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.
Highlights
Cloud computing, Cloud Computing (CC), applies distributed computing techniques to deliver an on-demand access to a shared virtual computing resources
The task size is expressed as Million Instructions (MI) and the computing power of the virtual machines is represented as the number of Millions of Instructions Per Second (MIPS) that can be processed
The expected execution time of task Ti running on virtual machine VMj (Sarathambekai and Umamaheswari, 2017) can be expressed as: ETC [i, j] = MITi / MIPS VMi where, i ∈{1,2... n}, j∈{1,2... m}
Summary
CC, applies distributed computing techniques to deliver an on-demand access to a shared virtual computing resources VM can be scaled up or down according to the demanded services It can provide resource sharing, high utilization of pooled resources, rapid provisioning and workload isolation (Ahmad et al, 2015b; Zhang et al, 2018). All tasks or VMs are known a priori to scheduling. These tasks are independent of the virtual machine's states and their availability. The execution time of task may not be known and the information about VMs is not obtained until it comes into the scheduling stage (Nagadevi et al, 2013; Mathew et al, 2014). Metaheuristic task scheduling techniques are implemented using CloudSim simulator and compared to the traditional heuristic methods to solve the independent static TS problem in CC environment. Because it does not take full advantage of the asymmetric nature of VMs
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call