Congestion Control in ATM Networks using Artificial Intelligence Techniques Guiomar Corral, Agustín Zaballos, Joan Camps, Josep M.

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1 Congestion Control in ATM Networks using Artificial Intelligence Techniques Guiomar Corral, Agustín Zaballos, Joan Camps, Josep M. Garrell Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Catalonia, EUROPE Paseo Bonanova, 8, Barcelona Tlf: , Fax: {guiomar, zaballos, joanc, josepmg}@salleurl.edu Abstract Nowadays high-speed transmissions and heterogeneous traffic are some of the most essential requirements that a communication network must satisfy. Therefore, the design and management of such networks must consider these requirements. Traffic and congestion control techniques are decisive to a successful operation of ATM networks. They support different types of services and this fact makes them less predictable networks. Congestion can be defined as a state of network elements in which the network cannot guarantee the established connections the negotiated QoS. This paper proposes a system to reduce short-term congestion in ATM networks. This system uses Artificial Intelligence techniques to predict future states of network congestion in order to take less drastic measures in advance. Introduction The design, configuration and management of real communication networks are phases that must be taken into account in order to achieve network performance objectives. These objectives include high-rate transmissions and heterogeneous traffic. Asynchronous Transfer Mode (ATM) networks can fulfill these requirements with very high rates and different types of services. On the other hand, they are less predictable networks [1]. The types of traffic patterns imposed on ATM networks, as well as the transmission characteristics of those networks, differ markedly from those of other switching networks [5]. One reason is due to the applications that may generate very different traffic patterns and they require different network services. Another reason for this is that trusted statistics from traffic characteristics and connection establishment patterns aren t given. ATM networks also guarantee the engaged Quality of Service (QoS), so when a connection is accepted, the network must fulfill all the agreements reached with the user. In effect, the network and the subscriber inter into a traffic contract. But due to the statistical multiplexing of the traffic, an inherent feature of ATM, it is possible to have congestion even though all connections carry out their contracts. ATM congestion can be defined as a state of network elements in which the network is not able to guarantee the negotiated Network Performance objectives for the already established connections [2]. Congestion can be caused by unpredictable statistical fluctuations of traffic flows or fault conditions within the network [2]. Since an ATM network supports a large number of bursty traffic sources, statistical multiplexing can be used to gain bandwidth efficiency, allowing more traffic sources to share the bandwidth. But if a large number of traffic sources become active simultaneously, severe network congestion can result [3]. Without traffic and congestion control techniques, traffic from user nodes can exceed the capacity of the network, causing memory buffers of ATM switches to overflow and leading to data losses. [3] Congestion control in ATM networks presents difficulties not found in other types of networks [3]. Thus, and ATM network must implement an effective traffic management in order to guarantee the QoS. So this traffic management should avoid not only long-term congestion but also short-term congestion. The former takes place when network load overcomes network resources and can be avoided with an accurate network resource planning and preventive controls like Call Admission Control (CAC). The latter lasts less than the roundtrip delay and it is difficult to foresee because of dynamic variations of cell interarrival times due to bursty traffic sources. However, contention techniques could minimize its consequences [4]. This paper proposes a system to reduce short-term congestion in ATM networks. This system uses Artificial Intelligence (AI) techniques to predict future states of network congestion. An existing buffer control algorithm [4] is used. Using historical data files and AI techniques, a synthetic-generated mathematical function (a model) is created. This model is used to generate several input parameters of the buffer control algorithm in order to adapt its behavior to the situation. Results show that the system improves network performance, including heavy load situations. This paper is organized as follows. The first section introduces and locates the problem that is going to be solved. The second section focuses on the congestion concept with congestion control schemes. The third section describes the implemented solution, which predicts short-term congestion using AI techniques. In the fourth and fifth sections, the simulation model and results are given. Finally, results, conclusions and further work are presented. 1

