Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations\nto outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in\ncloud users� demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial\namounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this\npaper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically\nassigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy\nconsumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate\nthe VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to\nfinding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based\napproach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more\nthan 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.
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