I-Corps: DataCOOL Software Application

数据中心（DC）是信息技术（IT）设备（如计算机服务器、网络交换机和存储设备）的数字仓库。为了保持其计算的可靠性，来自DC设施的废热通过专用的冷却硬件（例如计算机房空调单元和后门热交换器）移除。由于这些冷却硬件系统需要40-50%的数据中心电力消耗（通常为数百万千瓦时），因此其节能运行对于优化DC的总拥有成本至关重要。 该团队开发了一个实时软件应用程序，可为数据中心的不同冷却硬件推荐最佳冷却设定点。拟议的软件（DataCOOL）将为数据中心提供实时端到端冷却资源优化软件。DataCOOL的关键价值主张在于其数据中心冷却成本优化，这源于弹性资源分配和随机数据中心流量的风险缓解。这些值是从模型阶数减少算法中得出的。这项技术可以节省数据中心每年15-25%的运营支出。 广泛的文献调查表明，所提出的方法是从现有的方法有很大的不同。主要目标客户将包括拥有数据中心的大型IT公司，如：思科，微软，谷歌，亚马逊和Facebook。另一方面，该团队还希望与Equinix、Internap和QTS等中型数据中心公司密切合作，以验证拟议的原型是否能够提供声称的成本节约优势。

Data centers (DC) are digital warehouses for information technology (IT) equipment such as computer servers, network switches, and storage devices. To maintain their computational reliability, waste heat from the DC facility is removed by dedicated cooling hardware such as computer room air conditioning units and rear door heat exchangers. Because these cooling hardware systems demand 40-50% of the data center electricity consumption (often in the order of few million kWh), their energy-efficient operation is imperative for the optimization of total cost of ownership for a DC. This team has developed a real-time software application that will recommend the optimal cooling set-points for different cooling hardware in a data center.The proposed software (DataCOOL) will deliver real-time end-to-end cooling resource optimization software for data centers. The critical value proposition of DataCOOL lies in its data center cooling cost optimization, derived from elastic resource allocation and risk mitigation from stochastic data center traffic. These values are derived from a model-order reduction algorithm. The proposed technology could potentially save 15-25% of a data center's annual operational expenditure. An extensive literature survey indicates that the proposed methodology is substantially different from the existing approaches. The primary target customers would include large IT companies with data centers such as: Cisco, Microsoft, Google, Amazon, and Facebook. On the other hand, the team also expects to work closely with mid-size data center companies like Equinix, Internap, and QTS to validate that the proposed prototype is delivering claimed cost-saving benefits.