Energy Efficient Dynamic Particle Swarm Optimization (EEDPSO) Resource Allocation in Cloud Computing

In cloud computing, a key characteristic is On-demand resource management. For proper resource allocation, fair computational resource sharing should be done by cloud providers. Better resources should be allocated to all users. Resource utilization enhancement is also focused by reducing resource fragmentation, where virtual machines are mapped to physical servers. In cloud environment, an adaptive resource allocation mechanism is proposed in this recent work. A limited resource quantity mapping to independent user for finishing their jobs is focused. However, based on operational costs, there will be an increase in energy requirement for operating cloud infrastructure. According to literature, energy minimization is focused in this work by CPU utilization regulation while operating at maximum frequency. For computing energy consumption, resource utilization and fairness, introduced a Dynamic Particle Swarm Optimization (DPSO) model. The computation is done while executing jobs by VMs on cloud computing resources in absent presence. The Google workload trace is used in simulation and resource wastage are minimized using proposed algorithm and achieves a better utilization of resource when compared with other allocation techniques as demonstrated in results.