The cloud-computing concept has emerged as a powerful mechanism for data storage by\nproviding a suitable platform for data centers. Recent studies show that the energy consumption of\ncloud computing systems is a key issue. Therefore, we should reduce the energy consumption to\nsatisfy performance requirements, minimize power consumption, and maximize resource utilization.\nThis paper introduces a novel algorithm that could allocate resources in a cloud-computing\nenvironment based on an energy optimization method called Sharing with Live Migration (SLM).\nIn this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs)\nbased on a novel algorithm that learns and predicts the similarity between the tasks, and then\nallocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services\n(QoS) constraints of the hosted applications by adopting a migration process. The experimental\nresults show that the algorithm exhibits better performance, while saving power and minimizing the\nprocessing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency\nand resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.
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