A Virtual Machine Deployment Approach Using Knowledge Curves in Cloud Simulation

Optimal deployment of simulation virtual machines is an important issue in Cloud Simulation. Challenges involve resource cost prediction for simulation tasks as well as host physical machine selection for simulation virtual machines. In this paper we propose a novel approach using knowledge curves (i.e., curves as knowledge base) to solve this problem. First we present a resource cost estimation algorithm using empirical load curves synthesis, and then discuss a deployment target host selection algorithm by curves matching. This approach can provide a promising solution for intelligent deployment of virtual machines in Cloud Simulation. In addition, the proposed approach will be increasingly precise and effective as curve knowledge base increases.