Metaheuristic Algorithms for Independent Task Scheduling in Symmetric and Asymmetric Cloud Computing Environment
- 1 Princess Nourah Bint Abdulrahman University, Saudi Arabia
- 2 Fayoum University, Egypt
Copyright: © 2020 Nagwan M. Abdel Samee, Sara Sayed Ahmed and Rania Ahmed Abdel Azeem Abul Seoud. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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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- Cloud Computing
- Task Scheduling
- Performance Metrics
- Asymmetric Environment