in the ATM network multiple connections share one physical link. .... Figure 5. Fuzzy Adapter. Network. CAC. Controller. Fuzzy Adapter. Monitor. TP, QoS.
Fuzzy Adaptive Connection Admission Control for Real-Time Applications in ATM-Based Heterogeneous Networks Biao Chen Department of Computer Science University of Texas at Dallas Richardson, TX 75083-0688
Abstract In this paper, we study connection-oriented service in heterogeneous network for real-time applications. Many existing distributed mission-critical systems are deployed over heterogeneous networks. Hence, it is necessary to extend the real-time communication technology to encompass heterogeneous networks. A connection can be considered as a contract between an application and the network: the application speci es the characteristics of the trac which it may generate and the network agrees to provide the requested quality of service (QoS) to the application. For real-time applications, the most crucial QoS is to meet deadline requirements. We propose a fuzzy intelligent system for connection admission control (CAC). Upon a request of connection establishment, the CAC determines if the worst case delays of the requesting and existing connections can be satis ed given the available network resources. If so, the CAC allocates appropriate network resources to the requesting con This work was partially sponsored by the Air Force Oce of Scienti c Research, Air Force Materiel Command, USAF, under grant number F49620-96-1-1076 and by Texas Higher Education Coordinating Board under its Advanced Technology Program with grant number 999903-204. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the ocial policies or endorsements, either expressed or implied, of the Air Force Oce of Scienti c Research, the U.S. Government, Texas State Government, Texas Higher Education Coordinating Board, or Texas A&M University.
Yingbi Zhang, John Yen, and Wei Zhao Department of Computer Science Texas A&M University College Station, Texas 77843-3112 nection. Our system uses fuzzy logic to capture the knowledge for adapting its strategy to dynamic system status. The parameters in fuzzy logic rule can be identi ed using genetic algorithms. Our approach is compatible with current network standards and hence can be readily used in practical systems.
1. The Problem In this paper, we study adaptive connection admission control for real-time applications in heterogeneous networks. In particular, we will concentrate on ATM-based heterogeneous networks where ATM serves as a backbone that connects di erent LAN segments by interface devices. These types of networks are popular lately due to their cost-e ectiveness, high bandwidth, and scalability. Figure 1 shows the architecture of an ATM-based heterogeneous network which uses ATM to interconnect Ethernet, Token Ring and FDDI LANs. This architecture takes advantage of features of ATM such as high bandwidth, high scalability, and quality of service(QoS) support. Communication services over packet-switching digital networks can be classi ed as best e ort and connection-oriented service. Many of the currently deployed digital communication networks provide the best e ort service. That is, the underlying network (and its management) makes a good e ort to provide the best quality of service. However, there is no prior guarantee regarding the quality
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of service an application may receive. Although cost-e ective and acceptable for many common applications like electronic mail, le transfer, etc., the best e ort communication service is insucient for mission-critical applications such as industrial process control, space program, military systems, and newly emerging real-time applications such as multimedia and teleconferencing. These applications comprise of distributed processes that execute on di erent hosts and cooperate by exchange of messages to achieve a common objective. A requirement of these applications is that they must accomplish these tasks by speci c deadlines. Thus, the success of the system in supporting these applications depends crucially on the network's ability to guarantee a prespeci ed quality of service such as transfer of all critical messages by their deadlines. In other words, when these applications send messages, the network must guarantee that the worst case delay in transferring these messages is no more than the deadline. Since 1980, connection-oriented communication service has been proposed and developed to address the problem. A connection is an abstract communication service provided by the network to a particular application. It can be considered as a contract between a communication application and the underlying network: the application speci es the characteristics of its trac and the network agrees to provide the requested quality of service to the application. The network will
