Reference Hub

This research has been cited in:

Article
An IoT-based resource utilization framework using data fusion for smart environmentsInternet of Things10.1016/j.iot.2022.100645
Article
Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approachJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104766
Article
A novel load balancing technique for cloud computing platform based on PSOJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.10.016
Article
An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud ComputingComputers, Materials & Continua10.32604/cmc.2022.030778
Chapter
Comparative Study of Metaheuristic Algorithms for Scheduling in Cloud Computing Based on QoS ParametersAdvances in IoT and Security with Computational Intelligence10.1007/978-981-99-5088-1_1
Article
Multi objective trust aware task scheduling algorithm in cloud computing using whale optimizationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.01.016
Chapter
Heterogeneous-Aware Online Cloud Task Scheduler Based on Clustering and Deep Reinforcement Learning EnsembleAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-70665-4_18
Article
Hybrid Load Balancing Technique for Cloud Environment Using Swarm OptimizationThe Review of Socionetwork Strategies10.1007/s12626-024-00160-8
Chapter
Inertia weight strategies for task allocation using metaheuristic algorithmCognitive Big Data Intelligence with a Metaheuristic Approach10.1016/B978-0-323-85117-6.00004-2
Conference
Task Scheduling in Fog computing using hybrid GA and Success rate based PSO (GASPSO)2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon)10.1109/SmartTechCon57526.2023.10391634
Article
Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic AlgorithmIEEE Access10.1109/ACCESS.2020.3016762
Conference
Gradient Based Optimization Approach to solve Reliability Allocation System2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS51004.2021.9397197
Article
An improved particle swarm optimization algorithm for scheduling tasks in cloud environmentExpert Systems10.1111/exsy.13529
Article
Multi Objective Task Scheduling Algorithm in Cloud Computing Using the Hybridization of Particle Swarm Optimization and Cuckoo SearchJournal of Computational and Theoretical Nanoscience10.1166/jctn.2020.9427
Article
Hybrid ant genetic algorithm for efficient task scheduling in cloud data centersComputers and Electrical Engineering10.1016/j.compeleceng.2021.107419
Article
Ensure secured data transmission during virtual machine migration over cloud computing environmentInternational Journal of System Assurance Engineering and Management10.1007/s13198-022-01834-8
Article
Enhancing QoS with Resource Optimization Technique Based on Harmony Search in Cloud EnvironmentInternational Journal of Cloud Applications and Computing10.4018/IJCAC.311504

A PSO Algorithm Based Task Scheduling in Cloud Computing

Mohit Agarwal, Gur Mauj Saran Srivastava

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)10(4)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566090|DOI: 10.4018/IJAMC.2019100101

Cite Article Cite Article

MLA

Agarwal, Mohit, and Gur Mauj Saran Srivastava. "A PSO Algorithm Based Task Scheduling in Cloud Computing." IJAMC vol.10, no.4 2019: pp.1-17. http://doi.org/10.4018/IJAMC.2019100101

APA

Agarwal, M. & Srivastava, G. M. (2019). A PSO Algorithm Based Task Scheduling in Cloud Computing. International Journal of Applied Metaheuristic Computing (IJAMC), 10(4), 1-17. http://doi.org/10.4018/IJAMC.2019100101

Chicago

Agarwal, Mohit, and Gur Mauj Saran Srivastava. "A PSO Algorithm Based Task Scheduling in Cloud Computing," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.4: 1-17. http://doi.org/10.4018/IJAMC.2019100101

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

A PSO Algorithm Based Task Scheduling in Cloud Computing

MLA

APA

Chicago

Export Reference

Abstract

Request Access