A Static Multiprocessor Scheduling Algorithm for Arbitrary Directed Task Graphs in Uncertain Environments

Authors:
Jun Yang;Xiaochuan Ma;Chaohuan Hou;Zheng Yao
Affiliations:
Institute of Acoustics, Chinese Academy of Sciences, and Graduate University, Chinese Academy of Sciences,;Institute of Acoustics, Chinese Academy of Sciences,;Institute of Acoustics, Chinese Academy of Sciences,;Department of Electronic Engineering, Tsinghua University, China
Venue:
ICA3PP '08 Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing
Year:
2008

Citing 6
Cited 0

Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors

IEEE Transactions on Parallel and Distributed Systems
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm

Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors

ACM Computing Surveys (CSUR)
Theory and Practice of Uncertain Programming

Theory and Practice of Uncertain Programming
Hypertool: A Programming Aid for Message-Passing Systems

IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling

IEEE Transactions on Parallel and Distributed Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The objective of a static scheduling algorithm is to minimize the overall execution time of the program, represented by a directed task graph, by assigning the nodes to the processors. However, sometimes it is very difficult to estimate the execution time of several parts of a program and the communication delays under different circumstances. In this paper, an uncertain intelligent scheduling algorithm based on an expected value model and a genetic algorithm is presented to solve the multiprocessor scheduling problem in which the computation time and the communication time are given by stochastic variables. In simulation examples, it shows that the algorithm performs better than other algorithms in uncertain environments.