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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力将来自开发热管理技术，该技术可以节省20%的电力消耗，并将数据中心的设备寿命提高25%。2013年，美国数据中心耗电量约910亿千瓦时，造成9700万公吨碳污染。到2020年，数据中心的年用电量预计将增加到约1400亿千瓦时，并造成1.5亿吨的碳污染。从理论上讲，低效的运营能源使用和数据中心过度配置的容量有可能节省40%的能源。建议的技术提高了数据中心的能效和容量利用率，并实现了一半的潜在节省。实现这一节约潜力的一个主要障碍是对数据中心停机的风险规避，数据中心停机的成本为7,900美元/分钟，91%的数据中心平均每年停机86分钟。建议的技术通过保护数据中心免受因过热导致的停机时间的影响来确保可靠性。这个SBIR第一阶段项目将在无处不在的信息技术和相关服务行业实施绿色方法，从而产生深远的社会影响。该小型企业创新研究第一阶段项目将展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置点，被迫在IT利用率较低和过度配置冷却的情况下运行。该公司的软件设备确定最具成本效益的冷却设置点，确保实时可靠的计算操作，并通过建筑管理系统自动实施这些设定点。鉴于建议的SA促进了工作负载比例冷却，它避免了浪费的冷却资源高峰调配。拟议SA的开发将涉及以下活动：-开发实时预测分析框架；-提取不同工作负载类型和冷却设置点的关键数据(例如IT设备温度数据)；以及-将最佳冷却设置点传输到建筑物管理系统以控制冷却系统。该项目的预期结果是一个SA展示了拟议技术在减少数据中心的冷却能源使用量和提高冷却容量利用率方面的概念验证和可行性。

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.