A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling.

中文翻译：

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保证延迟约束的绿色数据中心时间感知多应用任务调度

越来越多的公司在绿色数据中心（GDC）中部署其应用程序，并为全球用户的任务提供服务。当前，越来越多的GDC供应商旨在通过部署绿色能源设施和减少棕色能源消耗来最大化其利润。但是，收入，电网价格和任务延误范围内的绿色能源的时间变化使GDC提供者要实现利润最大化，同时严格保证所有已接受任务的延误约束，具有挑战性。与现有研究不同，提出了一种时间感知任务调度（TATS）算法，该算法研究时间变化并调度所有允许的任务以在满足其延迟范围的GDC中执行。此外，本文提供了任务拒绝和服务率的数学模型。在每次迭代中 TATS通过基于模拟退火的混合混沌粒子群优化解决了公式化的利润最大化问题。与几种现有的调度算法相比，TATS可以提高利润和吞吐量，而不会违反所有已接受任务的延迟约束。给从业者的注意事项-本文研究了绿色数据中心（GDC）的利润最大化问题，同时满足了所有已接受任务的延迟约束。以前的任务调度算法不会共同调查收入，绿色能源和电网价格的时间变化。因此，它们无法满足所有已接受任务的延迟约束。本文提出了一种克服现有算法缺点的新方法。它是通过使用混合元启发式算法解决约束的非线性优化问题而获得的。仿真结果表明，与几种现有算法相比，该算法既提高了吞吐量，又提高了利润。它可以很容易地并入现实生活中的工业GDC。未来的工作需要研究GDC对拟议的时间感知任务计划的修复/故障影响。