SGER--CSR/SMA: Thermal Aware Dynamic Resource Management for Datacenters

最小化数据中心的能源成本和提高热性能是优化计算资源和最大限度地利用数据中心计算能力的关键问题之一。通常，在数据中心的物理环境与数据中心的管理软件/系统软件调度器之间不存在用于做出热和功率高效的资源管理决策的自动反馈控制回路。数据中心的能源成本和热分布取决于大量多模态系统变量的多尺度属性，例如冷却能力、硬件热特性和工作负载配置文件。一个集成的，面向系统的方法towardsthis问题可以提供几个显着的好处。这项工作，将调查的物理基础设施和集群操作系统的资源管理功能之间的独特合并的方法，采取数据中心管理的整体视图，并作出全球（在数据中心的水平），功率感知，和热感知的作业调度决策。该项目旨在为功率受限的制冷机开发一个全面的在线、动态和自动化热管理和控制框架解决方案。该方法包括一个集成的双管齐下的方法，将快速热评估，使用抽象的热模型，在资源managementfunctions的的企业，即：新的热感知调度技术将开发通过传统的调度技术认识到他们的调度决策的热性能，考虑到时空热影响的工作安排。这就需要做出调度决策，不仅要考虑作业特性，还要考虑计算节点的物理位置、异构系统的热特性和热力学。这些热唤醒重新调度技术将全面评估使用新的性能metricssuch如利用每运营成本。

Minimizing the energy cost and improving thermalperformance of datacenters are among the key issues towards optimizing computingresources and maximally exploiting a datacenter's computation capability. In general,there is no automated feedback control loop between the physical environment of a datacenter, and the datacenter's management software/system software scheduler for makingthermal and power-efficient resource management decisions. The energy cost and thermaldistribution of a datacenter depends on the multi-scale properties of a large number ofmulti-modality system variables, such as cooling capacity, hardware thermalcharacteristics, and workload profiles. An integrated, system-oriented approach towardsthis problem could provide several significant benefits.Thiswork, will investigate approachers for a unique merger between physical infrastructure and the resourcemanagement functions of the cluster operating system to take a holistic view of data centermanagement, and make global (at the level of a datacenter), power-aware, andthermal-aware job scheduling decisions. The project seeks to develop a comprehensive solutionfor on-line, dynamic and automated thermal management and control framework for thepower-limited datacenters. The methods include an integrated two-pronged approach for incorporating fast thermal evaluation, using an abstract heat model, in the resource managementfunctions of the datacenters; namely: new thermal aware scheduling techniques will be developedby making traditional scheduling techniques cognizant of thermal performance of theirscheduling decisions by taking into account the spatio-temporal thermal implications ofjob placements. This entails making scheduling decisions by taking into account not onlyjob characteristics but also physical placement of computational node, the thermalcharacteristics of heterogeneous systems, and thermodynamics. These thermal-awarescheduling techniques will be comprehensively evaluated using new performance metricssuch as utilization per operating cost.