Virtual Machine Placement Method with Compressed Sensing-Based Traffic Volume Estimation

In a cloud computing environment, a virtual machine placement (VMP) problem is considered for traffic load balancing, where each VM is placed on a physical host according to traffic loads in the physical hosts. In particular, VM is referred to as a node and a pair of nodes is considered a flow. To achieve load balancing, identifying flows that transfer heavy traffic volumes is necessary. We propose a VMP method with compressed sensing (CS)-based traffic volume estimation. In the proposed method, all flow traffic volumes are estimated by CS from the traffic volumes that nodes transmit and receive. VMs are placed according to the estimated flow traffic volumes by solving a combinatorial optimization problem. We validate the proposed method via simulation experiments.