Minimizing response latency via efficient virtual machine placement in cloud systems

As more and more applications migrate into clouds, the placement of virtual machines for these applications has much impact on the performance of cloud systems. A number of virtual machine (VM) placement techniques have been proposed over recent years. However, most of the existing works on VM placement ignore the response latency of the requests from tenants. In this paper, we investigate the techniques of VM placement with stochastic requests from the tenants to minimize the total (average) response latency. We first model the requests for each application from the corresponding tenant as independent Poisson stream. Moreover, the VMs are modeled as simple M/M/1 queueing systems. Then, we define the problem of VM placement for minimizing the total response delay (VMMD) and show it is NP-hard. We propose three heuristic algorithms, namely, Greedy, Local Adjustment (LA) and Simulated Annealing (SA). We conduct abundant simulation experiments to evaluate the performance of our proposed algorithms. The simulation results show that the proposed algorithms are efficient in decreasing the total response latency of the requests from tenants. Especially, the SA heuristic, which decreases the total response latency about 68% at most, shows the best performance on minimizing the total response latency in cloud systems.