Cloud Service Scheduling Algorithm Research and Optimization-Reference-Cited by-同舟云学术

Cloud Service Scheduling Algorithm Research and Optimization

Published:2017 Issue: Volume:2017 Page:1-7
ISSN:1939-0114
Container-title:Security and Communication Networks
language:en
Short-container-title:Security and Communication Networks

Author:

Cui Hongyan¹²^ORCID,Liu Xiaofei¹,Yu Tao³,Zhang Honggang⁴,Fang Yajun⁵,Xia Zongguo⁴

Affiliation:

1. State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

2. Key Lab of Network System Architecture and Convergence, Beijing 100876, China

3. Institute of Network Science and Cyberspace, Tsinghua University, Beijing, China

4. UMass Boston, 100 William T Morrissey Boulevard, Boston, MA 02125, USA

5. MIT CSAIL Lab, Cambridge, MA 02139, USA

Abstract

We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS). In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO) and a Genetic Algorithm (GA) to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO) are optimal.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Link

http://downloads.hindawi.com/journals/scn/2017/2503153.pdf

Reference14 articles.

1. Cloud federation in a layered service model

2. Adaptive scheduling strategies for cloud-based resource infrastructures

3. ProteoCloud: A full-featured open source proteomics cloud computing pipeline

4. Application of a Poisson distribution quality control measure to the analysis of two human hookworm drug treatment studies in Ghana

5. The cost of a cloud

Cited by 17 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. Industry 4.0 and industrial workflow scheduling: A survey;Journal of Industrial Information Integration;2024-03

2. A systematic literature review on soft computing techniques in cloud load balancing network;International Journal of System Assurance Engineering and Management;2023-12-20

3. A Distributed Microservice Scheduling Optimization Method;2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT);2023-04-28

4. Multi‐objective reliability‐based workflow scheduler: An elastic and persuasive task scheduler based upon modified‐flower pollination algorithm in cloud environment;Concurrency and Computation: Practice and Experience;2022-07

5. Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms;2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST);2021-12