An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing-Reference-Cited by-同舟云学术

An Improved Particle Swarm Optimization Algorithm Based on Adaptive Weight for Task Scheduling in Cloud Computing

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Computer Science and Application Engineering - CSAE '18
language:
Short-container-title:

Author:

Luo Fei¹,Yuan Ye¹,Ding Weichao¹,Lu Haifeng¹

Affiliation:

1. East China University Of Science And Technology, Shanghai, China

Publisher

ACM Press

Reference15 articles.

1. Ian Foster, Yong Zhao, and loan Raicu. 2008. Cloud computing and g.d computing 360-degsee compared. Grid Computing Environments

2. Shaobin Zhan, and Hongying Huo. 2012. Improved PSO-based Task Scheduling Algorithm in Cloud Computing. Journal of information and computational science, (13), 3821--3829.

3. Zuo, Xingquan, G. Zhang, and W. Tan. 2014. Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud. IEEE Transactions on Automation Science & Engineering 11.2(2014), 564--573.

4. Yan Kang, He Lu, Jing He. 2013. A PSO-based Genetic Algorithm for Scheduling of Tasks in a Heterogeneous Distributed System. Journal of Software, 8(6).

5. G. Vidya, S. Sarathambekai, K. Umamaheswari, S.P. Yamunadevi. 2012. Task Scheduling Using Constriction Weighted Particle Swarm Optimization for Multi-Objectives. Procedia Engineering, 38.

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions;IEEE Access;2023

2. Applications of Big Data in Smart Health Systems;Advances in Healthcare Information Systems and Administration;2022-06-24

3. Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning;IEEE Access;2022

4. Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud;Computer Systems Science and Engineering;2022

5. Improving completion time and execution time using FSMPIA: A case study;INT J NONLINEAR ANAL;2022