Load Balancing of Nodes in Cloud Using Ant Colony Optimization

TLDR

Ant colony optimization has been adapted for cloud and grid networks to achieve load balancing among nodes, ensuring smooth operation and cost efficiency even during peak usage. The paper proposes an ACO‑based algorithm to distribute workloads across cloud nodes. The algorithm uses ACO where ants continuously update a shared result set to guide load distribution. The modified algorithm outperforms the original by having ants share a single result set instead of individual ones.

Abstract

In this paper, we proposed an algorithm for load distribution of workloads among nodes of a cloud by the use of Ant Colony Optimization (ACO). This is a modified approach of ant colony optimization that has been applied from the perspective of cloud or grid network systems with the main aim of load balancing of nodes. This modified algorithm has an edge over the original approach in which each ant build their own individual result set and it is later on built into a complete solution. However, in our approach the ants continuously update a single result set rather than updating their own result set. Further, as we know that a cloud is the collection of many nodes, which can support various types of application that is used by the clients on a basis of pay per use. Therefore, the system, which is incurring a cost for the user should function smoothly and should have algorithms that can continue the proper system functioning even at peak usage hours.