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Communication (II) Chapter 4 5/25/2017 1 Topics Fundamentals Stream Multicast Overlay (application layer multicast) 5/25/2017 Application layer MC vs IP layer MC Epidemic and gossip 2 Multicast Multicast: a source sends a message to the subset of network nodes (say a multicast group) Applications: Video conferences, network games, database A source could send through many unicast However, sender may not know individual receivers and efficiency issue Unicast and Broadcast 5/25/2017 3 Network multicast A “join” protocol (so to send or receive from) multicast group - Internet Group Management Protocol (IGMP) - Routers forward mcast-addressed datagrams to hosts that have “joined” that multicast group - Multicast Routing protocols -- different trees for diff groups Group-shared tree 5/25/2017 Source-based trees 4 Overlay multicast An application forms their own multicast network - overlay network A network that has only hosts who wants to join the MC application -- “routers” are hosts! Network links are TCP connections. The network can be a tree or a mesh or … 5/25/2017 5 Overlay Construction The relation between links in an overlay and actual network-level routes. 5/25/2017 6 Overlay multicast metrics Problem: logical links vs physical links Metrics: Link stress (per link): how often a packet crosses the same link Closer to 1, the better Stretch or relative delay penalty (RDP): Measures the ratio in the delay between two nodes in the overlay, and the delay that those two nodes connecting in the underlying network. Smaller the ratio (minimum be 1), the better Tree cost: minimum spanning tree. 5/25/2017 7 Application architectures Rendezvous node Well know node, keep the tree members For a single source based applicaiton Find a best parent node 5/25/2017 Direct to the source (the stretch is 1 !)... Disadvantage? Consider nodes’ load (degree), e.g., k neighbor. 8 Topics Fundamentals Stream Multicast Overlay (application layer multicast) Epidemic and gossip 5/25/2017 9 Data Dissemination Simple techniques for spreading information in very large-scale distributed systems. Want efficiency, robustness, speed, scale Tree distribution is efficient, but fragile (plus configuration is difficult) Flooding is robust, but inefficient No central control Gossip is both efficient and robust, but has relatively high latency 5/25/2017 Or epidemic. 10 Probabilistic multicast Distributed local information (local view) Loose or no synchronization Scalable Reliable Probabilistic guarantees on full delivery No delay upper bound Graceful degradation in the presence of failure Delete message Replication, fault-tolerant 5/25/2017 11 Gossip-based Protocols Anti-entropy propagation model Node P picks another node Q at random Subsequently exchanges updates with Q Differ by the number of time they gossip the same message or the number of gossip targets they select each time. Some “epidemic” related terms: Infected – holds the data and willing to spread further Susceptible – not seen the data yet. Removed – has the date but will not send to others. 5/25/2017 12 Push and Pull Approaches to exchanging updates P only pushes its own updates to Q P only pulls in new updates from Q P and Q send updates to each other (1) when many are infected, push may induce long delay for spreading Pull is better: a susceptible asks around, high chance to hit a infected. (2) when data entries are big, send a “digest” (instead of the data directly) of the state, and the recipient can request anything it doesn’t already have. 5/25/2017 Apply to push, pull and push-pull. 13 Centralized algorithm Round: picking node, picking a piece of data 5/25/2017 log2(n), for n nodes. 14 Gossip-based protocol: k fanout 4 3 0 5 1 2 9 8 6 5/25/2017 7 15 Analysis Probability of infection n nodes, k member infected, Anybody can infect anyone else with equal probability. What is the probability Pinfect(k, n) that a particular uninfected member is infected in a round if k are already infected? 5/25/2017 16 Analysis For simple epidemic Gossip propagation time Expected number of rounds Expected # of rounds # rounds 5/25/2017 17 Analysis s: fraction of nodes remain uninfected: 1/k: probability of stopping infecting others. 5/25/2017 18 Further design issues How to know the n? Or , scalable membership protocol? Paper, [SCAMP: lightweight membership service for gossip-based protocols] Removing data: Early deletion cause restore old data keep record - Death certificates 5/25/2017 Repeat spreading Death certificates When to clean up? 19