Adaptive Fuzzy Logic Control of ABR Traffic Flow in ATM Networks

Transcription

1 Chapter 5 Adaptive Fuzzy Logic Control of ABR Traffic Flow in ATM Networks In this chapter the operation of the adaptive fuzzy explicit rate marking (A-FERM) congestion control algorithm is discussed. The A-FERM congestion control algorithm is built on FERM and brings adaptation capabilities to the scheme in order to obtain better network throughput under locally varying traffic conditions experienced by the ATM switches. 5.1 Adaptive Control Concepts and Adaptive Fuzzy Logic Controllers Fuzzy logic controllers are expected to work in situations where there is a large uncertainty or unknown variation in the parameters and structures of the system under control. Generally, the fundamental aim of adaptive control is to maintain consistent performance of a system in the presence of these uncertainties. Definition of the meaning of adaptive systems and adaptive controllers is difficult and there is no general consensus in the scientific community on such a meaning. The pragmatic definition of Aström [AW89] describes the structure of the con- 70

2 71 trol system used in this study: Adaptive control is a special type of nonlinear feedback control in which the states of the process can be separated into two categories, which change at different rates. The slowly changing states are viewed as parameters. This introduces the idea of two time scales: a fast time scale for the ordinary feedback and a slower one for updating the regulator parameters. Adaptive fuzzy logic controllers are constructed from fuzzy logic controllers which are equipped with adaptation algorithms for parameter updates. The most important advantage of adaptive fuzzy logic controllers over conventional adaptive control is that adaptive fuzzy logic controllers are capable of incorporating linguistic information from human operators or experts, whereas conventional controllers cannot. This attribute is especially important for systems with a high degree of uncertainty, such as chemical processes, aircraft, and so on, because even though these systems are difficult to control from a control theoretical point of view, they are often successfully controlled by human operators. The control actions can be expressed in fuzzy terms and some linguistic descriptions about the system behavior under various conditions. Although these fuzzy control rules and descriptions are not precise and may not be sufficient for constructing a successful controller, they provide very important information about how to control the system and how the system behaves. Adaptive fuzzy logic control provides a tool for making use of the fuzzy information in a systematic and efficient way. Adaptive fuzzy logic controllers are classified according to two criteria [Wan94, Page 104]: whether the adaptive fuzzy logic controller can incorporate fuzzy control rules or fuzzy descriptions about the system, and whether the fuzzy logic systems in the adaptive fuzzy logic controller are linear or nonlinear in their adjustable parameters. The adaptive fuzzy congestion controller used in the A-FERM algorithm directly incorporates fuzzy control rules and its adjustable parameters are linear. Detailed

3 72 Figure 5.1: Block diagram of the Adaptive Fuzzy Congestion Controller. It is very similar to Fuzzy Congestion Controller (Figure 4.1) with the exception of the existence of adaptive law block in the Adaptive Fuzzy Congestion Controller. information and derivations of the governing equations of the adaptive fuzzy congestion controller are provided in Appendix C. 5.2 Adaptive Fuzzy Explicit Rate Marking (A-FERM) Congestion Control Algorithm Operation of A-FERM algorithm is the same as FERM scheme explained in Chapter 4, with the exception that the congestion controller used in A-FERM has adaptive capabilities. As shown in Figure 5.1 the general structure of the adaptive fuzzy controller is an extension of the non-adaptive controller (Figure 4.1) with an additional block to monitor queue length error and queue growth rate, and make adjustments to the membership functions.

4 5.3 Implementation of the Adaptive Fuzzy Congestion Controller 73 The theoretical principles, derivation of the governing equations, and design steps of the Adaptive Congestion Controller are presented in Appendix C. The control objective of the Adaptive Fuzzy Congestion Controller is the same as its non-adaptive version; to regulate the ABR queue length around a reference value. The design steps (as in Appendix C) are as follows: Off-Line Processing 1. We first determine the controller parameters. We decide to track ABR queue length error e = q ref q ABR and its first derivative. Therefore e = ẹ e 2. Then, to ensure the stability of the system, we fix the controller parameters k = 2 1 to have all roots of the polynomial h(s) = s 2 + k 1 s + k 2 in the open left-half plane. 3. We choose a positive definite matrix Q = and, by solving Equation 28 of [Wan93] we obtain P and take its last column p n =

