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

最小化能源成本和提高数据中心的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。一般来说，在数据中心的物理环境和数据中心的管理软件/系统软件调度器之间没有自动的反馈控制回路，用于制定热和节能的资源管理决策。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特性，例如冷却能力、硬件热特性和工作负载配置文件。针对这个问题的集成的、面向系统的方法可以提供几个显著的好处。这项工作将研究物理基础设施和集群操作系统的资源管理功能之间独特合并的方法，以全面了解数据中心管理，并做出全局（在数据中心级别）、功率感知和热感知的作业调度决策。该项目旨在为电力有限的数据中心开发一种在线、动态和自动化热管理和控制框架的综合解决方案。这些方法包括一种综合的双管齐下的方法，使用抽象的热模型将快速热评估纳入数据中心的资源管理功能；即：新的热意识调度技术将通过考虑工作安置的时空热影响，使传统调度技术认识到其调度决策的热性能，从而开发新的热意识调度技术。这就需要制定调度决策，不仅要考虑作业特征，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。这些热感知调度技术将使用新的性能指标进行综合评估，例如每运行成本的利用率。

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.