2012 IEEE International Conference on Cluster Computing (2012)
Beijing, China China
Sept. 24, 2012 to Sept. 28, 2012
Parallel processing is an important pattern in cluster systems. To analyze the performance of parallel processing systems, we leveraged the fork-join queueing network (FJQN) models. However, there are no easy solutions to these models, especially for the multi-class closed ones. In this paper, a novel and efficient method named horizontal decomposition has been proposed. The main idea of our method is to approximate a non-product-form FJQN with some closed and open product-form networks. So the computational complexity can be dramatically reduced compared with the traditional hierarchical decomposition approach. And the algorithms for solving single-class and multi-class closed FJQNs have been developed respectively based on the horizontal decomposition. With these algorithms, the response time and throughput of each service center in a FJQN can be approximately calculated. The evaluation results show that 90 percentile of relative errors of most service centers are less than 15\% except for the shared ones. The evaluation results also showed that the number of iterations in the algorithm for the multi-class FJQNs almost grows linearly with the population of networks.
Computational modeling, Throughput, Niobium, Parallel processing, Sociology, Statistics, Approximation algorithms, horizontal decomposition, performance analysis, parallel processing, fork-join queueing network
H. Chen, J. Yin and C. Pu, "Performance Analysis of Parallel Processing Systems with Horizontal Decomposition," 2012 IEEE International Conference on Cluster Computing(CLUSTER), Beijing, China China, 2012, pp. 220-229.