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Dynamics of End-host controlled Routing

Dynamics of End-host controlled Routing. Mukund Seshadri Prof. Randy Katz. Sahara Retreat Jan 2004. Problem. Consider multiple independent overlay networks/flows, each choosing the best overlay route Can this be unstable/inefficient? Identify such scenarios. Suggest improvements.

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Dynamics of End-host controlled Routing

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  1. Dynamics of End-host controlled Routing Mukund Seshadri Prof. Randy Katz Sahara Retreat Jan 2004

  2. Problem • Consider multiple independent overlay networks/flows, each choosing the best overlay route • Can this be unstable/inefficient? • Identify such scenarios. • Suggest improvements.

  3. Motivation • Overlay routing can provide better functionality, performance or resilience. • e.g. RON[3], Detour[4], ESM[5]. • What if several entities set up their own overlay flows? • e.g. using overlay support primitives [2]. • Primary app – multimedia streams. • Flows can have some physical links in common, no explicit coordination. • e.g. on popular shared test-beds like PlanetLab[6]. • Different networks/independent flows from same network.

  4. Extra Slide More Background • Resilient Overlay Networks • Recovers from routing failures in around 20s, as opposed to several minutes in normal BGP. • If default route from Node A to B fails, then data is redirected through Node C. • All available paths are probed frequently • Does not scale beyond 50 nodes • End System Multicast • End-hosts form a low-delay or low-b/w degree-bounded mesh and then a multicast tree.

  5. Sources Sources Sources Destinations Destinations Destinations 1+ Mbps (L2) Sources Sources Sources Destinations Destinations Destinations 1 Mbps (L3) Unstable Routing Example Data Paths Available Paths Bottleneck Physical Link Overlay Nodes Each source has a 1Mbps flow. • Oscillation of both flows between the two alternate paths is possible.

  6. Outline of Study • Used simulations to study requirements for good performance and factors affecting it. • Some form of “restraint” is needed • Hysteresis Threshold (H) • Randomized selection • Decision times. • Automatic discovery of H • Factors affecting performance • Size and number of flows, path density, cross-traffic, more…

  7. Simulation Model • M overlay networks/flows with N available overlay paths each • All paths monitored • Available b/w inferred “perfectly” in a time window (Tm) • Configurable error factor • Best path is selected to send traffic on (GREEDY) • Route change based on bandwidth improvement threshold (H) • Periodic decisions (Tr) • M: 100-1000, N: 5-50. • Path-level simulator • Characterizes shared bottleneck links. • The level of sharing is characterized by “path density” (Pf) • Unicast CBR flows with bandwidth requirement. • Flows arrive and depart with lifetimes around 1000 sec. • Metric: Loss Rate (related to bandwidth).

  8. Extra Slide Simulation Parameters • Unless mentioned otherwise, these are the values used for system parameters.

  9. Need for Hysteresis • No/Low hysteresis => very unstable, high loss : red line. • H too high => high loss due to poor route selection : blue line. • Optimal value of H : green line.

  10. Path density and H • The best value of H varies significantly with path density (Pf), flow size and other parameters. • The minimum of each each line is the best setting of H, for that value of Pf . • High Pf => greater chance of interaction => worse stability and loss rate.

  11. Extra Slide Other factors and H • The best value of H varies with other parameters too: • Relative flow size – proportional to inter-arrival-time (IAT). • Cross-traffic percentage. • Explanation of observed trends: the impact of a flow’s re-routing is more significant when a it is a larger fraction of link capacity.

  12. Other factors - Summary • Combination of following factors leads to poor performance • High path density • Large flow size and number • Low cross-traffic • High load • High variation in bandwidths.

  13. Routing window (Tr) and H • Increasing routing window while keeping measurement window constant can improve performance • Since the no. of flows re-routing during the measurement window decreases. • But can increase reaction time after failure. • Best value of H (line minimum) varies a lot…

  14. Improvements to GREEDY • Randomly select path to be chosen • ARAND: In proportion to available bandwidths • SRAND: Best of randomly selected subset of size S • …in proportion to capacity • Reduces measurement overhead • Works well for server load balancing [1] • (different work model: jobs are assigned to only one server for their lifetime) • GRAND: Randomly select from the best S paths

  15. Randomized Selection • Much lower loss than GREEDY • SRAND and ARAND best • Best value of H still varies w.r.t. path density, etc, for SRAND

  16. Automatic Discovery of H • We propose that flows automatically discover the most suitable values of H. • Flows can independently “probe” values of H • No route change => decrease H • Route change => increase H • MIMD works slightly better than other methods. • High initial value; “quick-start” decrease phase (high decrease factor)

  17. Performance of H-discovery • Very low loss-rates compared to fixed-H. • Upper edge of C.I. is much lower than GREEDY. • H-discovery works well in all scenarios, including high IAT, below.

  18. “Cheating” flows • What if most flows use “restrained” selection, while some “cheat” by using more aggressive GREEDY methods? • When “good” flows use fixed H • The cheaters obtain much lower loss rates • Good flows don’t suffer unless cheaters exceed 35% of all flows. • When good flows use H-discovery • The cheaters do not benefit • Good flows’ loss increases when cheaters exceed 20% of all flows, but the loss is still lower than with fixed-H.

  19. Extra Slide “Cheating” flows - Graphs • The cheaters benefit when “good” flows use fixed H • …but not with the H-discovery method

  20. Conclusion • Already summarized the effect of different factors on performance. • Restraint is useful in route selection. • H, randomization, Tr • We propose dynamic discovery of H • Low loss rate in all scenarios. • Future work • Investigate dynamic models of flow and cross-traffic. • Study the usefulness of these forms restraint in network-layer routing.

  21. Extra Slide References • How Useful is Old Information – M.Mitzenmacher – PODC 1997 • Infrastructure Primitives for Overlay Networks – Karthik Lakshminarayanan et al. – under submission. • Resilient Overlay Networks – Andersen et al – SOSP 2001 • Detour: a Case for Informed Routing and Transport – Savage et al. – IEEE Micro Jan 1999. • A Case for End System Multicast – Yang-hua Chu et al. – JSAC 2002. • PlanetLab – http://www.planet-lab.org

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