Aiming at the optimization of servers' energy consumption in data center and the reasonable migration time of Virtual Machine (VM), a VM migration algorithm based on Dynamic Adjusting Threshold (DAT) was proposed. Firstly, the migration threshold was dynamically adjusted by analyzing the historical load data acquired from Physical Machine (PM), then the time for migrating VMs was decided by the delay trigger mechanism and the PM load trend prediction. The VM migration algorithm based on DAT was tested on datacenter platform in the laboratory. Experimental results indicate that compared with the static threshold method, the number of the shut down PMs of the proposed algorithm is larger, and the energy consumption of the data center is lower. The VM migration algorithm based on DAT can dynamically migrate VMs according to the variation of PM load, thus improving the utilization of resources and the efficiency of VM migration, reducing the energy consumption of the data center.

To deal with the combinatorial optimization problem in the era of big data, a parallel Ant Colony Optimization (ACO) algorithm based on Spark, a framework for the distributed memory computing, was presented. To achieve the parallelization of the phase of solution construction in ant colony optimization, a class of ants was encapsulated to a resilient distributed dataset and the corresponding transformation operators were given. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. Under the same experimental environment, the comparison results between MapReduce based ant colony algorithm and the proposed algorithm show that the proposed algorithm significantly improves the optimization speed at least ten times than the MapReduce one.