Optimal and suboptimal resource allocation techniques in cloud computing data centers

Cloud service providers are under constant pressure to improve performance, offer more diverse resource deployment options, and enhance application portability. To achieve these performance and cost objectives, providers need a comprehensive resource allocation system that handles both computational and network resources. A novel methodology is introduced to tackle the problem of allocating sufficient data center resources to client Virtual Machine (VM) reservation requests and connection scheduling requests. This needs to be done while achieving the providers' objectives and minimizing the need for VM migration. In this work, the problem of resource allocation in cloud computing data centers is formulated as an optimization problem and solved. Moreover, a set of heuristic solutions are introduced and used as VM reservation and connection scheduling policies. A relaxed suboptimal solution based on decomposing the original problem is also presented. The experimentation results for a diverse set of network loads show that the relaxed solution has achieved promising levels for connection request average tardiness. The proposed solution is able to reach better performance levels than heuristic solutions without the burden of long hours of running time. This makes it a feasible candidate for solving problems with a much higher number of requests and wider data ranges compared to the optimal solution.