Earticle

초록

영어

Load balancing of virtual machines is one of the most significant issues in cloud computing research. A common approach is to employ intelligent algorithms such as Ant Colony Optimization (ACO). However, there are two main issues with traditional ACO. First, ACO is very dependent on the initial conditions, which might affect the final optimal solution and the convergence speed. To solve this problem, we propose to employ Genetic Algorithm (GA) for ACO initialization. Second, ACO could arrive at local optimal point, and the convergence speed is typically low. Along this line, we introduce the idea of Simulated Annealing (SA) to avoid local optimal and accelerate the convergence. Lastly, our experiments show that our improved ACO achieves good performance in load balancing.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Preliminary and Problem Formulation
  3.1. ACO Basics
  3.2. Problem Formulation
 4. Load Balancing with Improved ACO
  4.1. Initialization
  4.2. Node Selection
  4.3. Pheromone Updates
 5. Experiment
 6. Conclusion
 References

키워드

저자

• Yang Xianfeng [ School of Information Engineering, Henan Institute of Science and Technology, Henan Xinxiang, China ]
• Li HongTao [ Hebi Polytechnic, Henan Hebi, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605