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  Load Balancing in a Distributed Network Environment - An ACO Inspired Approach  
  Authors : Neeharika V, Sreenivas Sremath Tirumala, Gang Chen, Jayawardena C
  Cite as:

 

The success of nature inspired algorithms, particularly Ant Colony Optimization (ACO) on various optimization and scheduling problems has inspired to apply this approach on load balancing problem. Messor is one such approach based on AntNet which achieved limited success with some unaddressed problems. Extending the Messor's work, in this paper, we propose an ACO based approach to address load balancing problem. In our approach, Swarm Intelligent techniques aid in identifying a suitable node for task transfers from overloaded network nodes to under loaded nodes and ANNs aid in directional decision making process that drives the ant search. The proposed approach is evaluated against Messor with two types of load arrival strategies static and dynamic on different sized networks with 20, 40, 60, 80 and 100 nodes and the experimental results show that the proposed approach has resulted in achieving better performance.

 

Published In : IJCSN Journal Volume 4, Issue 3

Date of Publication : June 2015

Pages : 525 - 534

Figures :13

Tables : 02

Publication Link : Load Balancing in a Distributed Network Environment - An ACO Inspired Approach

 

 

 

Neeharika V : Department of Computing, Unitec Institute of Technology Auckland, New Zealand

Sreenivas Sremath Tirumala : School of Computer and Mathematical Sciences, AUT University Auckland, New Zealand

Gang Chen : Victoria University of Wellington Wellington, New Zealand

Jayawardena C : Victoria University of Wellington Wellington, New Zealand

 

 

 

 

 

 

 

Distributed Computing

Load Balancing

Ant Colony Optimization

Messor

This paper proposes a new load balancing approach for distributed networks, utilizing ACO and ANN. This work was motivated by the research works conducted in a P2P system called Messor and is further improvised by exploring the available literature and attempts to address the shortcomings of Messor. In the proposed approach, load information is collected by ants (ACO Inspired) which serve as mobile agents. These ants are aided by ANNs for optimal decision making to find a suitable node for job transfer. Based on this decision the process of task migration is carried out at the node level. The proposed approach is tested on five different sized networks and with two different job arrival strategies (fixed and dynamic) to analyze the possibility of performance variations. In the fixed job arrival strategy experiment (experiment 1), network load balance is achieved at iteration number 18. Whereas in a dynamic job arrival strategy (experiment 2), load balance is achieved in iteration number 19. This variation is due to the busyness of nodes performing their own jobs while attending to the jobs from overloaded nodes. Experiments are conducted considering the combination of the job arrival strategies and variable network sizes. The proposed approach showed consistent performance irrespective of the network size and job arrival strategies.

 

 

 

 

 

 

 

 

 

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