Congestion Control

1. Congestion Control Andreas Pitsillides University of Cyprus

2. Congestion control problem • growing demand of computer usage requires: • efficient ways of managing network traffic to avoid or limit congestion in cases where increases in bandwidth not desirable or possible. • generally accepted that network congestion control problem remains critical issue and high priority, • especially given growing size, demand, and speed (bandwidth) of increasingly integrated services network. • One could argue that • network congestion unlikely to disappear in near future. • Furthermore congestion may become unmanageable • unless effective, robust, and efficient methods for congestion control are developed. Cost 257 final seminar 27-29 September 2000

3. Current scene • despite vast research efforts, still no universally acceptable solutions: • control solutions for TCP transported traffic • increasingly becoming ineffective, • cannot easily scale upeven with: • “fixes” (improved round trip time measurement, Slow-start and congestion avoidance, Fast retransmit, fast recovery algorithms, Improved congestion indication using delay (rather than loss) as feedback. • new approaches (RED, ECN, MPLS) • new architectures (diffserv, intserv,) Cost 257 final seminar 27-29 September 2000

4. Current scene (cont.) • non-TCP applications • As demand for streaming applications increases, important to ensure can co-exist with current TCP • streaming media should be subjected to similar rate controlsas TCP traffic • newly developed (also largely ad-hock) strategies are also not proven to be robust and effective • examples include model based and equation based approaches. • Even though based on a model, model is not dynamic, derived control strategy is ad-hock and not proven with regard to its properties. • Asynchronous Transfer Mode (ATM) • also witnessed similar approach, with performance of vast majority of congestion control schemes proposed for solution of Available Bit Rate (ABR) problem not proven analytically. Cost 257 final seminar 27-29 September 2000

5. Why problem still not solved? • In part, due to lack of structured approach, and • lack of strong theoretical foundationin stabilising controlled systems, • Most proposed schemes are developed using intuition and simple (ad-hock) non-linear designs. • Using simulation, these simple schemes demonstrated to be robust in variety of scenarios. • problem is that very little known why these methods work and very little explanation can be given when they fail. • Since designed with significant non-linearities, based mostly on intuition(e.g. two-phase—slow start and congestion avoidance—dynamic windows, binary feedback, …) • analysis of closed loop behaviour difficult, if at all possible, even for single control loop networks. Cost 257 final seminar 27-29 September 2000

6. Why problem still not solved? (cont.) • interaction of additional non-linear feedback loops can produceunexpected and erratic behaviour. • Empirical evidence demonstrates poor performance and cyclic behaviour of the controlled TCP/IP Internet (also confirmed analytically). • becomes worse • as link speed increases (hence bandwidth-delay product, and thus feedback delay, increases) • as demand on network for better quality of service increases. • for WAN networks • multifractal behaviour has been observed, • suggested that this behaviour—cascade effect—may be related to existing network controls. • Clearly, more effective congestion control schemes are needed to prevent serious economic losses and possible "meltdown" of the Internet. Cost 257 final seminar 27-29 September 2000

7. Two examples of existing disciplines with strong theoretical foundation • control systems theory • rich experience in controlling complex systems, • often concentrating (due to the difficulty) on single control loops to stabilise the whole system (by assuming if locally stable, then also globally—some theoretical foundation exists). • traditionally linearising model to apply linear control systems theory  new results in non-linear theory allow application • Pricing theory • has proven useful for stabilising complex interactions in human centred systems, • aiming to balance supply and demand. • Usually distributed algorithms, which through successive iterations reach stability Cost 257 final seminar 27-29 September 2000

8. IDCC: an example (with Petros Ioannou and L. Rossides) • Starting with a simple dynamic fluid flow model: • developed using packet flow conservation considerations and by matching the queue behaviour at equilibrium • Design a non-linear adaptive robust controller (IDCC - integrated dynamic congestion controller) • a specific problem formulation for handling multiple differentiated classes of traffic, operating at each output port of a switch is illustrated. • following same spirit adopted by IETF Diff-Serv for Internet define three classes of aggregated behaviour. • Premium, Ordinary, and Best Effort Traffic Services. • analytical performance boundsderived, for provable controlled network behaviour. Cost 257 final seminar 27-29 September 2000

9. Control concept Cost 257 final seminar 27-29 September 2000

10. Dynamic model For a packet buffer: For M/M/1 queue Cost 257 final seminar 27-29 September 2000

11. Simulative comparison Cost 257 final seminar 27-29 September 2000

12. Another dynamic fluid flow model for TCP window: Cost 257 final seminar 27-29 September 2000

13. Developed Control strategy • Premium Traffic Service (eq. 1, 2, 3) • Ordinary Traffic Service (eq. 4) Cost 257 final seminar 27-29 September 2000

14. Theoretical evaluation • A1. Proof of stability of Premium Traffic control strategy • Theorem A1. The control strategy described by the equations (1-3) guarantees that • queue length is bounded • allocated Capacity<=Server Capacity • queue length converges close to the reference value with time, with an error that depends on the rate of change of the traffic input rate. Cost 257 final seminar 27-29 September 2000

15. Theoretical evaluation (cont.) • A2. Proof of stability of the Ordinary Traffic control strategy • Theorem A2. The control strategy given by equation (4) guarantees that • queue length is bounded. • When bandwidth becomes available the queue length approaches the reference value with time. Cost 257 final seminar 27-29 September 2000

16. Simulative evaluation Cost 257 final seminar 27-29 September 2000

17. Steady state and transient behavior Switch 2 time evolution of Premium Traffic queue length for a LAN and WAN for 140% load demand. Note that as feedback information is local, there is no deterioration in performance due to increased WAN propagation delay. Qureue length Ref=100 ref=100 ref-=50 Cost 257 final seminar 27-29 September 2000