5 74 4. We specify the following values for the design parameters M θ = 2.9 γ = 0.1 M x = 1024 M u = 1 T s = 32 Cell Service Period = µs (on an OC-3 link) Initial Controller Construction 1. We specify a set of linguistic rules representing the human expert s qualitative knowledge. For the initial set of rules, the linguistic rules of the non-adaptive Fuzzy Congestion Controller are used (Table 4.2). 2. We determine the definitions (membership functions) of the linguistic values of the input and output variables used in these rules (again, definitions shown in Table 4.3 used in non-adaptive version are taken without any modifications). The initial set of linguistic rules and definitions of the variables specify the vector of regressors ξ(x). 3. Initial values of the adaptive parameters are chosen as ] θ0 [1 T = On-Line Operation and Adaptation On-line operation and the process of adaptation of the parameters can be summarized as the following algorithm: 1: k 0, θ θ 0 2: loop 3: Run the system, measure the ABR queue length: q ABR k 4: Determine the ABR queue growth rate: q ABR k 5: if k > 0 then 6: q ABR k q ABR k q ABR k 1

6 75 7: else 8: q ABR k 9: end if q ABR k 10: Calculate ξ(q ABR k, q ABR k ) 11: Calculate the flow rate u f (x θ) (Equation C.8) 12: if ABR traffic demand exists then 13: Use adaptive law, θ k 1 and queue error information to calculate the new estimate θ k (Equation C.20) 14: end if 15: k k : end loop 5.4 Performance Evaluation The initial control surface (which is same as the one used for the non-adaptive scheme (FERM)) is shown in Figure 5.2(a), and the shapes of the control surfaces (after adaptation) at the ATM switch 0, 1 and 2 are shown in Figures 5.2(b), 5.2(c) and 5.2(c) respectively, after 1 second of simulation time. The figures show that the control surface is modified relative to the initial surface, and is different at each switch. In particular switch 2 has a significantly different control surface from switches 0 and 1, presumably because it is the focus of more congestion. It is interesting to compare the adapted surfaces with the comments of Section 4.2.3, discussing the original intent of the linguistic rule base. After adaptation, two conservative features of the original design have been reduced: The region over which decrease flow rate sharply operates is much smaller after adaptation (the flat region at the base of Figure 5.2(a)). This seems to suggest that the original design was unduly cautious in this respect. The small flat region in the middle of Figure 5.2(a) is not present in the adapted control surfaces. This small flat region corresponded to the middle rule for steady state if queue length is acceptable and not changing then do not change flow rate. It seems likely that this status quo rule is not operationally important, since the flow rates in reality are volatile. Although the

7 76 rule provides a useful intuitive center point, it does not justify a significant plateau of its own. The overall performance of the ATM LAN and WAN is presented in Figures 5.3(a) and 5.3(b). These can be compared with the performance of the non-adaptive scheme shown in Figures 4.6(a) and 4.6(b). This shows some improvement in performance of the WAN, with respect to 3-hop traffic. Throughput of 3-hop traffic is greater with the adaptive scheme over the full range of offered link load. The performance of other traffic streams does not appear to have suffered as a result of this improvement. The simulations of the A-FERM congestion control algorithm had shown that making an adaptive control scheme to work within a fairly complex networking scenario has been quite a challenge because of the limits of the available computational resources. In order to observe that the 15 adaptive parameters of the adaptive fuzzy congestion controller stabilized, experiments simulating the network for longer than 1 second of the simulated time have been required. Due to the virtual memory limits, the computers 1 available to this study have not allowed longer simulations to be run. Because of this problem, simulations had to be stopped before all of the adaptive parameters stabilized. It could be reasonably expected that even better throughput results could be obtained if the experiments are repeated in the future when better computational resources become available. 5.5 Discussion and Remarks The simulation results clearly show the ability of the adaptation algorithm to modify the controller behavior in such a way (differently at each switch) as to provide better network throughput over the performance of the non-adaptive scheme. Adaptation algorithm has been able to make subtle but significant changes to the shape of the control surface. These changes have in particular improved the performance of the most difficult traffic flow the long distance flow (3-hop) in the 1 Sun SparcStation 20 with 256 MB RAM, 512 MB virtual memory capacity, running Solaris 2.6 Operating System.

