Optimization Algorithms for Large-scale, Thermal-aware Storage Systems

本项目研究在超大型数据存储中心执行热管理时出现的优化问题。为了满足日益增长的数据管理需求，这样的存储中心可能包含数十万个硬盘和其他组件，并且通常始终处于活动状态。这些都会产生大量热量，因此必须对存储系统进行冷却以保持可靠性，从而导致大量的冷却成本。存储中心的冷却机制和工作负载分配错综复杂地联系在一起。该项目通过关注系统层次结构中不同级别的热建模和管理，开发了一门针对大型存储系统的热管理的一般科学。正在开发用于将数据访问任务分配给数据所在的特定盘、用于控制数千个任务和盘的调度和速度以优化服务质量、以及用于重新组织盘上的数据布局的热感知技术。该项目将在数据存储中心实现更好的热管理，这可能会显著减少由这些数据存储中心造成的碳足迹。该项目将培训几名博士生在该大学进行研究，并通过在工业研究实验室的实习进行研究。

This project investigates optimization problems that arise while performing thermal management in very large data storage centers. To satisfy the growing data management needs, such storage centers contain possibly hundreds of thousands of hard disks and other components, and typically are consistently active. These generate a lot of heat, and hence the storage system must be cooled to maintain reliability, resulting in significant cooling costs. The cooling mechanism and the workload assignments in a storage center are intricately tied together.This project is developing a general science of thermal management for large scale storage systems, by focusing on thermal modeling and management at different levels of the system hierarchy. Thermal aware techniques for allocating data access tasks to specific disks on which data is located, for controlling the schedules and speeds of thousands of tasks and disks to optimize quality of service, and for reorganizing data layouts on disks are being developed. This project will enable better thermal management in data storage centers, which can potentially result in significant reductions in the carbon footprint caused by those. The project will train several Ph.D. students in conducting research both at the University, and through internships at Industrial Research Labs.