PT-GA-IRIAL: Enhanced Energy Efficient Approach to Select Migration VMs for Load Balancing in Cloud Computing Environment

  • V. RadhamaniEmail author
  • G. Dalin
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 44)


Cloud computing is a very well known technology for all business people, software developers, end-users, and so on. Significant researches are going on to balance the cloud load. The migration of heavily loaded Virtual Machines (VMs) into lightly loaded Physical Machines (PMs) balances the Cloud load. In Resource Intensity Aware Load Balancing (RIAL) method, based on the weight of resources under utilization, it selected the VMs from heavily loaded PMs for migration and chosen the lightly loaded PMs as destination. An Improved RIAL was proposed to consider both lightly and heavily loaded PMs as destination. Later it was enhanced in the proposed Power Consumption Aware- Traffic Aware- IRIAL (PT-IRIAL) method with the consideration of power consumption, temperature and traffic measures to select the VMs for migration and select PMs for destination. From all these, in this current paper, the crossover and mutation process of GA is utilized to optimally select the migration VMs and choose the destination PMs. Thus this GA based load optimization algorithm optimally maps the migration VMs with the destination PMs efficiently.


Cloud computing Load balancing Genetic algorithm 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceCoimbatore Institute of Technology, Coimbatore, Hindusthan College of Arts and ScienceCoimbatoreIndia
  2. 2.Department of Computer ScienceHindusthan College of Arts and ScienceCoimbatoreIndia

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