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Maximizing availability for task scheduling in on‐demand computing–based transaction processing system using ant colony optimization
Author(s) -
Mahato Dharmendra Prasad,
Singh Ravi Shankar
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4405
Subject(s) - ant colony optimization algorithms , computer science , job shop scheduling , mathematical optimization , scheduling (production processes) , database transaction , metaheuristic , foraging , maximization , ant colony , fair share scheduling , genetic algorithm , distributed computing , algorithm , schedule , mathematics , machine learning , ecology , biology , programming language , operating system
Summary Maximization of availability and minimization of the makespan for transaction scheduling in an on‐demand computing system is an emerging problem. The existing approaches to find the exact solutions for this problem are limited. This paper proposes a task scheduling algorithm using ant colony optimization (MATS_ACO) to solve the mentioned problem. In this method, first, availability of the system is computed, and then, the transactions are scheduled using the foraging behavior of ants to find the optimal solutions. We also modify two known meta‐heuristic algorithms such as genetic algorithm (GA) and extremal optimization (EO) to obtain transaction scheduling algorithms for the purpose of comparison with our proposed algorithm. The compared results show that the proposed algorithm performs better than others.