Research Article Open Access

Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment

Nagwan M. Abdel Samee1, Sara Sayed Ahmed2 and Rania Ahmed Abdel Azeem Abul Seoud2
  • 1 Princess Nourah Bint Abdulrahman University, Saudi Arabia
  • 2 Fayoum University, Egypt
Journal of Computer Science
Volume 15 No. 4, 2019, 594-611

DOI: https://doi.org/10.3844/jcssp.2019.594.611

Submitted On: 18 January 2019 Published On: 3 May 2019

How to Cite: Samee, N. M. A., Ahmed, S. S. & Abul Seoud, R. A. A. A. (2019). Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment. Journal of Computer Science, 15(4), 594-611. https://doi.org/10.3844/jcssp.2019.594.611

Abstract

Cloud Computing (CC) is a recent technology in the Information and Communication Technology (ICT) field. It provides an on-demand access to the shared pool of resources via virtualization. Large enterprises move toward CC due to its flexibility and scalability driven from its elastic pay-per-use model. To provide ensured efficient performance to users, tasks should be efficiently mapped to available resources. Therefore, Task Scheduling (TS) is significant issue in the CC technology. TS is a NP-complete optimization problem, so a deep investigation of different metaheuristic and heuristic TS algorithms is presented here. Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as metaheuristic algorithms are implemented and their performance have been compared to heuristic techniques (First Come First Serve (FCFS) and Shortest Job First (SJF)) on symmetric and asymmetric environment. The cloud service providers and users have different performance requirements. Six performance metrics including makespan, flow time, response time, resource utilization, throughput time and degree of imbalance have been measured. For asymmetric environment, real environment, metaheuristic TS algorithms surpassed the heuristic methods.

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Keywords

  • Cloud Computing
  • Task Scheduling
  • Meta-Heuristic
  • Performance Metrics
  • Asymmetric Environment