An enhanced cuckoo optimization algorithm for task graph scheduling in cluster-computing systems
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Optimized task scheduling is key to achieve high performance in the cluster-computing systems whose application is broad ranging from scientific to the military purposes. This combinatorial problem is NP-hard from the time complexity perspective, where applying newly proposed metaheuristics to it deserves further investigation based on the well-known no-free-lunch theorem. Accordingly, in this paper, an enhanced version of cuckoo optimization algorithm (COA) named E-COA is proposed to cope with the static task scheduling problem in the mesh topology cluster-computing environments. The proposed approach is equipped with an efficient adaptive semi-stochastic egg-laying strategy that significantly improves the local and global search potentiality of the basic COA. The experiments on a comprehensive set of randomly generated task graphs with different structural parameters reveal the efficiency of the proposed approach from the performance point of view, especially for the small-scale samples, and where the number of clusters in the machine is very restricted, i.e., we are in the lack of computational resource.
KeywordsCluster-computing environments Mesh topology Task graph scheduling Cuckoo optimization algorithm (COA) Metaheuristics
This work has been financially supported by the Department of Gotvand, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran, as a research project.
This study was funded by the Department of Gotvand, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran, as a research project.
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Conflict of interest
The author declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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