Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms

Cloud computing is expanding gradually as the number of educational applications rapidly increasing. To get Educational cloud services, internet connectivity predominantly important and Environment uses one basic technology to manage Physical servers effectively ie; Virtualization Technology. In Computing, data centers host numerous Virtual Machines (VMs) on top Servers. Due rapid growth platforms, workload VM computationally getting increased. execute jobs IT resources are provisioned over network. Since generated from client-side dynamic in nature, it difficult allocate computational efficiently. So enhance energy efficiency provide an optimized way, a Scheduling mechanism with Hybrid Genetic Algorithm-Modified Particle Swarm Optimization (GA-MPSO) proposed this work achieve QoS parameters like reduced Energy consumption, SLA violation, cost reduction heterogeneous environments. The G-MPSO develops optimal range improves best scheduling VMs (PMs). approach, when compared other algorithms, intensifies consumption 105KWH, violation rate 0.08%, reduces migrations count 2122, consumes overall 2567.68$. different methods for evaluated against results, which show that GA-MPSO method far better than existing algorithms.