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

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

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

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

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

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.

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.

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

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.

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

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!

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.

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

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.

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…