An integrated admission control based on measurements in ATM networks
Introduction
Asynchronous transfer mode (ATM) has been the hottest topic in the networking community and proposed by the International Telecommunications Union (ITU) as the transport mechanism of choice for B-ISDN (Broadband-Integrated Services Digital Network). However, there are a few issues which must be solved to implement efficient and robust ATM networks. To maximize the merits of ATM, designers have been faced with a wide range of problems, varying from the implementation of mechanisms for efficient congestion control, call admission and routing to the development of lightweight transport protocols. In particular, as pointed out in many researches, traffic control is the first aspect to be considered [15]. Traffic control remains, however a controversial problem in practice: how to provide network resources (Call Admission Control: CAC) and how to monitor the conformity (Usage Parameter Control: UPC). One of the reasons that traffic control is difficult in ATM networks is the diversity of traffic characteristics and QoS requirements. Adaptability to unknown and variable traffic demand and flexibility for introducing new services are more important in ATM networks than in conventional networks.
There have been numerous studies on call admission control. In most of these studies, the approaches depend on restrictive and precise assumptions on traffic models, or involve complex and computationally expensive procedures of matrix computations. These approaches, while representing the state-of-art in modeling and analysis, are hardly practical for real-time admission control schemes. Therefore, many analyses have adopted traffic measurements for admission control [1], [2], [3], [4]. However, relying on measured quantities raises estimation error problem that impairs developing robust control.
In this paper, we propose an alternate approach to efficient resource allocation and providing strong QoS guarantees. This approach integrates measurement process with cell loss probability estimation to prevent the overallocation of bandwidth and reduce the estimation error from the control based on network measurements alone. Since cell delay can often be controlled within a desired bound by adjusting the buffer size, we mainly concentrate on cell loss as the QoS measure of interest [5], [6], [7], [8]. The instantaneous cell loss rate of a new call can be calculated from simple formula which implements an implicit measurement procedure. And each traffic source can be modeled as on/off process as in many approaches [2], [9], [10]. In order to achieve higher bandwidth utilization, we use alternative two-state model which reflects statistical multiplexing gain efficiently.
We emphasize that the main advantages of the CAC developed here are its high link efficiency and low computational overhead that makes it practical for real time admission control. The performance of the developed algorithm is illustrated via analysis and simulation.
Section snippets
The traffic and buffer model
All traffic offered to an ATM node is regulated at the network edge. The regulation is performed by a leaky bucket and the UPC parameters which are peak cell rate, λjp, sustainable cell rate, λjs and the maximum burst size, bj. The traffic is segregated on classes to improve network performance and each class has its own traffic attributes which consist of UPC parameters and QoS limits. According to the segregation, every class is assigned its own queue so that a single queueing system is
Measurements for admission control
The measurement mechanism adopted in this paper, observes the current traffic through a time window. By periodic measurement of cell loss over the window, it is possible to characterize the instantaneous traffic stream. In other words, the window is used to compute the average and variance of current traffic. Assume that N-sized windows which have T time units, respectively, are moving as time goes on as in Fig. 2. Using these N-sized moving windows, the most recent traffic behavior can be
Simulation results and discussion
This section evaluates the performance of CACs under Non-Measurement-Based policy (NMB) where the evaluation of the mean and variance is based solely on UPC parameters, and our Integrated Measurement-Based policy (IMB), respectively. The NMB-CAC also uses Gaussian approximation, but is different from the IMB-CAC in computing mean and variance which are used in Gaussian approximation, directly from UPC parameters. And while the source modeling of the NMB-CAC is an on/off process, the modeling of
Conclusion
In this paper, a simple CAC algorithm was developed by combining the Gaussian approximation with traffic measurements. Measurement-oriented approach can suffer from the estimation error for each individual call and Gaussian approximation-oriented approach can be more conservative which causes a failure in utilizing statistical gain. Thus, this dilemma can be solved by the integration of the estimation and the Gaussian approximation. Our simulation studies have shown that the proposed
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