not admit a connection if the requested quality of service cannot be guaranteed. Thus, one may view a connection as a virtual link that has certain trac-carrying capacity and is dedicated for the use by an application. Clearly, the connectionoriented communication technology is useful for many mission-critical applications. In this study, we aim at providing connectionoriented services over an ATM-based heterogeneous networks (ABHN) for real-time applications. The key problem we have to address is the admission control of connections. For a given request of connection establishment, the connection admission control procedure determines if the deadline constraints of the requesting connection as well as existing connections can be met. If so, the requesting connection is accepted, otherwise, it is rejected. The procedure of connection admission control can be divided into two steps: Step 1: to determine if the worst case delay of the requesting and existing connections can be satis ed given the available network resources. Step 2: if yes, to allocate a proper amount of network resource and to establish the connection. In Step 1, the worst case end-to-end packet delay has to be analyzed. For this purpose, we take a decomposition approach in which a connection path is decomposed into a sequence of servers; then the worst case end-to-end delay is obtained by summing up the worst case delays suffered by the connection at individual servers. Previous studies have shown that such an approach is ecient and e ective, if proper trac speci cation is provided. Our study is the rst to use this approach in a heterogeneous networking environment for real-time applications. The second step is an interesting one. Allocation of network resources to the requesting connection impacts the admission of future connections. Overallocating network resource to a new connection can result in not only waste of resource and low network utilization but also rejection of future connection request due to \insucient" available
resource while the actual network load is still low. On the other hand, if we only allocate network resource to a new connection such that its worst case delay is just under its deadline, delay disturbance introduced by future connections may result in the violation of its deadline constraint since in the ATM network multiple connections share one physical link. In this case, by the connection admission requirement, the new connection establishement request will be rejected. Hence, excessive or insucient allocation of resources to a connection may jeopardize the chance of a future connection being admitted and severely a ect network performance.
2. The Adaptive Admission Approach We address this problem using an adaptive approach. Our CAC approach, once determines that a requesting connection is admissible, will adaptively allocate proper amount resource to the connection. The adaptation strategy is represented using fuzzy if-then rules [11]. We illustrate this by an example. Consider an ABHN where the LAN segments are FDDIs. In [2], it is shown that in this network, the critical resources to be allocated are the bandwidths on sender and receiver's FDDI rings, namely HS and HR . We call a pair of bandwidth allocation (HS , HR) feasible if it can satisfy the deadline requirement of the new connection. It is proved in [2] that for each connection all feasible allocations form a feasible region. A connection is admissible only if the feasible region in (HS , HR) plane is not empty. Figure 2 gives an example of (HS , HR ) feasible region. To maximize the chance of meeting message deadlines, one would select (HS , HR ) = (HSmax avai , HRmax avai ). That is, Mi;j is allocated to the maximum available bandwidth. The problem of this algorithm is that it allocates all the bandwidth available on sender and receiver's FDDI rings to connection Mi;j . This will result in the rejection of any future connection originated from or designated to these two rings simply because no bandwidth is available. On the other hand, one might like to choose (HS , HR) = (HSmin need , HRmin need ). That is, Mi;j
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is allocated with the minimum amount of bandwidth which just makes it possible to meet all the deadlines. Intuitively one might think that if we allocate network resource as little as possible to each connection we would be able to maximize the network utilization and hence achieve optimal network performance. Unfortunately, this is not true for most ATM-based heterogeneous network system. With the minimum bandwidth allocation, the worst case delay of some connection(s) may be very tight { very close to the deadline(s). Because of this, the disturbance generated by a future connections may cause it (them) to miss its (their) deadline(s). If this happens, the new connection cannot be admitted. Thus, this kind of allocation is not ideal either. That is, the allocation must be properly chosen based on the system status. Although any point in the feasible region is a feasible allocation, choosing di erent allocations will impact the future connection admission and network performance. Now the question is how to allocate bandwidth (HS , HR) appropriately. We suggest the following rules for the bandwidth allocation: Rule 1. (HS , HR ) must be in the feasible region. This is obviously necessary. Rule 2. The ratio of HS to HR should be the same as the ratio of HSmax avai to HRmax avai . That is, (HS , HR) should be a point on the line segment ( ) connecting (0, 0) and (HSmax avai , HRmax avai ). See Figure 2. By do-
