Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

中文翻译：

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云数据中心能量感知任务调度的分布松弛分配算法

由于高性能计算系统在这些领域的贡献，数据和计算中心的不断发展，由于二氧化碳排放可能对环境造成不利影响，能源消耗一直非常重要。近年来，在数据中心调度并行任务时，“能源”和“绿色计算”等概念发挥了至关重要的作用。复制和聚类策略以及动态电压和频率缩放 (DVFS) 技术都侧重于降低能耗和优化性能参数。针对采用DVFS技术的数据中心处理器的调度有向无环图（DAG），本文提出了一种基于双相调度的能量和时间感知算法，称为 EATSDCDD，应用复制和集群策略的组合以及集群任务之间的空闲时间分布。提议的绿色系统中的 DVFS 和控制程序被映射到基于 Petri 网的模型中，这有助于设计多重决策过程。在第一阶段，我们使用复制和集群策略的智能组合方法来运行 DAG 的即时任务，并通过专注于减少制造周期和处理器消耗的能量来监控吞吐量。所提出算法的主要思想涉及在第二阶段实现能源消耗的最大减少。为此，松弛时间分布在非关键的依赖任务之间。此外，我们基于绿色服务水平协议 (GSLA) 以 μ 的速率覆盖消费者和服务提供商之间的谈判问题，以实现更高的能源节约。最后，评估为进行检查而建立的一组数据以及构造的随机 DAG 的不同参数，以检查我们提出的算法的效率。获得的结果证实，我们的算法优于本研究中考虑的其他算法。