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Anemone: Edge-based network management

Anemone: Edge-based network management. Mort (Richard Mortier) MSR-Cambridge December 2004. Network management. …is the process of monitoring and controlling a large complex distributed system of dumb devices where failures are common and resources scarce

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Anemone: Edge-based network management

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  1. Anemone:Edge-based network management Mort (Richard Mortier) MSR-Cambridge December 2004

  2. Network management • …is the process of monitoring and controlling a large complex distributed system of dumb devices where failures are common and resources scarce • Enterprise networks are large but closely managed • Contrast with the Internet or university campus networks • No-one has the big picture! • Internet routeing uses distributed protocols • Current management tools all consider local info • Patchy SNMP support, configuration issues, sampling artefacts, tools generate CPU and network load

  3. Anemone • Building edge-based network management platform • Collect flow information from hosts, and • Combine with topology information from routeing protocols • Enable visualization, analysis, simulation, control • Avoid problems of not-quite-standard interfaces • Management support is typically ‘non-critical’ (i.e. buggy ) and not extensively tested for inter-operability • Do the work where resources are plentiful • Hosts have lots of cycles and little traffic (relatively) • Protocol visibility: see into tunnels, IPSec, etc

  4. Problem context: Enterprise networks • Large • 105 edge devices, 103 network devices • Geographically distributed • Multiple continents, 102 countries • Tightly controlled • IT department has (nearly) complete control over user desktops and network connected equipment

  5. Talk outline • System outline • What would it be good for? • In more detail… • Research issues

  6. routes srcs dsts System outline Packets Routeing protocol Flows Topology Traffic matrix Set of routes Anemone platform Simulator Control Visualize Simulate

  7. Where is my traffic going today? • Pictures of current topology and traffic • Routes+flows+forwarding rules  BIG PICTURE • In fact, where did my traffic go yesterday? • Keep historical data for capacity planning, etc • A platform for anomaly detection • Historical data suggests “normality,” live monitoring allows anomalies to be detected

  8. Where might my traffic go tomorrow? • Plug into a simulator back-end • Discrete event simulator, flow allocation solver • Run multiple ‘what-if’ scenarios • …failures • …reconfigurations • …technology deployments • E.g. “What happens if we coalesce all the Exchange servers in one data-centre?”

  9. Where should my traffic be going? • Close the loop: compute link weights to implement policy goals • Recompute on order of hours/days • Allows more dynamic policies • Modify network configuration to track e.g. time of day load changes • Make network more efficient (~cheaper)?

  10. Where are we now? • Three major components • Flow collection • Route collection • Anemone platform • Studying feasibility and building prototypes

  11. Data collection: flows • Hosts track active flows • Using ETW, low overhead event posting infrastructure • Built prototype device driver provider & user-space consumer • Used 24h packet traces from (client, server) for feasibility study • Peaks at (165, 5667) live and (39, 567) active flows per sec

  12. Data collection: routes • OSPF is link-state so collect link state adverts • Similar to Sprint IS-IS collection • Was also done at AT&T (NSDI’04 paper) • Completely passive • Modulo configuration  • Process data to recover network “events” and topology • Data collected for (local, backbone) areas (20 days) • LSA DB size: (700, 1048) LSAs ~ (21, 34) kB • Event totals: (2526, 3238) events ~ (5.3, 6.7) evts/hr • Small, generally stable with bursts of activity

  13. NB: Spike to ~100 from initial DB collection truncated for readability

  14. complete dataset steady state 35 mins: LSRefreshTime+CheckAge? 30 mins: LSRefreshTime? 10 mins: data ca. 25/Nov? 1–2 mins: RouterDeadInterval?

  15. The Anemone platform • “Distributed database,” logically containing • Traffic flow matrix (bandwidths), {srcs}×{dsts} • Hosts can supply flows they source and sink • Only need a subset of this data to get complete traffic matrix • …each entry annotated with current route, src to dst • Note src/dst might be e.g. (IP end-point, application) • OSPF supplies topology → routes • Where/what/how much to distribute/aggregate? • Is data read- or write-dominated? • Which is more dynamic, flow or topology data? • Can the system successfully self-tune?