8 1 1 Flow Rate Correction 0 Flow Rate Correction Queue Length Error Queue Growth Rate Queue Length Error Queue Growth Rate (a) Initial structure of the control surface. (b) Adaptive FCC of ATM switch Flow Rate Correction 0 Flow Rate Correction Queue Length Error Queue Growth Rate Queue Length Error Queue Growth Rate (c) Adaptive FCC of ATM switch 1. (d) Adaptive FCC of ATM switch 2. Figure 5.2: This diagram shows the evolution of the control surfaces at the ATM switches: (a) Initial shape of the control surface of fuzzy congestion controllers in the ATM switches; (b), (c) and (d) The shapes of the control surfaces after a 1 second of simulation. For this simulation, the offered ABR traffic loads are taken as 80% of the link capacities. 77

9 ATM LAN Performance (A-FERM Congestion Control Algorithm) (150%) link 1hop_a link 1hop_b link 1hop_c link 3hop (150%) average cell delay (ms) (150%) (20%) throughput (Mb/s) (a) ATM LAN ATM WAN Performance (A-FERM Congestion Control Algorithm) (150%) link 1hop_a link 1hop_b link 1hop_c link 3hop (150%) average cell delay (ms) (150%) (20%) throughput (Mb/s) (b) ATM WAN Figure 5.3: Plot of average end-to-end cell delay vs useful throughput of simulated ATM network under A-FERM congestion control. The graph has been produced by varying the offered link loads generated by ABR traffic sources from 20% to 150% of the link capacities.

10 79 large propagation delay network (the WAN). It is expected that performance would improve further if A-FERM were running freely online. Also, by implication it seems highly likely that A-FERM would adapt well to changing traffic conditions and/or changed parameters from higher levels of the control hierarchy.

11 Chapter 6 Concluding Remarks and Directions for Future Research The proposition addressed in this thesis is as follows: As discussed earlier in Chapter 2, the complexity of ATM networks and the services intended to be supported on these networks require integrated, multilevel control structures. Designing an effective control structure for these networks is known to be difficult because of the complexity of the structure of the networks, nature of the services supported, and the variety of the dynamic parameters involved. In addition to these, the uncertainties involved in identification of the network parameters lead to difficulty in obtaining realistic and cost effective analytical models of these networks. Classical control system design methods rely on availability of these models. The difficulty of obtaining analytical models renders the use of classical control techniques difficult at best, and possibly invalid and/or non-cost effective. This is what motivates the interest in non-analytical control system design and modeling schemes which have the potential to be powerful and cost effective as alternative design tools. Abundant research in various control fields demonstrates that control systems based on Computational Intelligence (fuzzy logic, artificial neural networks and evolutionary computation) have the ability to cope with these difficulties and to offer creative and effective techniques to supplement traditional control approaches. The aim of this study has been to demonstrate the suitability of fuzzy logic 80

12 81 based control techniques for designing superior control structures for ATM based multimedia networks. In order to do this, firstly, it presents the outline of a hierarchical control architecture for ATM networks in Chapter 2. Secondly, as part of this architecture, a formulation is proposed for a fuzzy logic based explicit-rate congestion control scheme using short-term predictions of the behavior of the queues at the ATM switches. This scheme is described in Chapter 4. Simulative evaluations presented in Chapter 4 show that the scheme keeps the queues well controlled, not only minimizing the cell loss, but also keeping the link utilization close to the optimum levels. This applies over a wide range of loads, including offered network loads exceeding the full carrying capacity of the links, and for both ATM WAN and ATM LAN network models. For offered traffic loads in excess of 100% of link capacity, the effective network utilization remains at unity, whilst for offered link loads up to 100% the end-to-end delays are very close to the propagation delay (about 13 ms for 3-hop traffic in the case of the ATM WAN). As expected, for offered link loads beyond 100%, the end-to-end delays are dominated by admission delays (for the 3-hop traffic they increase to about 70 ms, for an offered link load of 140% at a sustained overload period of 1 s). The study shows that the FERM scheme described in Chapter 4 is well suited to ATM network congestion control in both an ATM LAN and an ATM WAN environment, in the presence of competing delay-sensitive traffic, without the need for changing or re-tuning the control law. As is evident from the simulations, the same control algorithm achieves very good delay and throughput performance in steady-state (much better than the case of no control or the case with EPRCA control) in both the ATM LAN and the ATM WAN. Its transient behavior is excellent exhibiting low latency, quick rise time and quick settling time. FERM also achieves fairness in a max-min sense without the need for any information from either the sources or other switches in the network. In order to improve the responsiveness of FERM, and to ensure that the algorithm remains optimally tuned under wide ranging network conditions, an adaptive version of FERM has been proposed and demonstrated in Chapter 5 (A-FERM). The adaptive algorithm trades off complexity for improved performance. In the next section, an overview of the salient features of the FERM and A-

