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PATH SELECTION AND MULTIPATH CONGESTION CONTROL BY P. KEY, L. MASSOULIE, AND D. TOWSLEY R02 – Network Architectures Michaelmas term, 2013 Ulku Buket Nazlican

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Outline Introduction The Multipath Framework Load Balancing Path Selection Game Random Path Selection Discussion and Deployment Conclusion

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Introduction Multipath routing with rate control Key ideas: Coordinated control is much better in load balancing. Nash equilibrium achieved to maximize welfare, when users try to maximize their own benefit. More paths better for performance resampling for a small set of paths

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Introduction P2P applications use uncoordinated TCP rate control Coordinated control: rates as a function of all paths Load balancing Worst case throughput > 0 Lead to Nash equilibrium More paths are better Greater welfare states and throughput capacity Uncoordinated control: rates independent Worst case throughput: log(log N) / log N Worst case: as if each user has a single path Lead to Nash equilibrium if no RTT-bias exists, maximizing welfare

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The Multipath Framework G = (V, E, C) C l : capacity of link l S: set of session classes N s: # sessions in class s. U s (x) :utility for a session in class s, sending data at rate x Every class s session uses b s paths

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Coordinated control Objective function to max social welfare: Utility is applied to aggregate sending rate. Constraint: N c : number of class s sessions, which use set of paths c U s : utility of class s session λ cr : sending rate of a class s session, that uses path r in c congestion controller TCP congestion control solves it when each session is restricted to a single path.

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Uncoordinated Control Session with path set c Independent rate controllers over each path in c. Done by separate TCP connections for each path. Objective function changes: Utility is evaluated on each path and then summed over all paths.

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Load Balancing N resources with unit capacity aN users Each selects b>1 resources randomly Load balance metric: worst-case user rate allocation Unfair allocation more time to download data

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Load Balancing Uncoordinated multipath congestion control: Resource: handling X connections Connection gets 1/X rate allocation Worst case rate allocation: log(log(N))/log(N) Coordinated multipath congestion control: Worst case rate allocation > 0 Much more better load balancing than uncoordinated control.

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Path Selection Game Each session uses exactly b paths. Same equilibrium with: Coordinated control Uncoordinated control if there is no RTT-bias. Equilibrium solves optimization problem max welfare

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Path Selection Game Coordinated Congestion Control Path allocations in Nash equilibrium solve the welfare maximization problem. Type s players only use minimum cost paths. Uncoordinated Congestion Control Utility functions are path-independent Users find throughput optimal paths Nash equilibrium Not applies for TCP Reno!

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Random Path Reselection Coordinated control: Aggregate utility increases with the number of paths. Overhead Session uses a small set of paths (say 2). In parallel tries new paths and replace with the better performing ones. Resampling process converges to a state: used aggregate rates maximize the aggregate sum of utilities. Uncoordinated control: Random resampling beneficial if no RTT bias exists.

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Discussion and Deployment Workload of finite length flows Capacity metric to handle these flows. Coordinated control produces monotonicity More is better Random reselection applies Uncoordinated control Small set of paths + random resampling higher capacity. Deployment Diversity Congestion controller

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Conclusion Beneficial Uncoordinated control: simpler, poorer performance Coordinated control: better performing, harder. Users selecting paths selfishly: Coordinated control optimum rates Uncoordinated achieves too, if RTT bias is removed. Optimum can be achieved by a small set of paths and by resampling.

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Conclusion Beneficial Uncoordinated control: simpler, poorer performance Coordinated control: better performing, harder. Users selecting paths selfishly: Coordinated control optimum rates Uncoordinated achieves too, if RTT bias is removed. Optimum can be achieved by a small set of paths and by resampling. Thanks for listening…

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