Glowworm Swarm Optimisation Based Task Scheduling for Cloud Computing

Dabiah Ahmed Alboaneen; Huaglory Tianfield; Yan Zhang

doi:10.1145/3018896.3036395

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Conference proceeding

Glowworm Swarm Optimisation Based Task Scheduling for Cloud Computing

Dabiah Ahmed Alboaneen, Huaglory Tianfield and Yan Zhang

PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, DATA AND CLOUD COMPUTING (ICC 2017), pp.1-7

01/01/2017

DOI: https://doi.org/10.1145/3018896.3036395

Abstract

Computer Science

Computer Science, Information Systems

Computer Science, Theory & Methods

Science & Technology

Technology

Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks.

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Title: Glowworm Swarm Optimisation Based Task Scheduling for Cloud Computing
Creators - without role: Dabiah Ahmed Alboaneen - Glasgow Caledonian University
Huaglory Tianfield - Glasgow Caledonian University
Yan Zhang - Glasgow Caledonian University
Contributors - without role: H Hamdan
D E Boubiche
F Hidoussi
Publication Details: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, DATA AND CLOUD COMPUTING (ICC 2017), pp.1-7
Publisher: Assoc Computing Machinery
Number of pages: 7
Identifiers: 9915260308331
Academic Unit: Imam Abdulrahman Bin Faisal University
Language: English
Resource Type: Conference proceeding