With the rapidly increasing computing demand, data centers become more and more power-hungry, which incurs substantial electricity cost. Meanwhile, due to the time-dependent demand preference, power grid is suffering high load variations, which results in a large profit loss. In this paper, we consider a cost-efficient workload scheduling with a coordination between a cloud service provider operating multiple geo-distributed data centers and smart grids. The aim is to explore the flexibility of data center power demands to reduce the cost of the cloud service provider and smooth the load variations of smart grids simultaneously. We first present the penalty model of the computation workload scheduling at each data center, and introduce the cost model of smart grids, including power generation cost and the cost due to the power load variations. To jointly minimize the cost of smart grids and penalty of the cloud service provider resulted from workload scheduling, we formulate the objective function as a weighted sum of the cost and the penalty to study the tradeoffs, and obtain the optimal offline solution by the dual decomposition technique. In order to make the coordination implemented in an online fashion, we propose a Receding Horizon Control (RHC) based online algorithm to obtain the suboptimal workload management based on the predicted information, including the future amounts of interactive workload, batch workload, and power load, in the prediction horizon. The simulation results show that with the coordination between the cloud service provider and smart grids, the cost of smart grids can be significantly reduced, by up to 20 percent, and the load variations of smart grids can be well smoothed simultaneously.

中文翻译：

---

协调地理分布式数据中心的工作负载调度和智能电网的发电

随着计算需求的快速增长，数据中心变得越来越耗电，这会产生大量的电力成本。同时，由于需求偏好随时间变化，电网负荷变化较大，利润损失较大。在本文中，我们考虑了一种具有成本效益的工作负载调度，在运营多个地理分布式数据中心和智能电网的云服务提供商之间进行协调。目的是探索数据中心电力需求的灵活性，以降低云服务提供商的成本并同时平滑智能电网的负载变化。我们首先提出了每个数据中心计算工作负载调度的惩罚模型，并介绍了智能电网的成本模型，包括发电成本和电力负载变化引起的成本。为了共同最小化工作负载调度导致的智能电网成本和云服务提供商的惩罚，我们将目标函数制定为成本和惩罚的加权和以研究权衡，并通过对偶分解获得最优离线解技术。为了使协调以在线方式实施，我们提出了一种基于后退地平线控制（RHC）的在线算法，以根据预测信息获得次优的工作负载管理，包括未来的交互工作负载、批量工作负载和功率负载，在预测范围内。仿真结果表明，通过云服务提供商与智能电网的协调，智能电网的成本可以显着降低，最高可达20%，