SBIR Phase I: Real-time Software-based Cooling Optimization for Data Centers

这项小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将源于热管理技术的开发，该技术可以节省20%的电力消耗，并将数据中心的设备寿命延长25%。2013年，美国数据中心消耗了大约910亿千瓦时的电力，造成了9700万吨的碳污染。到2020年，数据中心年用电量预计将大约增加到1400亿千瓦时，并造成1.5亿吨碳污染。从理论上讲，数据中心中低效的运营能源使用和过度配置的容量可以节省40%的能源。提出的技术提高了数据中心的能源效率和容量利用率，实现了一半的潜在节约。实现这一节约潜力的主要障碍是对数据中心停机时间的风险规避，这一成本为7900美元/分钟，91%的数据中心平均每年经历86分钟的停机时间。该技术通过防止数据中心因过热而停机来确保可靠性。SBIR一期项目通过在无处不在的信息技术和相关服务行业实施绿色方法，有望产生深远的社会影响。这个小型企业创新研究阶段I项目将演示解决数据中心冷却优化问题的软件设备（SA）的可行性。由于IT和冷却操作之间的时间尺度差异以及相关设备的多样性，数据中心运营商无法实时确定可靠的冷却设定点，并被迫在低IT利用率和过度配置冷却的情况下运行。该公司的软件设备确定了最具成本效益的冷却设定点，确保可靠的计算操作，并通过建筑管理系统实时自动实施。考虑到所提出的SA促进工作负载比例冷却，它避免了浪费的冷却资源峰值分配。制定建议的安全评估将涉及以下活动：-制定实时预测分析框架；-提取不同工作负载类型和冷却设定值的关键数据（例如IT设备温度数据）；并将最佳冷却设定点传递给建筑管理系统来控制冷却系统。该项目的预期结果是一个SA，演示概念验证，以及拟议技术在减少冷却能源使用和提高数据中心冷却能力利用率方面的可行性。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from development of a thermal management technology that could save 20% of electricity consumption and increase equipment life-time by 25% in data centers. In 2013, U.S. data centers consumed approximately 91 billion kWh of electricity and caused 97 MMT of carbon pollution. By 2020, the data center annual electricity consumption is projected to increase roughly to 140 billion kWh and cause 150 MMT carbon pollution. Inefficient operational energy usage and over-provisioned capacity in data centers theoretically offer the potential for 40% energy savings. The proposed technology improves data centers' energy efficiency and capacity utilization and realizes half of the potential savings. A major barrier to realizing this savings potential is the risk aversion toward data center downtime that costs $7,900/min, with 91% data centers experiencing an average 86 min outage per year. The proposed technology ensures reliability by safeguarding data centers from downtime due to overheating. This SBIR Phase I project is poised to have a far-reaching societal impact by implementing green methods in the ubiquitous information technology and related service industries. This Small Business Innovation Research Phase I project will demonstrate the feasibility of a software appliance (SA) that addresses cooling optimization for data centers. Due to time-scale differences between IT and cooling operations and diversity of interrelated equipment, data center operators fail to determine reliable cooling set-points in real-time and are compelled to operate with low IT utilization and over-provisioned cooling. The company's software appliance determines the most cost-efficient cooling set-points, that ensure reliable computing operations, in real-time and automatically implements them through the building management system. Given that the proposed SA promotes workload-proportional cooling, it avoids wasteful cooling resource peak provisioning. The development of the proposed SA will involve the following activities:- development of a real-time predictive analytics framework;- extraction of critical data (e.g. IT device temperature data) for different workload types and cooling set-points; and- transferring optimal cooling set-points to the building management system to control cooling systems.The anticipated result for the project is an SA demonstrating proof-of-concept, and feasibility of the proposed technology in cooling energy usage reduction and cooling capacity utilization improvement of data centers.