Computer Science 1 ShapeShifter: Scalable, Adaptive End-System Multicast John Byers, Jeffrey Considine, Nicholas Eskelinen, Stanislav Rost, Dmitriy Zavin Listed alphabetically

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

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

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

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.

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)

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).

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

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%

Computer Science 10 Download from Multiple Nodes in Parallel Well-connected newcomer scenario Non-uniform dissemination of content 1 MB/s

Computer Science 11 Resilience To Partitioning Partition avoidance through discovery and awareness of multiple candidates Collaborative Reconstruction

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