Congestion Control. Andreas Pitsillides University of Cyprus. Congestion control problem

Transcription

1 Congestion Control Andreas Pitsillides 1 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 September

2 Current scene despite vast research efforts, still no universally acceptable solutions: G control solutions for TCP transported traffic increasingly becoming ineffective, cannot easily scale up even 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,) September Current scene (cont.) G 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 controls as 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. G 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 September

3 Why problem still not solved? In part, due to lack of structured approach, and lack of strong theoretical foundation in 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 September Why problem still not solved? (cont.) interaction of additional non-linear feedback loops can produce unexpected 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 September

4 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 September 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 bounds derived, for provable controlled network behaviour September

5 λ r () t Allowed common rate sent to the Ordinary Traffic sources Control concept Integrated Dynamic Congestion Controller (IDCC) x () p t x () t C () t r p ref x () t p ref x () t r references Premium Traffic λ p() t Cp( t) Fixed service rate C server (e.g. 155 Mb/s) Incoming traffic Ordinary in Traffic λ () t r Cr( t) Scheduler with server buffer Best effort traffic λ () t b Instantaneous left-over capacity September Dynamic model For a packet buffer: x ( t) = f ( t) f ( t) For M/M/1 queue out + in xt () x () t = C() t +λ() t 1 + xt ( ) September

6 Simulative comparison x(t), queue length OPNET simulation fluid flow model solution time (s) September Another dynamic fluid flow model for TCP window: 1 W( t) W( t R( t)) W () t = p( t R()) t Rt () 2 Rt () Nt () xt () = Wt () C Rt () server xt () R() t = + Tp C server September

7 Developed Control strategy Premium Traffic Service (eq. 1, 2, 3) 1 + xp ( t) Cp() t = max0, min Cserver, ρp() t αp xp() t + kp() t xp () t 0 if x p ( t) 0.01 ρ p( t) = 1.01 xp( t) 0.01 if 0.01 < xp( t) 1 1 if x p ( t) > 1 Ordinary Traffic Service (eq. 4) k p() t = Pr δ pxp() t xr () t λr() t = max0, min Cr(), t Cr() t αrxr() t 1 + xr ( t) September 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 September

8 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 September Simulative evaluation September

9 Steady state and transient behavior Qureue length ref=100 ref-=50 Ref=100 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 September 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 msec to 353 celltimes 0.94 msec) September

10 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 September 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 September

11 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= September fairness - LAN 3-hop 1 hop-a 1 hop-a 3-hop 1 hop-a 3-hop 1 hop-b 1 hop-b 3-hop 1 hop-c Allocation of bandwidth to Ordinary Sources for LAN. All sources dynamically allocated their fair share at all times September

12 fairness - WAN 3 hop 1 hop-a 1 hop-a 3 hop 1 hop-a 3 hop 1 hop-b 1 hop-b 3 hop 1 hop-c Allocation of bandwidth to Ordinary Sources for WAN. All sources dynamically allocated their fair share at all times September 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 kms away from the switch. All sources are allocated their fair share September

13 Behaviour of control Insensitivity of control to the value of the control update period 32 celltimes 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 msec to 1 msec. For all simulations the behaviour of the network remains very well controlled, without any unacceptable degradation September IDCC properties provable stable and robust behaviour at each port, and by tightly controlling each output port, overall network performance expected 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 T s msec (control update period) thereby reducing processing overhead. controller fairly insensitive to value of T s September

14 IDCC properties (cont.) Achieves max/min fairness in a natural way without additional computation or information can guarantee minimum agreeable service rate 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 September 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 September

15 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 September further work for IDCC In this paper full 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 September

16 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 September