Congestion Control in CSMA-Based Networks with Inconsistent Channel State V. Gambiroza and E. Knightly Rice Networks Group

Violeta Gambiroza Motivation Congestion Indication Loss Loss is not a good congestion indicator in CSMA-based networks Time Sending Rate packet loss (TCP)

Violeta Gambiroza Motivation Congestion Indication Loss (TCP) Buffer occupancy Buffer Threshold

Violeta Gambiroza Motivation Congestion Indication Loss (TCP) Buffer occupancy Buffer Threshold Dropped packet CSMA-based networks Mutually interfering links Distributed queue

Violeta Gambiroza Our Approach Assumptions –Unmodified MAC such as IEEE  Decoupled vs. joint design –Utility maximization congestion control Define and incorporate key issues and challenges –Study their impact on performance

Violeta Gambiroza Outline Key issues and challenges Background –Utility function –Utility maximization congestion control Results –Inconsistent states –Comparison with TCP

Violeta Gambiroza Issues and Challenges in CSMA-Based Networks Channel state in multihop networks –Inconsistent S 2 is unaware of S 1 -R 1 transmission

Violeta Gambiroza Issues and Challenges in CSMA-Based Networks Channel state in multihop networks –Inconsistent Data transmission capacity – Actual capacity for data transmission unknown – Depends on number of competing flows, node locations, propagation environment – Efficiency Index γ – Fraction of C available for data transmission Distributed queue service order – Service order is not FIFO – Information asymmetry service order (close to) strict priority State observation and sharing – Multiple metrics measured flow 1 flow 2 Strict Priority Mutually interfering links Data transmission capacity?

Violeta Gambiroza Issues and Challenges in CSMA-Based Networks Channel state in multihop networks –Inconsistent Data transmission capacity – Actual capacity for data transmission unknown – Depends on number of competing flows, node locations, propagation environment – Efficiency Index γ – Fraction of C available for data transmission Distributed queue service order – Service order is not FIFO – Information asymmetry service order (close to) strict priority State observation and sharing – Multiple metrics measured Critical to performance Not incorporated by any of the prior work

Violeta Gambiroza Utility Function Degree of user’s satisfaction with service quality –Quality indicators: bandwidth, time (delay), power… Bandwidth utility function –Relation between user’s satisfaction and network bandwidth

Violeta Gambiroza Utility Function (Example) Voice traffic Utility Bandwidth β

Violeta Gambiroza Utility Function (Example) File transfer: U = S – a t –S: user’s happiness when transfer is infinitely fast –t: transfer time –a: rate at which satisfaction decreases with time Utility Time S S/a

Violeta Gambiroza Utility Maximization Congestion Control C - capacity vector A routing matrix x – vector of allocated rates (x r – allocated rate of user r) U r (x r ) – utility –Increasing, strictly concave, continuously differentiable, additive Assumes knowledge of utility functions

Violeta Gambiroza For any initial condition congestion control algorithm converges to the unique solution Sum of the prices needs to be known Price can be conveyed using just one bit feedback Efficiency Index approximation ε 0 arbitrarily close to exact solution Congestion Control Algorithm μ j (t) – penalty function, price charged by resource j U(x) = logx Approximation

Violeta Gambiroza Example Node B unaware of D-E transmission –B’s perception of congestion and feedback incorrect Impact on performance?

Violeta Gambiroza Assumptions –Unmodified MAC such as IEEE  Decoupled vs. joint design –Utility maximization congestion control Define and incorporate key issues and challenges –Study their impact on performance  Single hop topologies »Possibly a part of more complex multihop scenarios  No collaboration »Consistent states »Inconsistent states  Collaboration »Measurement metric »Comparison with TCP Our Approach

Violeta Gambiroza Assumptions –Unmodified MAC such as IEEE  Decoupled vs. joint design –Utility maximization congestion control Define and incorporate key issues and challenges –Study their impact on performance  Single hop topologies »Possibly a part of more complex multihop scenarios  No collaboration »Consistent states »Inconsistent states  Collaboration »Measurement metric »Comparison with TCP Our Approach

Violeta Gambiroza Congestion Control with Inconsistent States and w/o Collaboration Different transmission and carrier sense ranges Leads to inconsistent states

Violeta Gambiroza Difference in Channel States We prove that any difference in channel busy times leads to convergence to unfair rates

Violeta Gambiroza Throughput and Fairness Properties Different Transmission and Carrier Sense Ranges Convergence to unfair rates Time [sec] Throughput [Mb/sec] γ = 0.8

Violeta Gambiroza Congestion Control with Collaboration Collaboration –Nodes collaborate in order to realize “true” channel state –Study effects of collaboration –Study choice of measurement metric Multiple issues and scenarios incorporated Previously studied scenarios

Violeta Gambiroza Congestion Control with Collaboration Results TCP: Has 2 outcomes

Violeta Gambiroza Congestion Control with Collaboration Results TCP: Has 2 outcomes UMCC: Fair shares very close to ideal rates UMCC achieves throughput up to 17% higher than TCP

Violeta Gambiroza Conclusions Framework to study key issues in CSMA-based networks –Channel state, data transmission capacity, service order, state observation and sharing No globally “optimal” data transmission capacity even with consistent states Inconsistent states lead to convergence to unfair rates Collaboration among nodes alleviates the problem –Per-flow measurement needed –Compared to TCP: starvation removed, better fairness, 17% higher throughput

Congestion Control in CSMA-Based Networks with Inconsistent Channel State V. Gambiroza and E. Knightly Rice Networks Group