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Computer Science 1 ShapeShifter: Scalable, Adaptive End-System Multicast John Byers, Jeffrey Considine, Nicholas Eskelinen, Stanislav Rost, Dmitriy Zavin Listed alphabetically
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Computer Science 2 Problem Problem: efficient delivery of popular bulk content Existing Approaches: Single-source unicast Forward caching/Content Delivery Networks Reliable IP Multicast End-system multicast Our approach: Improving end-system multicast through the use of forward error correction and better topologies
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Computer Science 3 Network-supported (IP) Multicast Optimal solution: duplicates and disseminates data only when necessary Relies on network support: in the real world, IP Multicast lacks deployment Scalability concerns: per-group accounting and topology management do not scale due to limited router resources Reliability: many proposals, few solutions
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Computer Science 4 End-System Multicast Does not rely on network support: builds and manages a virtual, overlay topology of unicast links on top of the network’s physical topology Flexibility: optimization of the tree according to a richer set of metrics (perhaps specified by the application), ability to change topology on-demand Improved scalability: end-systems are richer than routers in terms of dedicated resources Problems: increased network resource requirements compared to IP Multicast, non-optimal mapping of the virtual topology onto physical topology
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Computer Science 5 Related Work Narada/End-System Multicast: Build and maintain a mesh of low-latency unicast links and use its minimal spanning tree for distribution. Also showed costs relative to IP Multicast are not excessive. Overcast: A core group of well-placed nodes uses end-system multicast to distribute bulk content internally, in order to eventually provide it to clients. A node chooses a parent based on bandwidth through the candidate nodes using the number of network hops as a tie breaker.
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Computer Science 6 Improvements in ShapeShifter Erasure codes: improved overlay management, more connected graph structure, increased adaptivity Scalable group management: a node need only be aware of a small portion of the graph but achieves coverage through continuous discovery Measurements: metrics crucial to optimization of the overlay graph, such as shared-link congestion (refer to Khaled’s presentation)
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Computer Science 7 Forward Error Correcting Codes FEC codes: a well-known solution to dealing with packet loss without using feedback – instead of retransmitting packets, redundant packets are sent combining the original packets to recover from losses. e.g. x, y, x+y, a, b, a+b+x Efficient codes: instead of the traditional slow Reed- Solomon codes, we use a variant of Tornado codes. This allows fast decoding while only requiring a small number of extra packets. Strategy: the original server sends out FEC packets along the end-system multicast graph (à la Digital Fountain).
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Computer Science 8 Recoding Problem Correlation: client nodes may have a high degree of correlation in information received due to common sources Duplication: given correlation, duplicate packets received from client nodes can be ineffectual Solution Recoding: nodes blend received packets to generate new, high utility symbols for other nodes Beyond trees: recoding allows non-tree topologies since duplication is avoided
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Computer Science 9 Uncorrelated Loss Compensation A neighbor node with greater throughput may supplement the flow of content to another node, circumventing the problematic physical link. Loss Rate 30% Loss Rate 5%
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Computer Science 10 Download from Multiple Nodes in Parallel Well-connected newcomer scenario Non-uniform dissemination of content 1 MB/s
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Computer Science 11 Resilience To Partitioning Partition avoidance through discovery and awareness of multiple candidates Collaborative Reconstruction
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Computer Science 12 Future Work Implementation underway More analysis of Codes and correlation Graph management Security issues Testing WAN, simulated and real Mobile environments Extensions to streaming media
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