Cloud computing, Multi-tier applications, energy efficiency, communication-aware scheduling


Computer Sciences


the increasing demand for cloud computing services has led to the adoption of large-scale cloud data centers (DCs) to meet the user’s requirements. Efficiency and managing of such DCs have become a challenging problem. Consequently, energy-efficient solutions to optimize the whole DC energy consumption, optimize the application’s performance and reduce the cloud provider operational cost are crucial and needed. This paper addressed the problem of Virtual Machines (VMs) placement of multi-tier applications to maximize the compute resources utilization, minimize energy consumption, and reduce network traffic inside modern large-scale cloud DCs. The VM placement problem with communication dependencies among the VMs is modeled as an optimization problem. In this context, to solve the proposed problem, that formulated as a variant of a multiple knapsack problem, an adaptive genetic algorithm is implemented to find a near-optimal solution to the NP-complete modeled optimization problem. To validate the efficacy of the proposed model, extensive simulations are conducted using CloudSimSDN simulator. The experimental results validate the usefulness of the proposed model and its effectiveness in reducing DC energy consumption and optimize network traffic inside DC.

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Title page





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