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New Models and Algorithms for Active Networks

New Models and Algorithms for Active Networks. Danny Raz and Yuval Shavitt Bell Labs, Lucent Technology www.cs.bell-labs.com/ ~shavitt/actnet.html. Outline. Motivation Model description Example analysis Summary. The Active Bell-Labs Engine. An adjunct active engine to any COTS router

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New Models and Algorithms for Active Networks

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  1. New Models and Algorithms for Active Networks Danny Raz andYuval Shavitt Bell Labs, Lucent Technology www.cs.bell-labs.com/ ~shavitt/actnet.html

  2. Outline • Motivation • Model description • Example analysis • Summary

  3. The Active Bell-Labs Engine • An adjunct active engine to any COTS router • Only some (mainly) control packets are diverted to the AE • Focus on efficient network management Active Engine (AE) session 1 session 2 manager MIB filter router

  4. The Active Bell-Labs Engine (2) • Packet Delay depends on whether it passes thru the AE. • Processing time in the AE may depend on • data in the packet. • soft state in the AE. Active Engine (AE) session 1 session 2 manager MIB filter router

  5. What is the right model to analyze algorithmic solutions? • How to compare the strength of AN architectures? • Are active networks efficient?

  6. Standard Asynchronous Model • Communication is between neighbors • A message arrivals triggers computation at a node • A single bound on the delay of a communication + computation cycle

  7. [Cidon, Gopal, Kutten, 1995] • Two bounds on the delay: • C thru the SS • P thru the NCU • Caveats: • P is a constant • source routing. Not applicable to datagrams. NCU SS

  8. Our Model • Two bounds on the delay: • C thru the FF. • P(k) thru the EE. • Forwarding is done according to the destination addr. • No assumptions on the routing. • We use P(k) = P ·k Execution Environment (EE) FF Filter forwarding oracle

  9. DARPA Model vs. Our Model Execution Environment (EE) EE 1 EE 2 EE 3 FF Filter class forwarding IP NodeOS oracle

  10. Performance Measures • Communication (Message) complexity - hops traveled by messages • Time complexity - time to mission completion. • processing complexity - CPU time used.

  11. An Application Example:Route Exploration • In the model - a node is only aware of its local neighbors. • A node wishes to learn the route to some destination. • Abstraction of the traceroute program. 45

  12. A naïve Solution • The source query nodes sequentially. • O(n2) messages. • O(n2C+nP) time.

  13. report-en-route • A query process advances sequentially. • Reports are sent to the source for each query. • O(n2) messages. • O(nC+nP) time.

  14. collect-en-route • A query process advances sequentially. • Information is collected in the forward direction, and sent by the destination to the source. • O(n) messages. • O(nC+n2P) time.

  15. Route Detection Can we do better?

  16. Report-every-l • Obtain the route length. • Initiate collect-en-route in n/l segments of length l. • Complexities: • message O(n2/l) • time O(nC+l2P) • For l=n2/3: • message O(n4/3); time O(nC+ n4/3 P)

  17. Collect-rec • Optimal up to a log factor ! • Obtain the route length. • Partition the route to two segments. • Send results from the second segment using the FF. • Perform recursively. • Complexities: • message O(n log n); time O(nC+nP)

  18. Other Basic Problems • Bottleneck detection - computation along a route. • Message dissemination to an ad-hoc group. • Topology discovery. • Computation of a global function.

  19. Summary • A new model to analyze active network applications. • We demonstrated its use to optimize algorithms. • Can be used to compare strength of architectures by comparing lower bounds.

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