Document Type

Article

Source of Publication

Internet of Things and Cyber-Physical Systems

Publication Date

7-14-2023

Abstract

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.

ISSN

2667-3452

Publisher

Elsevier BV

Volume

4

First Page

32

Last Page

39

Disciplines

Computer Sciences

Keywords

Ant colony optimization (ACO), Cloud computing, Density-based spatial clustering of applications with noise (DBSCAN), PSO (particle swarm optimization), Virtual machine (VM)

Scopus ID

85165086546

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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