18. Steady state and transient behavior (cont.) Ref=900 Ref=600 Ref=300 Switch 2 time evolution of Ordinary Traffic queue length for (a) a LAN and (b) WAN for 140% load demand. (control period varies between 32 celltimes0.085 msec to 353 celltimes0.94 msec) Cost 257 final seminar 27-29 September 2000

19. Steady state and transient behavior (cont.) Typical behaviour of the time evolution of the common calculated allowed cell rate at Switch 2 for (a) LAN and (b) WAN. Cost 257 final seminar 27-29 September 2000

20. Steady state and transient behavior (cont.) Typical behavior of time evolution of transmission rate of controlled sources using Switch 2 for (a) LAN and (b) WAN configurations. Cost 257 final seminar 27-29 September 2000

21. Network test configuration for demonstrating fairness 3-hop traffic start transmitting at t=0 the one 1-hop-a traffic at switch 0 is next started at t=0.2 the two 1-hop-b sources atswitch 1 are started at t=0.4 the three 1-hop-c sources are started at t=0.6 Cost 257 final seminar 27-29 September 2000

22. fairness - LAN Allocation of bandwidth to Ordinary Sources for LAN. All sources dynamically allocated their fairshare at all times. Cost 257 final seminar 27-29 September 2000

23. fairness - WAN Allocation of bandwidth to Ordinary Sources for WAN. All sources dynamically allocated their fairshare at all times Cost 257 final seminar 27-29 September 2000

24. fairness - WAN Allocation of bandwidth to the Ordinary Sources at Switch 2. Observe that the top 3 figures are for local sources and the last one is for a 3 hop source located about 12000 kms away from the switch. All sources are allocated their fairshare Cost 257 final seminar 27-29 September 2000

25. Behaviour of control • Insensitivity of control to the value of the control update period • 32 celltimes0.085 msec to 353 celltimes1 msec • Robustness of control design constant to changing network conditions • for diverse traffic demands ranging from 50%-140% and source location (feedback delays) up to about 250 msec RTT, as well control periods ranging from 0.085 msec to 1 msec. For all simulations the behaviour of the network remains very well controlled, without any unacceptable degradation Cost 257 final seminar 27-29 September 2000

26. IDCC properties • provablestable and robust behaviour at each port, • and by tightly controlling each output port, overall network performanceexpected to be tightly controlled. • high utilisation with bounded delay and loss performance • good steady state behaviour, with no observable oscillations • good transient behaviour, i.e. fast rise and quick settling times • Uses minimal information to control system and avoids additional measurements and noisy estimates: • Uses only one primary measure, namely queue length • Does not require per connection state information, queuing, or servicing at the switch • Does not require any state information about set of connections bottlenecked elsewhere in network (not even count) • Computes Common Ordinary Traffic allowable transmission rate only once every Ts msec (control update period) thereby reducing processing overhead. • controller fairly insensitive to value of Ts. Cost 257 final seminar 27-29 September 2000

27. IDCC properties (cont.) • Achieves max/min fairness in a natural way without additional computation or information • can guarantee minimum agreeable servicerate without additional computation • works over wide range of network conditions, such as RTT (feedback) delays, traffic patterns, and controller control intervals, without change in control parameters • works in integrated way with different services (e.g. Premium Traffic, Ordinary Traffic, Best Effort Traffic) without need for any explicit information about their traffic behaviour • proposed control methodology and its performance is independent of size of queue reference values. • network operator can be more or less aggressive and steer performance, in accordance with current network and user needs, using global consideration. • Has simple implementation and low computational overhead • features very small set of design constants, • can be easily tuned from simple understanding of system behaviour Cost 257 final seminar 27-29 September 2000

28. Conclusions for IDCC • generic scheme for congestion control. • uses integrated dynamic congestion control approach (IDCC). • specific problem formulation for handling multiple differentiated classes of traffic, operating at each output port of a switch illustrated. • derived from non-linear control theory using a fluid flow model. • analytical performance bounds derived, for provable controlled network behaviour. • divide traffic into three basic types of service, in same spirit as those adopted for Internet Diff-Serv i.e. Premium, Ordinary, and Best Effort. Cost 257 final seminar 27-29 September 2000

29. Conclusions for IDCC (cont.) • As shown earlier, proposed control algorithm possesses a number of important attributes • works in integrated way with different services • has simple implementation and low computational overhead, • features a very small set of design constants that can be easily set (tuned) from simple understanding of system behaviour. • These attributes make proposed control algorithm appealing for implementation in real, large-scale heterogeneous networks Cost 257 final seminar 27-29 September 2000

30. further work for IDCC • In this paperfull explicit feedback was used in the simulations, signalled using RM cells in an ATM setting. • challenging task is to investigate other explicit (e.g. single bit feedback as in ECN proposal for IP) and implicit (end-to-end) feedback and signalling schemes. • A comparative analytic and simulative evaluation between the different feedback and signalling schemes is a topic for future research. Cost 257 final seminar 27-29 September 2000

31. General Recommendations • Advocate a structured and formal approach to designing congestion control systems • could be from other fields with solid theoretical foundation, possibly drawn from stabilising (controlling) large scale, complex systems • encourage collaboration with other disciplines • Integrate with other control functions and study their interactions (e.e. with routing and CAC) • A common simulative framework (CSF) and pilot test bed environment (e.g. ns 2 could be such a simulative test-bed) • with well known and understood scenaria that test the properties of proposed algorithms • e.g. dynamic properties, robustness, large scale deployment aspects, steady state behaviour, and so on… Cost 257 final seminar 27-29 September 2000