijb) ∈{0, 1} is just the value of xkji, which is the solution to the optimal block scheduling. • Algorithm complexity: • O(nm(loglogU)log(nC)), where n and m are the number of vertices and arcs while U and C is the largest magnitude of arc capacity and cost

  13. Distributed Algorithm • We first use a simple way to estimate the bandwidth that is available from each neighbor with historical information. • qki(m):the total number of blocks arrived at node i from neighbor k in the mthperiod. • Wki(m+1): the estimated bandwidth from node k to node i

  14. Distributed Algorithm • With the estimated available bandwidth, a local block scheduling is performed on each node • It can be also transformed into an equivalent min-cost network flow problem for local optimal request

  15. Distributed Algorithm • Heuristic distributed algorithm: • Node i estimates the bandwidth Wki(m+1) that its neighbor k can allocate it in the (m+1)th period with the traffic received from that neighbor in the previous M periods, as shown in equation (3); • Based on Wki(m+1), node i performs the local block scheduling (2) using min-cost network flow model. The results xkji∈{0,1} represent whether node i should request block j from neighbor k; • Send requests to every neighbor.

  16. Performance Evaluation- Compared Scheduling Methods • Random Strategy: each node will assign each desired block randomly to a neighbor which holds that block. Chainsaw uses this simple strategy. • Local Rarest First (LRF) Strategy: A block that has the minimum owners among the neighbors will be requested first. DONet adopts this strategy. • Round Robin (RR) Strategy: All the desired blocks will be assigned to one neighbor in a prescribed order in a round-robin way. If there is multiple available senders, it is assigned to a sender that has the maximum surplus available bandwidth.

  17. Simulation Configuration • For a fair comparison, all the experiments use the same simple algorithm for overlay construction • Delivery ratio: to represent the number of blocks that arrive at each node before playback deadline over the total number of blocks encoded. • DSL nodes: • Download bandwidth: 40% 512K, 30% 1M, 30% 2M • Upload bandwidth: half of download bandwidth • 500 nodes • Each node has 15 neighbors • Request period: 2 second

  18. Simulation Results • All are DSL nodes with exchanging window of 10 sec and bottlenecks only at the last mile. Group size is 500

  19. Simulation Results • All are DSL users with exchanging window of 10 sec and end-to-end available bandwidth 10~150Kbps. Group size is 500

  20. Conclusion & Future Work • The contributions of this paper are twofold. • First, to the best of our knowledge, we are the first to theoretically address the streaming scheduling problem in data-driven (swarming based) streaming protocol. • Second, we give the optimal scheduling algorithm under different bandwidth constraints, as well as a distributed asynchronous algorithm which can be practically applied in real system and outperforms existent methods by about 10%~80% • Future work • How to do optimization over a horizon of several periods, taking into account the inter-dependence between the periods. • How to do optimal scheduling with scalable video coding (such as layered video coding) or multiple description coding

  21. Thanks Q&A

More Related