Next Article in Journal
Dynamic Gesture Recognition Based on MEMP Network
Next Article in Special Issue
Identity-as-a-Service: An Adaptive Security Infrastructure and Privacy-Preserving User Identity for the Cloud Environment
Previous Article in Journal
Social Engineering Attacks: A Survey
Previous Article in Special Issue
Nonlinear Analysis of Built-in Sensor in Smart Device under the Condition of Voice Actuating
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

1,2 and 3,*
1
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2
School of Mechanical Engineering, Baicheng Normal University, Baicheng 137000, China
3
School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(4), 90; https://doi.org/10.3390/fi11040090
Received: 19 February 2019 / Revised: 29 March 2019 / Accepted: 30 March 2019 / Published: 2 April 2019
(This article belongs to the Special Issue Security and Privacy in Information and Communication Systems)
  |  
PDF [2676 KB, uploaded 2 April 2019]
  |  

Abstract

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state. View Full-Text
Keywords: System Wide Information Management; ant colony optimization algorithm; hardware performance quality index; load standard deviation function; load balancing System Wide Information Management; ant colony optimization algorithm; hardware performance quality index; load standard deviation function; load balancing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, G.; Wu, Z. Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing. Future Internet 2019, 11, 90.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top