Hybrid meta-heuristic VM load balancing optimization approach

Abstract

Cloud computing brings the business computing to a novel pattern from manufacturing to services. Clouds enhance the next generation data centers as a network of virtual computing services. Resource allocation and distribute should be as per Quality of Service (QoS) with Service Level Agreement (SLA) to decrease energy intake and carbon release. Cloud environment resources requests are concurrent and competitive. Efficient resources handling is elementary need in data center and should be performed through the implementation of efficient and dynamic load balancing algorithm. Optimization is a scientific discipline to discover optimal solution from numerous solutions for a problem. In NP-hard problem, finding the optimal solutions for algorithms is expensive. Hence, many of the proposed algorithms focus on searching approximate solutions for VM load balancing. Heuristic, meta-heuristic and hybrid optimization strategy gaining popularity as find optimized solution in reasonable time for complex problems. A hybrid meta-heuristic is proposed using nature inspired Genetic Algorithm and Particle Swarm Optimization approaches. Optimized results achieved in terms of performance when proposed approach evaluated on Cloud Analyst Simulation tool.

Subject Classification:

• 68T20

Keywords:

• Cloud Computing
• Optimization
• Load Balancing
• Genetic Algorithm
• Particle Swarm Optimization (PSO)