Exploring VoD in P2P Swarming Systems By Siddhartha Annapureddy, Saikat Guha, Christos Gkantsidis, Dinan Gunawardena, Pablo Rodriguez Presented by Svetlana Geldfeld

P2P Networks  Used in many different applications for large scale content distribution  Have recently been accepted by digital media companies as an alternative distribution mechanism  Have recently been proven to be feasible for live media distribution (CoolStreaming and others)  Still challenges arise when attempting to use the system for Video-on-Demand

Paper Focus  Analysis of the issues of providing VoD using P2P  Main focus on mesh-based networks  Scheduling techniques and network coding used to improve efficiency and resources utilization  Feasibility is shown using simulations and a prototype implementation  Main concern is the feasibility of play-as-you-download P2P systems

System Requirements  Large scale of video content distribution  Low startup times and sustainable playback rates  VoD users can arrive at any point in time

Multicast Paradigm Cisco IP Multicast Example Shortcoming:Require multicast-enabled infrastructure

Peer-to-Peer Solution  No infrastructure support required  Same scalable distribution solution  Two approaches to building a P2P network:  Tree-based (push)  Mesh-based (pull)

Tree-Based P2P Network  Trees or forests are constructed for data distribution  A peer is either an interior node or a leaf node  All data is forwarded down the structure from a server (root of a tree) down to a leaf node.  Shortcomings:  System is not fair and tends to quickly get unbalanced  Interior nodes may not have sufficient network capacity to handle the application.

Mesh-Based P2P Network  Do not enforce fixed structure  Allow peers to exchange random blocks of data (efficiency)  Have lower protocol overhead  Much easier to design  More resilient to high rates of churn  Proved to be effective and efficient for bulk file distribution

Proposed System Model  A special peer (server) contains a highly demanded video content  Users arrive at random points in time  Video content is only provided sequentially from the beginning (no Fast Forward functionality)  The resources (network bandwidth) are limited  Download and upload capacity of each peer is also limited with download rate being higher that upload)

Network Model – Main Components  System consists of peers and a tracker  Tracker is responsible for new peer accommodation into the system  Each peer in the system is connected to a small subset of active nodes (neighborhood of a peer)  Peers periodically drop and establish connections in an attempt to increase download rate

Network Model – File Structure  File is divided into a number of segments  Segments are further divided into blocks  Each peer has to download all blocks in order to view the video segment.  If a block is missing, video pauses.

Experimental Setup  Simulator and a prototype network were created to a. understand the performance requirements and b. evaluate effectiveness of proposed algorithms. Simulator: a. Models performance factors (access capacities, block scheduling algorithms, etc); b. Allows to experiment on large networks. Implementation: Allows a more detailed insight into the system operation.

Simulator  Operates in discrete intervals of time (rounds).  Takes as input the size of a video file in blocks and the number of nodes  Nodes arrive/depart during simulation  Nodes locate their peers at random during each round  All block transfers happen simultaneously  Simulation does not account for network delays, locality properties, etc.

Implementation  Consists of a. Peers – active nodes b. Tracker – enables peer discovery and matching c. Logger – keeps network statistics The implementation is only used to study small scale scenarios.

Main System Description  Terms:  Setup time – the initial buffering time  Goodput – the sustainable playback rate.  Throughput – total number of blocks the node has exchanged per round  Goal:  Maximise throughput (system efficiency) and  Provide high goodput (playback rates).

Evaluated Algorithms  Naïve Approaches  True P2P (random block exchange)  Sequential block exchange  Segment-random policy:  divides the files into segments and blocks;  exchange is done at random on block level,  but sequentially on segment level.  Rarest client :  Client requests a globally rarest block  Algorithm requires global information

Network Coding  Network coding is a technique where, instead of simply relaying the packets of information they receive, the nodes of a network will take several packets and combine them together for transmission.  In the simulation the coding a. Is only restricted to segments b. Prevents the occurrence of rare blocks and ensures that each block is useful with high probability.

Algorithm Performance Comparison - Goodput

Algorithm Performance Comparison - Throughput

Network Coding Advantages  Provides greater throughput (about 14% better than global rarest)  Results in significantly less variance  Provides more predictable download times  Provides greater benefits in such cases as: a. Dynamic arrivals and departures b. Heterogeneous network capacities c. Limited peer network visibility

Scheduling Across Segments  Considerations:  Naïve scheduling reduces throughput  Network coding cannot be used Proposed approach: Worst Seeded First Algorithm Similar to traditional rarest-first approaches. The algorithm is particularly useful for the segments that are underrepresented in the network.

Worst Seeded First Algorithm

 Assumption: The source node has global knowledge of the segment representation in the network (can be done either centrally or distributively).

Operation and Effects  Policy heavily relies on a good estimate of segment representation in the network.  It increases the diversity of segments in the network  The segment that is least well represented is always picked first.  The segment representation estimate includes partially downloaded segments.

Performance Analysis

Conclusions  Naïve, greedy scheduling algorithms provide bad throughputs  Network coding is only effective when applied over a small segments (few seconds) of a video file.  Network coding reduces number of duplicate uploads and minimizes the performance variance.  Network coding improves efficiency of the system.

Conclusions  Network coding does not solve a problem of scheduling across segments.  Spanning the entire video file requires algorithms that avoid underrepresentation of segments.  The rarest first algorithms are feasible and provide good system throughput.  A combination of network coding and segment scheduling provides significant performance improvement.

Conclusions  Mesh-based P2P systems are simple to engineer and result in high resource utilization.  “Play as you download” experience with P2P systems can be achieved by combining network coding and segment scheduling.  The proposed mesh-based system is capable of playback rate close to peer’s maximum bandwidth (with a small startup delay).