Dynamic Load Balacing in Cloud Computing Using Hybrid Particle Swarm Optimization Based Ga Alogorithm
Cloud Computing is the most recent communication network, as well as a common archetype for hosting applications and distributing services over the internet. Virtualization is the most important cloud computing technique because it allows developers to create applications, dynamically share resources, and provide a range of services to cloud consumers. A service provider can ensure Quality of Service to users while also lowering server usage and increasing energy efficiency through virtualization. The problem of VM placement and job scheduling is one of the most significant challenges in the cloud computing world. This paper deals with the problem of VM placement and task scheduling in cloud environment for load balancing. The primary aim of load balancing is to use resources and to improve performance. It also eliminates nodes that have a lot of data or are overwhelmed, as well as nodes that aren't doing well or are just doing a small amount of work. By combining particle swarm optimization (PSO) and genetic algorithm, we proposed a hybrid algorithm, namely particle swarm optimization and genetic algorithm (HPSOGA) (GA). Our hybrid algorithm, along with the normal PSO and genetic operators, has an important objective function.