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ing so, we reserve resources from both rings in a proportional manner. According to above discussion, we propose to allocate bandwidth (HS , HR) as follows:
HS = HSmin need + (HSmax need ? HSmin need ) and
HR = HRmin need + (HRmax need ? HRmin need ) where is a real number between zero and one. This allocation is a compromise between the minimum and maximum allocations. It is easy to verify that this selection still satis es the above two rules. According to the above formula an allocation (HS , HR ) can be identi ed by one parameter . So we can denote it by (HS ( ), HR( )). Let's see how values of a ect the network performance. The performance metric we are interested in is admission probability (AP) de ned as the ratio of total number of admitted connections to total number of connection requests, i.e., of conns admitted by CAC algo. : AP = Total #Total # of conns requested This metric has been used in evaluation of realtime communication systems. We simulate an FDDI-ATM-FDDI system with link utilization (U ) in the ATM backbone being 0.3, 0.6, and 0.9. Figure 3 shows the relationship between AP and . We see that the admission probability is sensitive
Figure 4. Fuzzy CAC Off-Line Self-Training Model
to the selection of especially when the system load is heavy. This implies that a good selection algorithm should select depending on the current network system status. Now the problem becomes how to adaptively select parameter so that the system can provide the best performance under di erent system load. We propose an adaptive CAC model that consists of o -line training and on-line adaptive resource allocation. The key idea is to use fuzzy if-then rules to describe the relationships between the systems status and the admission control parameter . The o -line self-training model shown in Figure 4 is used to obtain these fuzzy if-then rules. The connection request generator component will simulate the process of connection setup and breakdown. It generates di erent real-time connection requests to CAC controller for various types of traf c patterns and QoS requirements. The CAC controller makes decision whether to accept or reject the connection request(s) based on the current system status which is monitored by Monitor module. If the request is admissible, the CAC controller will allocate network resource (i.e., select ) to the new connection. The Monitor module also evaluates the performance of the resource allocation decision made by the CAC controller. The results are summarized into fuzzy rule base which re ects the relationships between and system performance. The nal results is summarized into the fuzzy rule base. Each rule matches the traf c pattern, QoS requirement and current network
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status with the best value of . If we apply self-training process to all types of real-time trac, the fuzzy rule base will be able to map the trac pattern and QoS requirement of a connection to the proper value of . With this fuzzy rule base, we can build up a fuzzy adapter, as shown in Figure 5. Figure 6 shows the structure of an on-line adaptive CAC and resource allocation framework which is based on the fuzzy adapter. The fuzzy adapter is able to provide the optimal value of based on system status which allows the adaptive CAC framework to manage network resource eciently and e ectively. To show how an adaptive approach can improve the system performance we compare the performance of our adaptive CAC with a CAC based on a fuzzy adapter which randomly selects value of between 0 and 1. The simulation is based on the
same FDDI-ATM-FDDI system that we used to show the sensitivity of . Detailed data of simaulation are shown in Figure 7. The graph shows that there is a big improvement on the network performance due to properly selected . The performance improvement is more obvious when the ATM link load is heavy. Since all the fuzzy rules can be formed oine, the time eciency of this adaptive CAC and resource allocation method is another advantage for the real-time application.
3. Conclusion In this paper we proposed a fuzzy adaptive connection admission control and resource allocation framework. Our study on the subject is preliminary. Many extensions are possible. The methodology can be easily extended to networks with di erent con gurations. An interesting extension will be to guarantee application-toapplication deadlines. In this case, for each host, trac processing must be modeled and the worst case delay needs to be analyzed.
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[7] John Yen, Bogju Lee,and James C. Liao \Using a Hybrid Genetic Algorithm and Fuzzy Logic for Metabolic Modeling ". Will appear in Proceedings of the 13th National Conference on AI, Portland, Oregan, Aug. 1996. [8] Chung-Ju Chang, Ray-Guang Cheng. \ Traf c Control in an ATM Network Using Fuzzy Set Theory". In Proceedings of IEEE INFORCOM'94, pages 1200-1207, June, 1994. [9] Michael A. Lee, Hideyuki Takagi. \Integrated Design Stages of Fuzzy Systems using Genetic Algorithms". In Proceedings of IEEE Internation Conference on Fuzzy Systems , pages 621-617. Mar.28-Apr.1, 1993.
[10] Chuck Karr. \Genetic Algorithms for Fuzzy Controllers". In AI Expert, pages 27-33, Feb. 1991. [11] L. A. Zadeh. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, pages 28-44, 1973.