2 Problem analysis Congestion occurs when network performance falls off dramatically [5]. Moreover, in ATM networks, congestion is defined as the network state in which the necessary resources to provide the QoS contracted by the users are not available [2]. This QoS, which must be assured, is negotiated in the connection establishment [2]. Monitoring ATM networks performance for traffic and congestion control purposes leads to difficulty because there are some parameters whose values must be tuned. These parameters are not only based on users traffic descriptors but als o on the different traffic control policies implemented [6]. Congestion control schemes can be classified depending on the congestion duration. The longer the congestion duration, the wider the perspective that will be used to control it. Some of the control mechanisms, arranged in order of congestion duration, are the following: network design, Connection Admission Control (CAC), routing, traffic shaping, end-to-end feedback, hop-by-hop control and buffering. CAC, used during the call set-up phase, determine whether a given new connection can be accommodated. A new connection request in an ATM network must contain traffic descriptor parameters that CAC uses to determine whether the connection may be established. By accepting the connection, the network forms a traffic contract with the user. Later on, Usage Parameter Control (UPC) will monitor the connection to determine whether the traffic conforms to the traffic contract [5] [6]. Priority control is useful when end-systems can generate different priority traffic flows using the Cell Loss Priority (CLP) bit of the ATM cell header [7]. Then, the network may selectively discard cells with low priority, if necessary, in order to protect network performance for high priority cells. Traffic shaping is a mechanism that alters cell stream traffic characteristics to ensure its conformance, whereas feedback is used to regulate the traffic submitted according to the network s state. Different performance parameters can be used to monitor ATM QoS, like Cell Loss Rate (CLR), Cell Misinsertion Rate, Cell Error Ratio, Cell Transfer delay (CTD), Cell Delay Variation and so on [2]. But the most important parameters related to congestion are CLR and CTD. However, since speeding up the ATM switching highway and the transmission link can satisfy CTD requirements, only CLR will be used [4]. [4] proposes a priority control for cell loss quality that attempts to tolerate short-term congestion situations at the expense of the source's low-priority traffic, which has the least restrictive traffic descriptors. In fact, this mechanism could be classified as a Partial Buffer Sharing policy with a priority control. Generally, a Partial Buffer Sharing policy, in contrast to Complete Buffer Partitioning policy, drops an incoming cell only if the total number of cells in the buffer exceeds a given threshold [8]. This means that a cell is only discarded if there is not enough free space in the buffer. Figure 1: A Partial Buffer Sharing Policy with three defined thresholds It is very important to focus on the pursued goal, which consists of discarding the minimum cells whose CLP field is disabled by UPC. Congestion would cause the cells that had been previously accepted by UPC to be discarded, and this is what must be avoided. In the end, the cells tagged as nonconforming cells are those that must be dropped first if congestion occurs. If the system discards cells with enabled CLP field before a heavy congestion situation happens, the amount of discarded cells with enabled CLP could be minimized. Therefore, accurate thresholds should be computed. Then, when the utilization thresholds are exceeded, low-priority cells are discarded. But these thresholds are static values, not dynamic. Thus, they cannot adapt to different network situations. Our proposal improves threshold selection by using AI techniques to predict a future short-term congestion based on the temporary evolution of the buffer utilization. Short-term Congestion Prediction using AI Techniques In order to predict short-term congestion in a network, AI techniques have been implemented. First of all, the prediction goal and input data must be explained. Then, the AI technique will be introduced, focusing on its application in our problem. Prediction Goal and Input Data The system should predict when a short-congestion situation is going to happen. If this moment is foreseen before it happens, some measures can be taken in advance to avoid this future and undesirable situation. The system can predict it, using information based on the temporary evolution of the buffer utilization. AI techniques learn from past situations and apply their knowledge to predict new situations. The AI technique used is Genetic Programming (GP) [9]. GPs are a machine learning method useful for prediction model generation. In this case, it will predict the future buffer utilization. Like the majority of machine learning methods, GPs need a representative set of problem samples to be trained. Once the training phase is completed, the output (the model) is used to make the prediction. 2

3 The test bed used to train our GP is made up of a set of samples, where every set of samples is included in a file. These samples come from previous network simulations obtained from OPNET. Thus a file contains a time-ordered list of buffer utilization states. The GP system needs a data set that reflects not only the past and present situation, but also the future buffer utilization, in order to achieve a good training. Therefore, the data set will be fragmented in windows and every window will be processed as a new sample. Every window includes an account of buffer utilization in the previous instants as well as the future buffer utilization. The account is summarized in intervals and every interval is represented by the maximum, the minimum and the mean buffer utilization. Figure 2 depicts a data set and its fragmentation. It illustrates the buffer utilization along simulation time and how it