Abstract
Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.
Highlights
With the rapid increase in demand for service-oriented computing, in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established worldwide. ese huge data centers consume power at a large scale, which results in a high operational cost [1]
Based on the proposed best heuristic scheduling (BHS) algorithm, a sorting method was selected for each task, resulting in the most optimal response. is has improved the proposed method compared to the multiheuristic resource allocation algorithm (MHRA) method
We have presented a dual-objective scheduling algorithm that is aware of makespan and energy consumption in order to allocate resources to tasks and to sort tasks, based on heuristic methods and grasshopper algorithm
Summary
With the rapid increase in demand for service-oriented computing, in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established worldwide. ese huge data centers consume power at a large scale, which results in a high operational cost [1]. Ese huge data centers consume power at a large scale, which results in a high operational cost [1] They emit greenhouse gases such as carbon dioxide [2,3,4,5,6,7] and produce adverse effects on the environment [8,9,10]. E main idea of green computing is to enable the effective and efficient use of resources, by designing algorithms and methods that can reduce energy consumption. To achieve this goal, data centers must manage their resources with the use of efficient energy reduction techniques [14]. We attempt to minimize makespan and energy consumption based on the importance factor determined by the enduser and the provider, by the utilization of resource allocation in a queue created by four heuristics methods and the grasshopper algorithm. e most appropriate method to implement each task is chosen using the roulette wheel
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