首页|RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment
RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
This paper proposes a hybrid metaheuristic based resource allocation framework named RAFL for load balancing in cloud computing environment. The objective is to proactively minimize the load imbalance across active physical machines and among their considered resource capacities (e.g., CPU and RAM). This prevents overloading/underloading of active physical machines and utilizes their considered resource capacities in a balanced manner. In the proposed framework, a phasor particle swarm optimization and dragonfly algorithm based hybrid optimization algorithm named PPSO-DA is used to generate an optimal resource allocation plan for balancing the load. Simulation experiments are performed using CloudSim simulator to measure the metrics of load imbalance across active physical machines and among their considered resource capacities. Results show that the proposed PPSO-DA algorithm outperforms phasor particle swarm optimization, dragonfly algorithm, comprehensive learning particle swarm optimization, memory based hybrid dragonfly algorithm, sine cosine algorithm, and elephant herding optimization, in finding an optimal resource allocation for balancing the load. The statistical analysis and benchmark testing also validates the relative superiority of PPSO-DA.
NSTL
Elsevier
