1. PERSISTENT DROPPING: An Efficient Control of Traffic Aggregates Hani JamjoomKang G. Shin Electrical Engineering & Computer Science UNIVERSITY OF MICHIGAN, Ann Arbor

2. Main Focus Controllability of new connection requests during server overload High arrival rate of legitimate SYN packets is a main ingredient in Flash Crowd Events High arrival rate of legitimate SYN packets is a main ingredient in Flash Crowd Events Persistence of new connection requests: Changes the arrival behavior of incoming traffic Changes the arrival behavior of incoming traffic Reduces the accuracy of rate-based flow control Reduces the accuracy of rate-based flow control Designing an effective AQM mechanism Better controls persistence in underlying traffic Better controls persistence in underlying traffic Reduces the mean client-perceived delay Reduces the mean client-perceived delay

3. Dynamics of Aggregate Traffic New Connection Requests Queue Packet drop Detect loss Retransmit Persistent behavior AQM Control Point: End-server/Router independent

4. What is Persistence? Persistence is the repeated attempts of accessing a service even after network congestion or server overload is detected. Examples: User Persistence: Real users pressing reload button when servers are too slow User Persistence: Real users pressing reload button when servers are too slow Client Persistence: TCP retransmission upon timeout Client Persistence: TCP retransmission upon timeout Basic mechanism to recover from network errors

5. Factors Affecting Client Persistence Browser Parallelism CLIENT SERVER ROUTER Congestion Control (loss recovery) HTML Content Connection Processing Queue Management Drops cause more traffic, which can cause more drops Drops increase client-perceived delay, which can cause a new set of parallel connections

6. Persistence Changes Arrival Behavior Mimic a Flash Crowd event to a Web server Drop-tail queue Non-persistent (open-loop) arrival is unaffected by packet drop Persistence increases burstiness and amount of aggregate traffic

7. Persistence Affects Controllability network pipe Network Queue drop prob. = 0.5

8. Increase of Aggregate Traffic Incoming requests are independent Uniform dropping with probability Uniform dropping with probability “p” retransmission attempts before timing out “n” retransmission attempts before timing out Effective aggregate arrival rate:   + p + p 2 + … + p n = (1-p n+1 ) / (1-p) Only p n+1 requests time out Define the effective timeout probability (p*) as the true impact of applied control

9. Estimation of Connect Delay Split incoming streams into n+1 transmission classes Assign each transmission class k a drop probability p k Let T k be the retransmission time of class k Rewrite the aggregate arrival rate:   + p 0 + p 0 p 1 + … + p 0 … p n-1   + p 0 + p 0 p 1 + … + p 0 … p n-1 will incur no delay will incur T 1 delay will incur T 2 delay p 0 … p n will time out & incur T abort delay Expected connection-establishment latency EL h = (1- p 0 … p n ) RTT + p 0 (1-p 1 ) T 1 + p 0 p 1 (1-p 2 ) T 2 + … + p 0 (1-p 1 ) T 1 + p 0 p 1 (1-p 2 ) T 2 + … (p 0 … p n ) T abort (p 0 … p n ) T abort

10. Optimization Connection-establishment latency EL h = (1- p 0 … p n ) RTT + p 0 (1-p 1 ) T 1 + p 0 p 1 (1-p 2 ) T 2 + … + p 0 (1-p 1 ) T 1 + p 0 p 1 (1-p 2 ) T 2 + … (p 0 … p n ) T abort (p 0 … p n ) T abort Minimize EL h under the constraint p* = p n+1 Set p 0 = p* and p 1 = p 2 = … = 1 Delay is minimized Delay is minimized EL h = (1- p*) RTT + (p*) T abort Same timeout probability Same timeout probability

11. Persistent Dropping (PD) Randomly choose (p*  ) requests and keep dropping them on every retransmission attempt Advantages: Minimizes delay Minimizes delay Minimizes aggregate traffic Minimizes aggregate traffic Reduces correlation between current and future arrivals Reduces correlation between current and future arrivals Decouples control from connection-establishment delay Decouples control from connection-establishment delay

12. Example of PD network pipe Network Queue drop prob. is 0.5 but timeout prob. is 0.125

13. Implementing PD Must distinguish between new and retransmitted requests How to choose connections to drop IP source and destination addresses & TCP source and destination ports IP source and destination addresses & TCP source and destination ports Using hashing to improve performance h(x 1,x 2, …, x k ) = x 1  x 2  …  x k  K(t) mod R Use prime multiplier, K(t), to minimize hash collisions Mapping: Stateless (efficient but may be unfair) Stateless (efficient but may be unfair) State-based (accurate but requires more computing resources) State-based (accurate but requires more computing resources)

14. State-based Implementation Hash 0 1 allow drop Incoming packet p* used drop table U update each entry holds the first drop time of an incoming request

15. State-based Implementation Hash 0 1 allow drop retransmitted packet p* used drop table U update Minimize state representation Detect hashing collisions Monitor dropping accuracy used

16. Evaluation Setup Focus on how control affects connection- establishment latency CLIENTSERVER ROUTER Apache 1.3Eve mimicking typical user behavior Implement different drop policies

17. Observed Behavior Emulating Persistent Clients user think time sync point sync point ON period OFF period New clients arrive independently

18. Mean Request Delay Rate-Based Dropping 56% of requests will timeout 10% and will have 100% longer delays than PD Connection timeout dominates delay

19. Successful Request Delay

20. Delay of Successful Visits

21. Conclusions Client persistence matters It affects controllability and underlying traffic It affects controllability and underlying traffic Traditional control mechanisms are not designed to deal with client persistence Traditional control mechanisms are not designed to deal with client persistence PD provides effective control under sustained overload scenarios Controls the traffic more accurately Controls the traffic more accurately Minimizes the delay for successful requests Minimizes the delay for successful requests PD can be efficiently implemented using hash- based flow selection

22. Future Directions Model the persistence of real users Simple trace playback is not enough Simple trace playback is not enough Must account for demography Must account for demography Persistence changes based on importance of information Persistence changes based on importance of information Study effects of persistence on other network environments