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Cloud Control with Distributed Rate Limiting

Cloud Control with Distributed Rate Limiting. Barath Raghavan , Kashi Vishwanath , Sriram Ramabhadran , Kenneth Yocum , and Alex C. Snoeren University of California, San Diego. Centralized network services. Hosting with a single physical presence However, clients are across the Internet.

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Cloud Control with Distributed Rate Limiting

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  1. Cloud Control withDistributed Rate Limiting Barath Raghavan, KashiVishwanath, SriramRamabhadran, Kenneth Yocum, and Alex C. Snoeren University of California, San Diego

  2. Centralized network services • Hosting with a single physical presence • However, clients are across the Internet

  3. Running on a cloud • Resources and clients are across the world • Services combine these distributed resources 1 Gbps

  4. Key challenge We want to control distributed resources as if they were centralized

  5. Ideal: Emulate a single limiter • Make distributed feelcentralized • Packets should experience same limiter behavior Limiters S D 0 ms S D 0 ms 0 ms S D

  6. Distributed Rate Limiting (DRL) Achievefunctionally equivalent behavior to a central limiter 1 2 Flow Proportional Share 3 Global Token Bucket Global Random Drop Packet-level (general) Flow-level (TCP specific)

  7. Distributed Rate Limiting tradeoffs Accuracy (how close to K Mbps is delivered, flow rate fairness) + Responsiveness (how quickly demand shifts are accommodated) Vs. Communication Efficiency (how much and often rate limiters must communicate)

  8. DRL Architecture Limiter 1 Estimate interval timer Limiter 2 Packet arrival Gossip Limiter 3 Enforce limit Estimate local demand Gossip Gossip Limiter 4 Global demand Set allocation

  9. Token Buckets Packet Token bucket, fill rate K Mbps

  10. Building a Global Token Bucket Demand info (bytes/sec) Limiter 1 Limiter 2

  11. Baseline experiment Limiter 1 3 TCP flows Single token bucket D S 10 TCP flows D S Limiter 2 7 TCP flows D S

  12. Global Token Bucket (GTB) (50ms estimate interval) Global token bucket Single token bucket 10 TCP flows 7 TCP flows 3 TCP flows Problem: GTB requires near-instantaneous arrival info

  13. Global Random Drop (GRD) Limiters send, collect global rate info from others 5 Mbps (limit) 4 Mbps (global arrival rate) Case 1: Below global limit, forward packet

  14. Global Random Drop (GRD) 6 Mbps (global arrival rate) 5 Mbps (limit) Same at all limiters Case 2: Above global limit, drop with probability: Excess Global arrival rate 1 6 =

  15. GRD in baseline experiment (50ms estimate interval) Global random drop Single token bucket 10 TCP flows 7 TCP flows 3 TCP flows Delivers flow behavior similar to a central limiter

  16. GRD with flow join (50ms estimate interval) Flow 2 joins at limiter 2 Flow 3 joins at limiter 3 Flow 1 joins at limiter 1

  17. Flow Proportional Share (FPS) Limiter 1 3 TCP flows D S Limiter 2 7 TCP flows D S

  18. Flow Proportional Share (FPS) Goal: Provide inter-flow fairness for TCP flows “3 flows” Local token-bucket enforcement “7 flows” Limiter 1 Limiter 2

  19. Estimating TCP demand Limiter 1 1 TCP flow S D 1 TCP flow S Limiter 2 3 TCP flows D S

  20. Estimating TCP demand Local token rate (limit) = 10 Mbps Flow A = 5 Mbps Flow B = 5 Mbps Flow count = 2 flows

  21. Estimating TCP demand Limiter 1 1 TCP flow S D 1 TCP flow S Limiter 2 3 TCP flows D S

  22. Estimating skewed TCP demand Local token rate (limit) = 10 Mbps Flow A = 2 Mbps Bottlenecked elsewhere Flow B = 8 Mbps Flow count ≠ demand Key insight: Use a TCP flow’s rate to infer demand

  23. Estimating skewed TCP demand Local token rate (limit) = 10 Mbps Flow A = 2 Mbps Bottlenecked elsewhere Flow B = 8 Mbps 10 8 Local Limit Largest Flow’s Rate = = 1.25 flows

  24. Flow Proportional Share (FPS) Global limit = 10 Mbps Limiter 1 Limiter 2 1.25 flows 2.50 flows Global limit x local flow count Total flow count Set local token rate = 10 Mbps x 1.25 1.25 + 2.50 = = 3.33 Mbps

  25. Under-utilized limiters Limiter 1 1 TCP flow S D 1 TCP flow S Wasted rate Set local limit equal to actual usage (limiter returns to full utilization)

  26. Flow Proportional Share (FPS) (500ms estimate interval)

  27. Additional issues • What if a limiter has no flows and one arrives? • What about bottlenecked traffic? • What about varied RTT flows? • What about short-lived vs. long-lived flows? • Experimental evaluation in the paper • Evaluated on a testbed and over Planetlab

  28. Cloud control on Planetlab • Apache Web servers on 10 Planetlab nodes • 5 Mbps aggregate limit • Shift load over time from 10 nodes to 4 nodes 5 Mbps

  29. Static rate limiting Demands at 10 apache servers on Planetlab Wasted capacity Demand shifts to just 4 nodes

  30. FPS (top) vs. Static limiting (bottom)

  31. Conclusions • Protocol agnosticlimiting (extra cost) • Requires shorter estimate intervals • Fine-grained packet arrival info not required • For TCP, flow-level granularity is sufficient • Many avenues left to explore • Inter-service limits, other resources (e.g. CPU)

  32. Questions!

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