In the late three decades, grid computing has emerged as a new field providing a high computing performance to solve larger scale computational demands. Because Directed Acyclic Graph (DAG) application scheduling in a...
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ISBN:
(数字)9783319257440
ISBN:
(纸本)9783319257440;9783319257433
In the late three decades, grid computing has emerged as a new field providing a high computing performance to solve larger scale computational demands. Because Directed Acyclic Graph (DAG) application scheduling in a distributed environment is a NP-Complete problem, meta-heuristics are introduced to solve this issue. In this paper, we propose to hybridize two well-known heuristics. The first one is the heterogeneousearliestfinishtime (HEFT) heuristic which determines a static scheduling for a DAG in a heterogeneous environment. The second one is Particle Swarm Optimization (PSO) which is a stochastic meta-heuristic used to solve optimization problems. This hybridization aims to minimize the makespan (i.e., overall completion time) of all the tasks within the DAG. The experimental results that have been conducted under hybridization show that this approach improves the scheduling in terms of completion time compared to existing algorithms such as HEFT.
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