We study buffer access policies which provide different loss priorities between two types of services, namely, real-time and nonreal-time services in ATM networks. Real-time services, such as video and voice, require the cell transmission with bounded delay. For these services, their available buffer sizes are limited by the delay bounds. We compare the performance of several buffering policies with bounded delay constraints of real-time services. Numerical results indicate that a simple buffering policy, called limited partial buffer sharing (LPBS) proposed in this paper, has a good performance for efficient use of ATM networks.

We analyze M,MMPP/G/1 finite queues with queue-length-threshold (QLT) scheduling policy and Bernoulli schedule where the arrival of type-1 customers (nonreal-time traffic) is Poisson and the arrival of type-2 customers (real-time traffic) is a Markov-modulated Poisson process (MMPP). The next customer to be served is determined by the queue length in the buffer of type-1 customers. We obtain the joint queue length distribution for customers of both types at departure epochs by using the embedded Markov chain method, and then obtain the queue length distribution at an arbitrary time by using the supplementary variable method. From these results, we obtain the loss probabilities and the mean waiting times for customers of each type. The numerical examples show the effects of the QLT scheduling policy on performance measures of the nonreal-time traffic and the bursty real-time traffic in ATM networks.

It is known that in a nonblocking packet switch with input queueing, head-of-line (HOL) blocking affects significantly the maximum throughput of the packet switch. To alleviate the HOL blocking effect, a window policy has been proposed in that each input queue can scan up to a fixed number of packets (called the window size) to select for transmission on the outputs. However, the performance of the window policy has never been precisely characterized. In this paper, we use a closed queueing network model to characterize the performance of the packet switch with window policy. We obtain explicit closed-form formulae for the maximum throughput of the packet switch as a function of the window size. Both balanced and imbalanced traffic patterns are discussed. The formulae can easily determine the effectiveness of the window policy.

The security problems of object-oriented database system are investigated and security level assignment constraints and an access control mechanism based on the multilevel access control security policy are proposed. The proposed mechanism uses the Trusted Computing Base. A unique feature of the mechanism is that security levels are assigned not only to data items (objects), but also to methods and methods are not shown to the users whose security level is lower than that of the methods. And we distinguish between the security level of a variable in a class and that in an instance and distinguish between the level of an object when it is taken by itself and it is taken as a variable or an element of another complex object. All of this realizes the policy of multilevel access control.

The effects of changing system parameters on job scheduling policies are studied for load balancing of multi-class jobs in a distributed computer system that consists of heterogeneous host computers connected by a single-channel communications network. A job scheduling policy decides which host should process the arriving jobs. We consider two job scheduling policies. The one is the overall optimal policy whereby jobs are scheduled so as to minimize the overall mean job response time. Tantawi and Towsley obtained the algorithm that gives the solution of the policy in the single class job environment and Kim and Kameda extended it to the multiple job class environment. The other is the individually optimal policy whereby jobs are scheduled so that every job may feel that its own expected response time is minimized. We can consider three important system parameters in a distributed computer system: the communication time of the network, the processing capacity of each node, and the job arrival rate of each node. We examine the effects of these three parameters on the two load balancing policies by numerical experiment.