Data center profits are directly impacted by electric power consumption, which in turn has critical environmental implications. Geo distributed data centers maximize profits by optimizing resource allocation and leveraging diverse power tariffs available in a deregulated market. However, a power network state apathetic profit maximization can destabilize the grid and hamper a data center's sustainability goals. In this paper, we formulate the data center profit maximization as a constrained optimization problem. We then propose a multi-level algorithm where the lower level scheme is based on evolutionary optimization. The algorithm simultaneously optimizes revenue and expenses while preserving QoS and power network stability. The proposed approach considers real-time demand-based energy price variations to orchestrate request routing and resource allocation. The approach is suitable for heterogeneous and homogeneous data center architectures. The proposed scheme is also thermal-aware and considers both computing and cooling consumption. Simulation results show that the proposed technique is effective; it achieves a higher profit over a broad range of price variations and data center utilization levels.

数据中心的利润直接受到电力消耗的影响，而电力消耗又对环境产生了至关重要的影响。地理分布式数据中心通过优化资源分配并利用放松管制的市场中可用的各种电价来最大化利润。但是，电网状态冷淡的利润最大化会破坏电网的稳定性，并妨碍数据中心的可持续性目标。在本文中，我们将数据中心利润最大化公式化为约束优化问题。然后，我们提出了一种多级算法，其中低级方案是基于进化优化的。该算法可同时优化收入和支出，同时保留QoS和电网稳定性。提出的方法考虑了基于实时需求的能源价格变化，以协调请求路由和资源分配。该方法适用于异构和同类数据中心架构。所提出的方案也是热感知的，并且考虑了计算和冷却消耗。仿真结果表明，该方法是有效的。通过广泛的价格变化和数据中心利用率水平，它可以获得更高的利润。