is fragmented in different intervals. samples correspond to the samples of a measurement interval (). Some samples will be used to predict the future utilization of a prediction interval (IP). The distance between samples and the prediction interval is DIP (Prediction Interval Distance). In this case, data fro m four windows is used to predict the future buffer utilization. Buffer utilization NI Windows DIP IP Mean Prediction Figure 2: Buffer utilization and fragmentation. We must consider that there are different traffic sources and they can generate traffic of different priorities. So the intervals will contain the utilization percentage for every priority. On the other hand, the future interval buffer utilization will only be represented by the utilization mean. Future buffer utilization will be the predicted value. Consequently, a file containing buffer utilizations for every traffic priority in a period of time will be given to GP. Genetic Programming Approach As stated before, Genetic Programming [9] has been chosen to predict ATM node queue utilization. This is an AI technique related to evolutionary computation, which is inspired by Charles Darwin s theories on evolution and by Gregor Mendel s genetic inheritance, where the existence of a population is simulated and it evolves as time goes by. t The basic GP algorithm imitates nature simulating a population of individuals that clone, recombine with others or disappear. The better-adapted individuals to the environment are those that have more possibilities of survival and become progenitors of the new generations of individuals. The population spreads toward a group of better individuals, due to these transformations and to natural selection. The population's global tendency will maintain and replicate the important information, while it will lose the less representative one. In evolutionary computation, the environment is the outlined problem that wants to be solved. Likewise, each one of the population's individuals is a potential solution. The better the solution, the more possibilities it has to survive from one generation to another. GP considers that each individual is a program that can solve the outlined problem. Thus the population is considered as a group of programs that will be possible solutions. From one generation to another, these programs will be cloned, crossed and mutated. Among all the programs, only the selected ones will be used. This selection depends on the fitness of each program, on the similitude between the desired solution and the obtained solution for each program. In the end, a group of sufficiently good programs is obtained and one of those is chosen, usually the best one. Symbolic regression has been the strategy used to obtain a GP prediction model, so specific formulas that solve the proposed problem can be obtained. Then, a formula will be used to predict the mean utilization of an ATM node queue. With symbolic regression, the population's individuals are reduced to formulas. The fitness population formula is evaluated according to the prediction success. The formulas that achieve a more accurate prediction of the prospective utilization will have more probability of persisting. The obtained formulas that predict mean buffer utilization after DIP contain some of the parameters listed in Table 1. These parameters must be calculated using the history of buffer utilization. One example of a formula given by the GP is the following: Future Mean Buffer Utilization = ( P3 / P13 ) P8 + P16 + P17 Historical Maxim um value Minim um value Mean %Low priority %High priority Window 1 P1 P2 P3 P4 P5 Window 2 P6 P7 P8 P9 P10 Window 3 P11 P12 P13 P14 P15 Window 4 P16 P17 P18 P19 P20 Table 1: List of parameters that can appear in the GP prediction model 3

4 Genetic Programming Approach The AI algorithm will predict congestion situations based on the buffer utilization; thus, a mechanism that works closely with the buffer should be chosen. [4] is a UPC mechanism that monitors the traffic behavior generated by the user's sources and follows a performance policy. To determine the performance of the new proposal a simulation environment in Mil3 OPNET has been programmed. The scenarios and testbed implemented are explained in this section. Simulation OPNET has been used to develop a simulation model for the congestion control. In fact, several models have been simulated in order to make a comparison between them. bursty distribution. The first one produces high priority traffic and the second one generates low priority cells. Queue: This process is a programmable FIFO buffer queue of an ATM device. Marker_Low and Marker_High: These processes tag all the cells that don t fulfill the traffic parameters negotiated in the connection establishment. It was necessary to program this process because the algorithm works closely with UPC techniques but OPNET sources do not tag the CLP field. It implements the Jumping Window algorithm [10]. Both markers have the same state machine (Figure 4), where the Marker state will be used to tag the cells that overcome the authorized number of cells. Firstly, a congestion control model without using AI was developed. This model proposes a priority control that eliminates cells to avoid the short-term congestion [4]. This is a reactive control that eliminates low-priority cells when a queue becomes congested. This implementation consists of two steps. First of all, the cells that don t fulfill all the traffic parameters and their field CLP is enabled will be discarded if congestion overcomes a threshold. The second step consists of discarding