Study on Load Balancing Scheduling Strategy based on Prediction and ACO Algorithm
Jianhua He, Peng Yan, Xia Long · 2020
It is difficult for cloud computing providers to provide quantitative computing resources for dynamic resource requests of cloud computing tasks in advance. The static scheduling algorithm cannot serve cloud computing tasks well without Pre-sense load status. To make better use of cloud computing resources and decrease the power consumption of datacenter, we proposed a load balancing scheduling strategy based on prediction and ant colony optimization algorithm. The prediction algorithm is used to sense the load of the datacenter, the ACO algorithm reasonably schedules the VMs to the hosts. The results of simulation show that datacenter energy consumption and SLA violation rate can be effectively reduced by using load balancing scheduling strategy based on prediction and ant colony optimization algorithm.