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چکیده
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Cloud computing has fundamentally reshaped how individuals and organizations access and manage information, supported by a vast infrastructure of data centers globally. With approximately 500,000 data centers in operation, the energy consumption associated with these facilities has become a critical concern, as data centers are projected to account for up to 4.5% of global energy use by 2025. This growing energy demand has driven the need for more efficient resource management strategies, particularly within the context of cloud computing. One of the most effective techniques for achieving energy efficiency in cloud environments is virtual machine consolidation (VMC). This process involves the dynamic allocation and reallocation of virtual machines (VMs) across physical machines (PMs) to optimize resource utilization. By concentrating VMs onto a minimal number of active PMs, VMC allows idle PMs to be powered off or transitioned into low-power states, thereby reducing overall energy consumption. The dynamic nature of VMC is particularly advantageous in cloud computing, where workloads fluctuate and demand adaptive resource management. This thesis introduces a mixed-integer linear programming (MILP) model designed to address the dynamic virtual machine consolidation challenge. The model is structured to minimize energy consumption while ensuring that the required quality of service (QoS) is maintained. The consolidation process is divided into four key sub-problems: identifying underloaded PMs, detecting overloaded PMs, selecting VMs for migration, and determining the optimal destination PMs for these VMs. The proposed model leverages the flexibility of cloud environments, utilizing live migration techniques to reallocate VMs with minimal performance impact. To validate the model, standard mixed-integer programming solvers such as CPLEX, and SCIP are employed. The results demonstrate the model’s effectiveness in reducing the number of active PMs and, consequently, the overall energy footprint of cloud data centers. By addressing both energy efficiency and service quality, this research contributes to more sustainable and cost-effective cloud computing operations.
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