cells of a low-priority service when congestion overcomes a second threshold. Thus, any network node will detect the congestion situation monitoring its buffer utilization. The simulation model uses two traffic sources that generate traffic of different priorities: low-priority and high-priority cells. It also contains a queue that stores the cells and its utilization is going to be monitored. Then, a sink is used to manage the outgoing traffic. Finally, between the sources and the queue there is a process that manages and applies the congestion control. Figure 3 depicts the node model which implements [4]. Figure 4: Process Model of Marker Performance: This process centralizes link performance statistics in order to obtain graphics and results. Sink: This process is the cell sink that avoids the collapse of the simulation system. Eliminator: This process implements [4]. Its Finite State Machine is shown in figure 5. Figure 3: Node Model The node mo del of Figure 3 has several processes. The processes that form the control congestion node are the following: Generator_High and Generator_Low: these sources generate ATM traffic following almost any probabilistic or Figure 5: Process Model of Eliminator Depending on previously defined thresholds, [4] always eliminates fewer high-priority cells than low-priority cells. Several types of sources and traffic loads have been used to 4

5 implement different simulations. Thresholds, which have been calculated previously, will determine a CLR for each traffic type. Later on, this simulation model was adapted using AI to control the congestion. Another node model was designed, which is shown in Figure 6. This model contains a new process, called Out, that monitors the number of cells that come into the queue or leave the buffer. It also monitors the priority of these cells and this fact will be useful to obtain all the statistics and graphics. disabled CLP. The first threshold X1 is used to implement a hysteresis cycle avoiding oscillation. MAX_BUFFER Buffer Length X3 X2 X1 X3 X2 X1 Threshold Policies X3 X2 X1 Figure 8: Threshold policies applied to the buffer Figure 6: Modified Node Model The Eliminator process had to be modified in order to adapt the system to the use of AI information. This new process calculates the estimated buffer utilization using the obtained prediction model and carries out an appropriate threshold policy. Traffic Sources Applications on ATM networks may generate very different traffic patterns, including constant-bit-rate and variable-bitrate sources. Thus, congestion control techniques should handle fairly such variety of sources and simulated models should use them. Then, different traffic patterns are used in every simulation allowing us to study a high number of real cases. In several simulations, ideal sources have been used. These sources are implemented by OPNET with the Ideal Generators. In this case, our simulations have worked with persistent and stochastic sources. A persistent source uses a constant distribution and data generation speed is constant. On the other hand, stochastic sources transmit depending on a certain probability distribution. In our case, exponential and normal distribution stochastic sources have been used. Also fluid sources have been implemented for our simulations. Bursty sources are characterized by an on-off behaviour because they alternate between active and idle periods. On-off models have been usually adopted in order to simulate real traffic sources [11]. In active periods, cells are generated at a peak bit rate, whereas in idle periods no cells are generated. As Ideal Generators don t follow this behaviour, a new process model was designed and included in our traffic generators. This process model is shown in Figure 9. Figure 7: Modified Process Model of Eliminator In relation to the prediction value, the static thresholds of [4] will be modified. Thus, the process that executes the prediction formula, should also calculate its parameters. Depending on the prediction, thresholds will be more or less restrictive, that is to say, the node will drop cells sooner or later. Buffer length and threshold policies are used to settle on the instant when the node starts dropping cells. These thresholds are called X1, X2 and X3. When buffer length overcomes X2, any cell with enabled CLP is discarded. If, far from improving, the congestion level increases, even overcoming X3, the system will discard low-priority cells with Figure 9: Bursty sources Simulation Results Both control congestion models ([4] and AI application) have been simulated using a test-bed set under different conditions of traffic sources and network load. These different conditions allow us to perform a qualitative comparison between them. 5

6 Therefore, a measurement to evaluate both models and compare them is needed. The chosen measurement has been the number of high-priority and low-priority cells discarded by both algorithms. Both algorithms fulfil initial specifications, in other words, the number of low priority cells dropped is bigger than the number of high priority cells. cells. One trace of each figure shows original algorithm results and the other one shows modified algorithm results. Previous simulations and also Figure 10 analysed all discarded cells. Now, if only CLP0 discarded cells are taken into account, similar results are obtained. These results are shown in Figure 11. The best results to analyse are obtained in situations of heavy load. If there is not much traffic in the network, a congestion situation won t take place, so it won t make much sense to