With the rapid emerging of Internet of Things (IoT) devices and the proliferation of cloud-based applications, the cloud computing industry is becoming a vital element for ensuring our daily services. However, cloud computing uses large scale data centers equipped with energy-hungry servers and huge power facilities that massively consume power. This presents a real challenge which can negatively influence the power grid, while exposing the environment to global warming issues. Therefore, minimizing cloud data center power consumption is a challenging problem and has to be addressed. In this paper, we look at renewable energy in the context of a smart grid–cloud architecture and investigate the issue of grid power dispatching to cloud data centers. Since cloud data centers have a non-cooperative nature regarding power demand from the power stations, we model our power allocation problem as a non-cooperative game. Afterwards, we prove the existence and the uniqueness of Nash equilibrium. Moreover, we formulate the payoff function of our game as a non-linear optimization problem before resolving it using Lagrange multipliers and Karush–Kuhn–Tucker (KKT) conditions. Thus, we determine the assigned optimal quantity to each data center based on three main criteria : renewable energy usage, number of critical running applications and workload charge. Extensive simulations are performed by comparing our scheme with an existing work. Results show that our scheme outperforms the comparing approach with a percentage of 31.2% in terms of power load rate and significantly reduces emissions of carbon dioxide.

随着物联网 (IoT) 设备的快速兴起和基于云的应用程序的激增，云计算行业正成为确保我们日常服务的重要元素。然而，云计算使用配备耗能服务器和大量耗电的巨大电力设施的大型数据中心。这提出了一个真正的挑战，它可以对电网产生负面影响，同时使环境面临全球变暖问题。因此，最小化云数据中心功耗是一个具有挑战性的问题，必须解决。在本文中，我们在智能电网-云架构的背景下研究可再生能源，并研究将电网电力调度到云数据中心的问题。由于云数据中心在发电站的电力需求方面具有非合作性质，因此我们将电力分配问题建模为非合作博弈。然后，我们证明了纳什均衡的存在性和唯一性。此外，在使用拉格朗日乘数和 Karush-Kuhn-Tucker (KKT) 条件解决它之前，我们将游戏的收益函数公式化为非线性优化问题。因此，我们根据三个主要标准确定分配给每个数据中心的最佳数量：可再生能源使用量、关键运行应用程序的数量和工作负载费用。通过将我们的方案与现有工作进行比较，进行了广泛的模拟。结果表明，我们的方案以 31 的百分比优于比较方法。