On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata

The problem, and consequently the solution, can, quite naturally, be compartmentalized into two phases which follow each other. In the first, the task is to consolidate VMs into clusters, where those that communicate with each other fall into the same cluster. The second phase assigns these clusters onto the available server racks. Both of these phases must be executed in a traffic-aware manner. This paper provides efficient intelligent solutions for both these phases. First of all, the VMs are consolidated with a VM clustering algorithm, and this is achieved by utilizing the toolbox involving Learning Automata (LA). By mapping the clustering problem onto the Graph Partitioning (GP) problem, our LA-based solution successfully reduces the total communication cost by amounts that range between 34% and 85%. Thereafter, the resulting clusters are assigned to the server racks using a cluster placement algorithm that involves a completely different intelligent strategy, i.e., one that invokes Simulated Annealing (SA). This phase further reduces the total cost of communication by amounts that range between 89% and 99%. The analysis and results for different models and topologies demonstrate that the optimization is done in a fast and computationally-efficient way.