Open AccessGenetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing
Author(s) -
Ahmed A. A. Gad-Elrab,
T. A. A. Alzohairy,
K. R. Raslan,
Farouk A. Emara
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.2.2163
Subject(s) - computer science , distributed computing , virtual machine , dynamic priority scheduling , cloud computing , scheduling (production processes) , fair share scheduling , two level scheduling , rate monotonic scheduling , job shop scheduling , earliest deadline first scheduling , operating system , mathematical optimization , schedule , mathematics
Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.