VCE-PSO: Virtual Cloud Embedding through a Meta-heuristic Approach

Resource allocation, an integral and continuously evolving part of cloud computing, has been attracting a lot of researchers in recent years. However, most of current cloud systems consider resource allocation only as placement of independent virtual machines, ignoring the performance of a virtual machine is also depending on other cooperating virtual machines and also the net links utilization, which result in a poor efficient resource utilization. In this paper, we propose a novel model Virtual Cloud Embedding (VCE) to formulate the cloud resource allocation problem. VCE regards each resource request as an integral unit rather than independent virtual machines including their link constraints. To address the VCE problem, we develop a meta-heuristic algorithm VCE-PSO, which is based on particle swarm optimization algorithm, to allocate multiple resources as a unit considering the heterogeneity of cloud infrastructure and variety of resource requirements. We exploit specific knowledge like the locations of virtual machines, inter-link distance, etc., to measure the fitness of different resource assignments, and utilize them to define the assignment update operation corresponding to the operations and steps of particle swarm optimization algorithm. Experiment results demonstrate that VCE-PSO can find an optimal resource assignment with 12% reduction of average link-mapped-path length than existing greedy algorithms.