apply a congestion control method. Then, Figure 10 illustrates buffer utilization employed in our simulations. It shows a scenario of heavy load with the queue almost full during the simulation time. The lower graphic in Figure 10 depicts the number of cells generated by bursty sources, not only lowpriority but also high-priority cells. Figure 12: The upper figure shows dropped low priority cells and the lower figure shows dropped high priority cells. One trace of each figure shows original algorithm results and the other one shows modified algorithm results. Figure 12 enlarges Figure 11 and it focuses on the point where the algorithm with AI improves the original one. From this point, the enhanced algorithm eliminates a smaller number of cells than the first one [4]. Figure 10: The upper figure shows buffer utilization and the lower figure shows traffic generated by bursty sources. The following figures show the performance of the different approaches, comparing the number of dropped cells of every priority. Figure 11 points up that, in heavy load situations, our proposal eliminates fewer cells than the other one. However, in poor load situations, the original algorithm drops a barely noticeable number of cells than the modified ones. Figure 11: The upper figure shows dropped low priority cells and the lower figure shows dropped high priority Figure 13: Focus on the intersection point. Conclusion To predict and control short-term congestion in ATM networks, a control method using Artificial Intelligence techniques has been introduced in this paper. After an exhaustive analysis over previous papers, a UPC algorithm that works with buffer utilization has been chosen. This algorithm has been adapted in order to improve the congestion control. The prediction of the future buffer utilization is the main goal of the new algorithm. This prediction will be obtained from the results of a formula given by AI. Using the account of buffer utilization and AI techniques, this mathematical function is created. The new algorithm uses this formula in order to foresee future states of network congestion. Then, it will apply this new knowledge with the purpose of minimizing the congestion effects. 6

7 Another goal has focused on obtaining a qualitative evaluation of AI application in ATM networks congestion control. Furthermore, it was important to determine its viability and effectiveness. Finally, it has been proved that AI contributes, in general, to the improvement of the mechanism capabilities in which it is applied. It is important to find out which of the processes used to control ATM congestion are likely to be improved by AI. In our concrete application case, the AI learning process could be improved using other techniques of evolutionary computation or increasing or reducing distance prediction. It could be interesting to analyse how different threshold policies could change algorithm performance. Then, it might be possible to obtain a concrete AI model that will improve the behavior of short -term congestion control whatever the real traffic pattern was. To improve AI application advantages, several questions regarding this work still exist. These questions include the AI application formula, the methodology used to obtain this prediction formula, the utilization of different traffic sources and the threshold policy used. If, for example, threshold policy is modified to exclusively use more restrictive thresholds than the reference ones and never less restrictive thresholds, a new algorithm could be obtained. This new algorithm would eliminate a smaller number of cells than the original algorithm whatever the traffic load and whatever the traffic sources. All these points remain a subject to be studied. Acknowledgments We would like to thank the Comisión Interministerial de Ciencia y Tecnología for its support under grant number CICYT/Tel The results of this work were obtained using the equipment co-funded by the Direcció General de Recerca de la Generalitat de Catalunya (D.O.G.C. 30/12/1997). We would also like to thank Enginyeria i Arquitectura La Salle (EALS), Universitat Ramon Llull for their support to our research group. References [1] M. de Prycker, Asynchronous Transfer Mode, solutions for Broadband ISDN, 2nd edn. Ellis Horwood, ISBN [2] The ATM Forum, ATM User-Network Interface (UNI) Signalling Specification, Version 3.1, p. 69, September [3] Salim Hariri and Bei Lu, ATM -Based Parallel and Distributed Computing, 1996 [4] S.Abe, T.Soumiya, A traffic Control Method for Service Quality Assurance in ATM Networks, IEEE Journal on Selected Areas in Communications Vol. 12, n.2, February 1994 [5] William Stallings, Data & Computer Communications, 6 th. ed., Prentice Hall, June 2000 [6] D. Gaïti and G. Pujolle, Performance Management Issues in ATM Networks: Traffic and Congestion Control, IEEE Transactions on Networking, vol. 4, n.2, April 1996 [7] Ramesh, Rosenberg and Kumar, Revenue Maximization in ATM Networks Using the CLP Capability and Buffer Priority Management, IEEE/ACM Transactions on Networking, Vo. 4, N.6, December 1996 [8] Norio Matsufuru and Reiji Aibara, Flexible QoS Using Partial Buffer Sharing with UPC, IEICE Trans. Commun. Vol. E83-B, N.2, February 2000 [9] John R. Koza, Genetic Programming, 6th Printing, Massachusetts Institute of Technology, ISBN [10] Hilde Hemmer and Per Thomas Huth, Evaluation of Policing Functions in ATM Networks, Elsevier Science Publishers B.V., 1991 [11] Tsern-Huei Lee, Kuen-Chu Lai, Design of a Real-Time Call Admission Controller for ATM Networks, IEEE/ACM Transactions on Networking, vol. 4, N.5, October