Data centers handle impressive high figures in terms of energy consumption, and the growing popularity of Cloud applications is intensifying their computational demand. Moreover, the cooling needed to keep the servers within reliable thermal operating conditions also has an impact on the thermal distribution of the data room, thus affecting to servers' power leakage. Optimizing the energy consumption of these infrastructures is a major challenge to place data centers on a more scalable scenario. Thus, understanding the relationship between power, temperature, consolidation and performance is crucial to enable an energy-efficient management at the data center level. In this research, we propose novel power and thermal-aware strategies and models to provide joint cooling and computing optimizations from a local perspective based on the global energy consumption of metaheuristic-based optimizations. Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies that may be used during runtime. This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74\% while maintaining quality of service.

翻译：数据中心在能源消耗方面呈现出惊人的高数值，而云应用的日益普及正不断加剧其计算需求。此外，为维持服务器在可靠热运行条件下所需的冷却，也会影响数据机房的热分布，进而影响服务器的功耗泄漏。优化这些基础设施的能源消耗是将数据中心置于更具可扩展性情景下的重大挑战。因此，理解功率、温度、整合与性能之间的关系，对于在数据中心层面实现节能管理至关重要。在本研究中，我们提出了新颖的动态功率与热感知策略及模型，基于元启发式优化的全局能耗，从局部视角实现冷却与计算的联合优化。我们的结果表明，元启发式算法与最佳适配递减算法的联合感知能力，使我们能够将全局能耗描述为可在运行时使用的更快速、更轻量级的优化策略。该方法使我们能够在保持服务质量的同时，将数据中心的能源效率（兼顾计算与冷却基础设施）提升高达21.74%。