The Impact of False Sharing on Shared Congestion Management Aditya Akella and Srinivasan Seshan (Computer Science Department, Carnegie Mellon University)

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The Impact of False Sharing on Shared Congestion Management Aditya Akella and Srinivasan Seshan (Computer Science Department, Carnegie Mellon University) Shared Congestion Management Successful in preventing congestion collapse But, sub-optimal when there are multiple concurrent flows between source and destination –Competition for bandwidth –Ensemble shows aggressive behavior Advantages –Enforces co-operation amongst multiple concurrent flows from a source to a destination –Ensures that the ensembles exhibits reasonable AIMD behavior Disadvantage –False sharing – two or more flows sharing congestion state may not share the same bottleneck Congestion Control TodayShared Congestion Management What is False Sharing? When would congestion sharing with Flow 1 and 2 result in false sharing? –Flows 1 and 2 treated differently by the network e.g. DiffServ –Flows 1 and 2 take different paths e.g., dispersity routing, NATs Flow 1 Flow 2 Evaluate the impact, detection and response –Is congestion control compromised? & Does performance of individual flows suffer? –When and how can false sharing be detected? –How should end systems be modified to deal with false sharing? Dst Src Service Differentiation –Network may give different flows different QoS  False sharing occurs when endpoint is unaware of QoS  E.g. IETF’s Diffserv architecture –Simulation set-up  Two Diffserv classes – Assured Forwarding (AF) and Best Effort (BE)  10 flows belong to AF and 40 belong to BE Impact of False Sharing False sharing reduces observed flow throughput False sharing increases observed flow loss rate Here  1 and  2 are the respective loss event rates of the two flows, 1 and 2 are the throughputs of the individual flows without sharing, R 1 and R 2 are their RTTs, and R min is min (R 1, R 2 ) Bandwidth in Mbps Path Diversity –When flows taking different paths are aggregated into a single macroflow, –Flows might share some/all/none of the bottlenecks –Results parallel analytic expectations Fully Shared Bottleneck Ratio of RTTs Bandwidth in Mbps Unshared Bottleneck Ratio of bottleneck bandwidths Bandwidth in Mbps Semi-Shared Bottleneck Ratio of potential bandwidths Bandwidth in Mbps On-going Work Currently implementing false sharing detection in linux kernel Responding to real world issues –Greater degree of noise in measurements –Short-lived flows –Realistic traffic patterns End-System Response Flows Share a Bottleneck Flows Share no Bottlenecks When the 90% confidence intervals for the auto and cross correlation metrics no longer overlap, the detection test outputs a decision of share or no share It is harder to detect shared bottlenecks (90 secs) than to detect no shared bottlenecks (35 secs), as can be deduced from the detection times Design Issues –Start with a default of sharing congestion  Scheduling – detection tests work best when packets are nicely interleaved Possible only when flows belong to the same macroflow  Delay and loss correlation tests detect false sharing more easily than they detect shared bottlenecks  One possible concern – host might transmit data too quickly during detection Unlikely, as bandwidth achieved during false sharing is limited by the slower flow –Upon detection – segregate flows  Congestion Manager – associate flows with different macroflows  Addition to API to support control of sharing Time in Seconds Correlation Measure Time in Seconds Correlation Measure Detection of False Sharing Unshared: Loss Correlation Plot Rule: Uncorrelated delays and losses across flows is a strong indicator of false sharing Exception All losses on flow 2 Losses on both flows – back2back losses show as a “pile” All losses on flow 1 Time in Seconds Fully Shared: Delay Correlation Plot Note: Strong correlation between flows Time in Seconds Delay in Seconds Unshared: Delay Correlation Plot Detecting False Sharing: Tests Loss Correlation Test: Not as good as delay correlation test Delay Correlation Test: Does not necessarily segregate flows with inherently different RTTs Out-of-Order Test: Most robust of the three tests and requires single destination host Tests do not detect false sharing when none exists –Loss and delay tests either output a decision that shared bottlenecks exists or remain inconclusive Time in Seconds Delay in Seconds Unshared Semi-Shared Fully Shared Three types of bottleneck sharing (unshared, semi-shared, fully-shared) Diffserv AF’s share of bottleneck bandwidth in %-age