Static scheduling of directed acyclic data flow graphs onto multiprocessors using particle swarm optimization

An efficient method based on particle swarm optimization (PSO) is developed to solve the Multiprocessor Task Scheduling Problem (MPTSP). To efficiently execute parallelized programs on a multiprocessor environment, a scheduling problem must be solved to determine the assignment of tasks to the proce...

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Bibliographic Details
Published in:Computers & operations research 2013-10, Vol.40 (10), p.2322-2328
Main Authors: Al Badawi, Ahmad, Shatnawi, Ali
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Assignment problem
Combinatorial problems
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Graph theory
Job shops
Language processing and microprogramming
Mathematical problems
Multiprocessing
Multiprocessor
Multiprocessor scheduling
NP-complete
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Parallel processing
Particle swarm optimization
Precedence constraints
Processors
Scheduling
Scheduling, sequencing
Software
Studies
Swarm intelligence
Tasks
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Summary:An efficient method based on particle swarm optimization (PSO) is developed to solve the Multiprocessor Task Scheduling Problem (MPTSP). To efficiently execute parallelized programs on a multiprocessor environment, a scheduling problem must be solved to determine the assignment of tasks to the processors, the execution order of the tasks, and the starting time of each task, such that some optimality criteria are met. The scheduling problem is known as an NP-complete problem even when the target processors are fully connected and no communication delay is considered among the tasks in the task graph. The complexity of the scheduling problem depends on the number of tasks (N), the number of processors (M), the task processing time and the precedence constraints. The Directed Acyclic Graph (DAG) was exploited to represent the tasks and their precedence constraints. The proposed algorithm was compared with the Genetic Algorithm (GA) and the Duplication Scheduling Heuristic (DSH). We also provide a systematic investigation on the effect of varying problem settings. The results show that the proposed algorithm could not outperform the DSH while it could outperform the GA in some cases.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2013.03.015