Abstract: Aiming at the virtual machine deployment problem in the cloud computing environment, based on the defining of the match-distance between the virtual machine and the server, the ant colony optimization(ACO) was used to re- search the deployment scheme. And the ACO was extended and modified for the deployment problem. Using the proba- bilistic tour decision with performance apperceive policy, the virtual machines with the same performance interest arc designedly placed in different servers to reduce the competition of the hardware resources. And using the single ant pheromone update rules, the misdirection of the inaccurate heuristic information is avoided. I}he parameter values for the arithmetic were researched with the experiments in C1oudSim. Finally, the performance of the extended ACC) was com- pared with that of the ranking deployment arithmetic and the original ACO. The experimental results show that the ex- tended ACO meets the need of the system load balancing better, and accelerates the convergence to the original ACO.

Key words: Cloud computing, Virtual machine, Ant colony optimization, Pheromone, Load balancing