R. M. Bryant

Bio: R. M. Bryant is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Flow network & Mean value analysis. The author has an hindex of 1, co-authored 1 publications receiving 18 citations.

A queueing network approach to the module allocation problem in distributed systems

Abstract: Given a collection of distributed programs and the modules they use, the module allocation problem is to determine an assignment of modules to processors that minimizes the total execution cost of the programs. Standard approaches to this problem are based on solving either a network flow problem or a constrained 0-1 integer programming problem.In this paper we discuss an alternative approach to the module allocation problem where a closed, multiclass queueing network is solved to determine the cost of a particular module allocation. The advantage of this approach is that the execution cost can be expressed in terms of performance measures of the system such as response time. An interchange heuristic is proposed as a method of searching for a good module allocation using this model and empirical evidence for the success of the heuristic is given. The heuristic normally finds module allocations with costs within 10 percent of the optimal module allocation.Fast, approximate queueing network solution techniques based on mean-value-analysis allow each heuristic search to be completed in a few seconds of CPU time. The computational complexity of each search is O (M K (K + N) C) where M is the number of modules, K is the number of sites in the network, N is the number of communications processors, and C is the number of distributed program types. It appears that substantial problems of this type could be solved using the methods we describe.

Analytic Queueing Network Models for Parallel Processing of Task Systems

Abstract: This paper is concerned with the performance evaluation of a realistic model of parallel computations. We present an efficient algorithm to determine the mean completion time and related performance measures for a task system: a set of tasks with precedence relationships in their execution sequence, such that the resulting graph is acyclic. A queueing network (QN) is used to model tasks executing on a single or multicomputer system. In the case of multicomputer systems, we take into account the delay due to interprocess communication. A straight- forward application of a QN solver to the problem is not possible due to variations in the state of the system (composition of tasks in execution). An accurate algorithm based on hierarchical decomposition is presented for solving task systems. At the higher level, the system behavior is specified by a Markov chain whose states correspond to the combination of tasks in execution. The state transition rate matrix for the Markov chain is triangular (since the task system graph was assumed to be acyclic), therefore it can be solved efficiently to compute the state probabilities and the task initiation/completion times. At the lower level, the transition rates among the states of the Markov chain are computed using a QN solver, which determines the throughput of the computer system for each system state. The model and the solution method can be used in performance evaluation of applications exhibiting concurrency in centralized/distributed systems where there are conflicting goals of load balancing and minimizing interprocess communication overhead.

Task allocation in fault-tolerant distributed systems

Abstract: This paper examines task allocation in fault-tolerant distributed systems. The problem is formulated as a constrained sum of squares minimization problem. The computational complexity of this problem prompts us to consider an efficient approximation algorithm. We show that the ratio of the performance of the approximation algorithm to that of the optimal solution is bounded by 9m/(8m?r+1)), wherem is the number of processors to be allocated andr is the number of times each task is to be replicated. Experience with the algorithm suggests that even better performance ratios can be expected.

Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem

Abstract: Hill-climbing, simulated annealing and genetic algorithms are search techniques that can be applied to most combinatorial optimization problems. The three algorithms are used to solve the mapping problem, which is the optimal static allocation of communication processes on distributed memory architectures. Each algorithm is independently evaluated and optimized according to its parameters. The parallelization of the algorithms is also considered. As an example, a massively parallel genetic algorithm is proposed for the problem, and results of its implementation on a 128-processor Supernode are given. A comparative study of the algorithms is then carried out. The criteria of performance considered are the quality of the solutions obtained and the amount of search time used for several benchmarks. A hybrid approach consisting of a combination of genetic algorithms and hill-climbing is also proposed and evaluated. >