Hybrid Algorithms for Placement of Virtual Machines across Geo-Separated Data Centers

Cloud computing has recently emerged as a new technology for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard Generalized Quadratic Assignment Problem (GQAP). In this paper, we present a Greedy Randomized Adaptive Search Procedure (GRASP) and a Biased Random-Key Genetic Algorithm (BRKGA), both hybridized with a Path-Relinking strategy and a local search based on Variable Neighborhood Descent (VND) for solving this problem, also tested in instances of GQAP. We show that both algorithms are effective in solving quickly small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art.