Energy Aware Load Balancing Using Ant Colony Optimization Technique for Cloud Data Centers

Abstract

In recent years we can access the data through the internet from the different client devices
using remote servers, computers and databases from the Cloud. As the number of users of
cloud is growing enormously, the load on the data centers also has increased causing a load
imbalance among the data centre resources which in turn led to increased energy utilization in
the data centers. Also to satisfy the user demands, there is a rapid growth in the number of
cloud data centers which has led to vast amount of consumption of energy. In addition to this,
while attempting to minimize energy consumption, aggressive virtual machine (VM)
consolidation may results in to enormous number of VM migrations affecting the
performance of cloud data centers. It has become a big challenge to reduce energy usage
without violating Service Level Agreement (SLA) which was negotiated between service
providers (CSPs) and users of the cloud. To overcome the above stated issues, a Load
Balancing Factor Associated - Ant Colony Optimization (LBFA-ACO) technique is proposed
which reduces the consumption of energy and prevents violation of SLAs. Based on the task
performances, the hosts or VMs measures the fitness function. The proposed method LBFAACO
provides energy efficient computing for cloud data centre optimization. The simulation
results shows that proposed LBFA-ACO achieves better results in terms of energy efficiency
which is 49013 as compared to existing techniques Adaptive Threshold VM Consolidation
Framework (KMIMR- MRCPMB- 1.0 )with an energy efficiency of 30089 and Joint VM
Container Multi-Criteria Migration Decision (JVCMMD) which has achieved an energy
efficiency of 10001.