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Distributed Monitoring of Mesh Networks Elizabeth Belding-Royer Mobility Management and Networking (MOMENT) Lab Dept. of Computer Science University of California, Santa Barbara Joint work with Krishna Ramachandran and Kevin Almeroth Motivation: Monitoring crucial for robust network operation benefits to network operators, system designers, researchers essential for evolving network technologies critical last piece in the product conceptiondesign-development-improvement loop helps bridge the gap between the expected (simulations) and the unexpected (real-world) The Big Picture Deployment UCSB 25 node mesh network (NSF WHYNET project) Monitoring and Measurement (DAMON) UCSB mesh IETF meetings LocustWorld, IV deployments 11,000 AODV nodes in 50+ countries Simulation models movement models traffic models AODV refinement The Big Picture Deployment UCSB 25 node mesh network (NSF WHYNET project) Monitoring and Measurement (DAMON) UCSB mesh IETF meetings LocustWorld, IV deployments 11,000 AODV nodes in 50+ countries Simulation models movement models traffic models AODV refinement Outline DAMON Design and Architecture DAMON Implementation DAMON@IETF Conclusions Design Challenges Device mobility Resource constraints Fluctuating link quality Short-lived network connections Design Choices: Pervasiveness of Monitoring Solution Strategy of using a centralized network element Network fails Monitoring mobile networks requires pervasive solution no hierarchical structure to mobile networks mobility nodes participate in monitoring Amount of pervasiveness complete coverage strategy limited coverage strategy State Pervasiveness Pervasiveness tradeoffs Design Choices: Pervasiveness of Monitoring Solution Strategy of using a centralized network element fails Analysis Monitoring mobile networks requires pervasive solution no hierarchical structure to mobile networks mobility nodes participate in monitoring Amount of pervasiveness complete coverage strategy limited coverage strategy Effort Pervasiveness Pervasiveness tradeoffs Additional Design Choices Number of data sinks single sink? multiple sinks? Temporal property of monitoring information determined by monitoring requirements classifications time dependent information, e.g. topology information time independent information, e.g. packet logs require differentiated handling of data DAMON: Distributed Architecture for MONitoring mobile networks Overview agents within network collect information information stored at sinks sink auto-discovery resiliency to sink failures Architecture Agents within network send monitoring information to sinks Sinks emanate periodic beacons facilitates auto-discovery and resiliency to sink failures Sink Auto-discovery beacons contain agent instructions and hop count agents use hop count to choose primary sink Sink Auto-discovery Proximity-based association (hop count) simple, low overhead but, can lead to uneven distribution of agents to sinks Tradeoff between beaconing frequency and sink detection latency Monitoring Information Time dependent i.e., energy left on a device, neighbors typically small in size packaged into time dependent digests (TDDs) transmitted to sink frequently unreliable transmission Time independent i.e., packet logs, daily traffic statistics typically large in size broken into small-sized chunks called time independent digests (TIDs) reliable transmission Client Framework Digest Classifier Collector1 … File Server Collectorn Beacon Listener Packet Classifier TDD Dispatcher TID Dispatcher Network Packet Classifier: categorizes packets based on types, dispatches to appropriate packet handler Beacon Listener: handles beacons TDD dispatcher: handles received TDDs Collectors: summarize routing table info or link quality estimates in TDDs and TIDs Client Framework Digest Classifier Collector1 … File Server Collectorn Beacon Listener Packet Classifier TDD Dispatcher TID Dispatcher Network Digest Classifier: delivers digests created by Collectors to appropriate module TDD Dispatcher for immediate transmission to sink File Server for TIDs for later delivery to sink TID Dispatcher: periodically retrieves digests for transmission to sink DAMON Implementation Goals: monitor ad hoc network behavior monitor AODV performance metrics of interest throughput traffic distribution control packet overhead mobility patterns Implementations for Linux and Microsoft Windows DAMON Information Collection AODV control packet summaries RREQ, RREP, RERR, Hello received packet counters UDP payload and timestamp Topology data routing table deltas AODV-NEIGHBOR TDDs sent every minute Data traffic statistics IP source and destination application protocol type packet size DAMON@IETF 58th IETF Meeting in Minneapolis, MN, November 9-14, 2003 Deployment goals: validate DAMON design track IETF topology evaluate AODV performance observe traffic/mobility patterns AODV Implementation Linux, Windows (thanks Intel!) 130+ downloads 20+ simultaneous ad hoc network members Network configuration complete coverage strategy one gateway provided Internet connectivity to ad hoc network users one sink deployed to collect information ad hoc network co-located with 23 IETF APs nodes used tool called PUDL to avoid unidirectional links PUDL Periodic Uni-Directional Link detector periodic unicast probes between each neighbor pair sequence numbers used to measure reliability under some threshold (40%), link filtered from AODV DAMON@IETF: Network Topology Network Troubleshooting Connectivity problems with gateway reported during 13:00-15:30 IETF session on November 11th Node ID % Broadcast Hello % Unicast Probes 1 91.8 74.1 2 76.26 12.69 3 92.06 36 4 74.73 42.18 5 69.23 54.1 6 95.42 11.4 7 97.85 6.66 Lessons from Connectivity Information 1. 2. 3. No correlation between reception of unicast and broadcast packets Routing protocols should select routes based on how reliably a path delivers unicast packets Relying on thresholds to avoid unidirectional links can eliminate links that are necessary for connectivity Traffic Distribution Per Protocol, With Link Filtering Per Protocol, Without Link Filtering AODV Traffic Distribution 60 50 40 With Link Filtering Without Link Filtering 30 20 10 0 RREQ RREP RERR Hello Conclusions Monitoring essential for robust network operation DAMON overcomes challenges associated with mobile network monitoring Future work: more DAMON deployments and analysis tools http://moment.cs.ucsb.edu/DAMON Funding provided by NSF and Intel Corporation