Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases\nthe cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus\nenvironmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and\nefficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In\nthis paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed\nacyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by\nsorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform\neach task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the\nright time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor\ndetermined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of\nvirtual machines.
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