  16. The Anemone platform • Wish to be able to answer queries like • “Who are the top-10 traffic generators?” • Easy to aggregate, don’t care about topology • “What is the load on link l?” • Can aggregate from hosts, but need to know routes • “What happens if we remove links {l…m}?” • Interaction between traffic matrix, topology, even flow control • Related work • { distributed, continuous query, temporal } databases • Sensor networks, Astrolabe, SDIMS, PHI …

  17. The Anemone platform • Building simulation model • OSPF data gives topology, event list, routes • Simple load model to start with (load ~ # subnets) • Predecessor matrix (from SPF) reduces flow-data query set • Can we do as well/better than e.g. NetFlow? • Accuracy/coverage trade-off • How should we distribute the data and by what protocols? • Just OSPF data? Just flow data? A mixture? • How many levels of aggregation? • How many nodes do queries touch? • What sort of API is suitable? • Example queries for sample applications

  18. Research issues • Corner cases • Scalability • Robustness, accuracy • Control systems

  19. Research issues • Corner cases • Multi-homed hosts: how best to define a flow • L4 routeing, NAT, proxy ARP, transparent proxies • (Solve using device config files, perhaps SNMP) • Scalability • Host measurement must not be intrusive (in terms of packet latency, CPU load, network bandwidth) • Aggregators must elect themselves in such a way that they do not implode under event load • What happens if network radically alters? E.g. • Extensive use of multicast • Connection patterns shift due to e.g. P2P deployment

  20. Research issues • Robustness • Network management had better still work as nodes fail or the network partitions! • Accuracy in the face of late, partial information • By accident: unmonitored hosts • By design: aggregation, more detail about local area • Inference of link contribution to cumulative metrics, e.g. RTT • Network control: modify link weights • How efficient is the current configuration anyway? • What are plausible timescales to reconfigure?

  21. Summary • Aim to build a coherent edge-based network management platform using flow monitoring and standard routeing protocols • Applications include visualization, simulation, dynamic control • Research issues include • Scalability: want to manage a 300,000 node network • Robustness: must work as nodes fail or network partitions • Accuracy: will not be able to monitor 100% of traffic • Control systems: use the data to optimize the network in real-time, as well as just observe and simulate

  22. Current status • Submitted Networking 2005 paper • Prototype ETW provider/consumer driver • Studied feasibility of flow monitoring • Prototype OSPF collector & topology reconstruction • Investigating “distributed database” via simulation • Query properties • System decomposition • Protocols for data distribution • Questions, comments?

  23. Backup slides • SNMP • Internet routeing • OSPF • BGP • Security

  24. SNMP • Protocol to manage information tables at devices • Provides get, set, trap, notify operations • get, set: read, write values • trap: signal a condition (e.g. threshold exceeded) • notify: reliable trap • Complexity mostly in the table design • Some standard tables, but many vendor specific • Non-critical, so often tables populated incorrectly

  25. Internet routeing • Q: how to get a packet from node to destination? • A1: advertise all reachable destinations and apply a consistent cost function (distance vector) • A2: learn network topology and compute consistent shortest paths (link state) • Each node (1) discovers and advertises adjacencies; (2) builds link state database; (3) computes shortest paths • A1, A2: Forward to next-hop using longest-prefix-match

  26. OSPF (~link state routeing) • Q: how to route given packet from any node to destination? • A: learn network topology; compute shortest paths • For each node • Discover adjacencies (~immediate neighbours); advertise • Build link state database (~network topology) • Compute shortest paths to all destination prefixes • Forward to next-hop using longest-prefix-match (~most specific route)

  27. BGP (~path vector routeing) • Q: how to route given packet from any node to destination? • A: neighbours tell you destinations they can reach; pick cheapest option • For each node • Receive (destination, cost, next-hop) for all destinations known to neighbour • Select among all possible next-hops for given destination • Advertise selected (destination, cost+, next-hop') for all known destinations • Selection process is complicated • Routes can be modified/hidden at all three stages • General mechanism for application of policy

  28. Security • Threat: malicious/compromised host • Authenticate participants • Must secure route collector as if a router • Threat: DoS on monitors • Difference between client under DoS and server? • Rate pace output from monitors • Threat: eavesdropping • Standard IPSec/encryption solutions

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