13 82 FERM congestion control algorithms is presented. 6.1 Properties of the FERM and A-FERM Congestion Control Algorithms Any suggested control scheme must be effective and offer certain control features which will make it attractive for implementation purposes. FERM is effective and compares favorably with the following desirable features (some of which have been discussed by a number of researchers, e.g. [BF95, Jai95, RBO95]): Robustness Results of the simulations show no collapse in throughput, even in the presence of severe overloads and competing delay-sensitive traffic of unknown density. To the contrary, at a link load factor of 150% of the link capacity, the total network throughput is close to a maximum possible of 300 Mb/s (i.e., almost 100% network utilization). Its inherent robustness is also consistent with the robustness claims in the vast literature on fuzzy logic control. The ultimate test for any method of control is successful applications in industry. Fuzzy logic control is a prime example of a theory which is successful in solving difficult control problems in real practice. The good performance of FERM and A-FERM is in agreement with these generally successful outcomes in other fields. Efficiency Simulation results demonstrate the efficiency of FERM (e.g., close to 100% throughput at a link load factor of 150% of the link capacity). Behavior under both steady-state and transient network conditions the simulation results demonstrate the excellent performance of FERM in both steady-state and transient conditions. Implementation complexity There is no need to place a fuzzy inference engine (which is the most computationally intensive part of any fuzzy logic control scheme) in a real switch. After selecting appropriate fuzzy sets and rule base, and tuning the rules with a simulator, the control surface is known (Figure 4.2) and can be stored as a lookup table for fast execution [Bon92]. FERM

14 83 can be implemented in this way, requiring only a few kilobytes of ROM space and a simple interpolation algorithm. That is, it offers ease of implementation with a very fast response. A-FERM can also be implemented quite easily by the addition of multipliers to the interpolation algorithm. These multiplier values are calculated periodically in a separate process. Fairness The fairness of FERM (at least in a max-min sense) is demonstrated in Section Note that fairness is achieved without the need to keep track of any other than local information. (e.g. there is no need to track the bottleneck rates of individual sources, or congestion state of other switches.) Ease of tuning As discussed earlier, since the control information is given in terms of linguistic rules (Table 4.2), it is reasonably easy to relate this to desired control performance. This must be contrasted with the tuning of other schemes, as for example the EPRCA scheme (Tables D.1 and D.2), which requires the tuning of a number of parameters bearing no easy association to control performance. Scalability As shown in the performance evaluation section (Section 4.3), the FERM scheme is capable of operating in an ATM LAN or ATM WAN without the need to modify the control parameters. Here, scalability is taken to mean the end-to-end distances involved in the network. Inter-working with other schemes The scheme can be implemented by using Explicit Down Switches (EDS). Since the algorithm is fully decentralized, with the ER calculated at each switch based on local measurements, it can coexist with any of the other algorithms. Policing of connections Since the ER is placed in an RM cell, this can be provided to an appropriate policing unit at the edge of the network which will ensure that the maximum allowed rate is not exceeded by the source. Minimum Cell Rate (MCR) FERM supports implementation of MCR which ensures that a certain minimum portion of the network bandwidth is always provided to the connection.

15 84 The simulation results clearly show the ability of fuzzy logic as a convenient and effective design method to build a control algorithm without relying on formal models of the controlled system and control theoretic tools. Furthermore, the adaptation algorithm incorporated into the A-FERM scheme adds the capability of modifying the controller behavior differently at each switch for responding to local differences at network nodes. This leads to providing better network throughput relative to the performance of the non-adaptive scheme. 6.2 General Conclusion Regarding the Effectiveness of Fuzzy Logic Control for Network Control Problems This study is consistent with the ample evidence of positive research results reported in a wide range of application areas. The form of fuzzy logic controllers makes them well suited for use in hierarchical systems using hybrid control structures that incorporate classical control modules along with computational intelligence based ones. This study suggests that the power of fuzzy logic control to convert intuitive ideas into effective control systems is well suited to the hierarchical control requirements of ATM based multimedia networks. The vision for this work is that an effective and intuitively appealing hierarchical control structure may lead to much better resource utilization in ATM based multimedia networks and justifies longer term research into design and performance evaluation of the type of control paradigms discussed in the next section. 6.3 Focus of Future Research: Integrated Control of ATM Networks via Hierarchical Fuzzy Logic Control Research on applications of fuzzy logic in telecommunication systems, and particularly in ATM networks, is being pursued by an active research community, and methods are being developed simultaneously. However, unlike consumer applications, there are no commercially deployed applications as yet. The reason could be