• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment


Abstract

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.


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Cite this article

[IEEE Style]
Frederic NZanywayingoma and Yang Yang, "Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment," KSII Transactions on Internet and Information Systems, vol. 11, no. 12, pp. 5780-5802, 2017. DOI: 10.3837/tiis.2017.12.006

[ACM Style]
NZanywayingoma, F. and Yang, Y. 2017. Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment. KSII Transactions on Internet and Information Systems, 11, 12, (2017), 5780-5802. DOI: 10.3837/tiis.2017.12.006