Open AccessEfficient Task Scheduling of Virtual Machines using Novel Spectral Partitioning and Differential Evaluation Algorithm
Author(s) -
Anwar Shaheen,
Sundar kumar
Publication year - 2022
Publication title -
international journal of advances in soft computing and its applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 18
eISSN - 2710-1274
pISSN - 2074-8523
DOI - 10.15849/ijasca.220328.11
Subject(s) - computer science , cloudsim , virtual machine , scheduling (production processes) , algorithm , distributed computing , cloud computing , crossover , differential evolution , job shop scheduling , mathematical optimization , embedded system , artificial intelligence , operating system , mathematics , routing (electronic design automation)
Task-scheduling is a major challenge in cloud computing environment that degrades the performance of the system. To enhance the performance of the system, an effective task-scheduling algorithm is needed. Hence an effective task-partitioning and taskscheduling algorithm is introduced for enhancing the system performance. To create resources (datacentre, broker, Virtual Machine - VM and cloudlet) in a dynamic way through the use of CloudSim. In addition, this study intended to perform taskpartitioning and task-scheduling in an effective manner by utilizing the novel spectral partitioning - (SP) and differential evaluation algorithm - (DEA). At first, the task and datacentre is initialized. Subsequently, task-partitioning is performed using the novel SP. It includes a series of steps in which a Laplacian matrix is computed initially. Then based on the Eigen-values and Eigen-vectors of the Laplacian matrix the tasks are partitioned. Followed by this, taskscheduling is performed with the employment of proposed novel DEA. The process comprise the following series of steps such as threshold calculation, mutation, crossover, selection and knee solution for achieving efficient task-partitioning and scheduling. The performance of the proposed system is evaluated by comparing it with other traditional methods. And validated in terms of service cost, load balancing, makespan and energy consumption. The results proved the efficacy of the introduced system. The overall results obtained from comparative analysis also reveal that proposed method outperformed other traditional techniques thereby accomplishing effective task scheduling of VMs in cloud computing environment. Keywords: Cloud computing environment, Virtual Machines, Task Scheduling, Novel Spectral Partitioning and Differential Evaluation Algorithm.