1 On Class-based Isolation of UDP, Short-lived and Long-lived TCP Flows by Selma Yilmaz Ibrahim Matta Computer Science Department Boston University

2 Motivation Internet traffic is mixture of flows with different characteristics: –Smooth, unresponsive real-time traffic (UDP) –Bursty, congestion-sensitive traffic (TCP) Problems with “single class of best effort” service: –Real-time traffic: do not perform adequately because of delay variations do not back off in presence of congestion –TCP: congestion control unfairness

3 Motivation (cont.) Short-lived TCP flows –generally carry interactive/delay sensitive data –mostly operate in slow-start phase have smaller window size –produce smaller bursts –takes longer to recover from a single packet loss –arrival of flows are more bursty prevents long TCP flows from operating in a predictable mode Long-lived TCP flows –mostly operate in congestion avoidance phase have larger window size –produce big bursts can more easily recover from multiple packet losses –may shut off short-lived TCP flows –more stable

4 Architecture: Class-Based Isolation Idea: Isolate flows with different characteristics into different classes CBQ Logically Separate Communication Paths Class 1 Class 2 Round Robin/WRR Router Class 1 Class 2

5 Advantages of Architecture Protect each class from negative effects of the other classes Per-flow state is kept only at the edge routers –scalable

6 Analytic Model Extends that of T. Bonald, M. May, J. Bolot, “Analytic evaluation of RED Performance”, IEEE/INFOCOM Overview: - Bursts of B packets arrive according to Poisson process - Processing times of packets at the router are exponentially distributed - Burst size models average window size of a TCP flow - Number of packets buffered in the queue defines a Markov chain - Drop probability for a packet in a Tail-Drop and RED router is computed - Model does not capture congestion-sensitivity of TCP flows

7 RED – For simplicity, instantaneous queue size is used – min th =K/2, max th =K, max p =1 Remove Bonald et al. approximation: –“All packets in the same burst see the same drop probability d(k), where k is instantaneous queue size at the time the first packet in burst arrives at the router” >> accurate only if B is not too big than K >> for the same queue size, gives less accurate results for longer-lived flows Analytic Model (cont.)

8 Experiments Effects of –Fraction of service rate allocated to different classes –Burst size –Isolation into 2 classes: TCP, UDP –Isolation into 3 classes: UDP, short-lived TCP, long-lived TCP Each experiment is repeated for –FIFO with RED and Tail-drop –High load (total load=2) and low load (total load=1) –sharing (mixed): Flows with different characteristics compete for shared resources (queue size and service rate) –isolated: Resources are split in proportion to the load introduced by each class

9 Performance Measures Drop probability: measures effective goodput Fairness: Chui and Jain’s fairness index x i is the drop probability of flow-type i.

10 Observations 2 types of flows (UDP,TCP), 2 classes (UDP,TCP) –At high load, isolation improves fairness over shared Tail-drop by increasing drop probability of UDP –RED performs as good as isolated Tail-drop queues Isolation provides better control over QoS of each traffic type –At low load, static isolation suffers from loss in statistical multiplexing

11 Observations (cont.) 3 types of flows (UDP, short- and long-lived TCP), 2 classes (UDP, TCP) –At high load, isolation significantly reduces drop probability of short-lived flows independent of buffer management scheme Improves QoS of interactive/delay sensitive data Less timeouts, higher goodput

12 Observations (cont.) 3 types of flows (UDP, short- and long-lived TCP), 3 classes –At high load, isolation provides Better and predictable drop probability for all classes Perfect fairness across different flow types Better control over QoS of each traffic type

13 Conclusion At high load, class-based isolation provides –better fairness among different types of flows –TCP reduced drop probability improved fairness –short-lived TCP lower transmission delay –UDP generally sees increased drop probability improved service predictability Shared Tail-drop: isolation is necessary –no protection against misbehaving flows

14 Conclusion (cont.) Shared RED : isolation provides little gain –reduces bias against bursty traffic –fair Class-based isolation –better control over quality of service for each class –improved fairness –improved predictability –simple and scalable Use RED within each class to provide intra-class fairness At low load with static isolation, statistical multiplexing gains are lost –must implement dynamic resource allocation as in CBQ

15 Future Work More detailed models Non-homogeneous flows in each class and fairness among them Identify minimum number of classes for mix of TCP flows with various lifetime and RTT

16