CSR: Small: Collaborative Research: Software Defined Energy Adaptation in Large Scale Data Centers

CSR：小型：协作研究：大型数据中心的软件定义能源适应

• 批准号: 1422921
• 负责人: Krishna Kant
• 金额: $ 25万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422921&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Software

Today many compute and data intensive applications are running on the computers in data centers. Therefore, data centers consume large amounts of energy. Within a data center, many compute, storage and networking components are coordinated together to support these applications. This project studies how to efficiently allocate resources in data centers to satisfy the requirements of these applications with guaranteed quality of service (QoS) and at the same time taking advantage of the available renewable (green) energy to reduce the total energy to be consumed. As the data centers increase in size and complexity, it is becoming clear that the traditional distributed control of their resources poses daunting problems in ensuring the desired agility and QoS support demanded by the enterprise applications. This problem is further compounded by the need for aggressive energy management in order to minimize energy cost, cope with power and thermal related constraints, and adapt to variability in the energy produced from local "green" sources. A distributed management of energy is particularly difficult since energy by its nature is a central resource that must be properly divided up among various infrastructure components in order to yield acceptable application performance. This project explores a flexible, policy driven, software defined mechanism to manage energy at all levels of the data center and for all major resources including networks, storage systems, and compute servers. A crucial aspect in this research is the coordination of energy management decisions at various levels which is essential to achieve optimal performance under given energy/thermal constraints. The project brings in the emerging concept of software defined resource management to seamlessly tackle the energy sustainability and corresponding QoS issues in data centers. The project also forms a pilot realization of the software defined data center, since energy adaptation requires an adept management of all major data center resources. A set of new algorithms in resource allocation, resource monitoring, QoS enforcement, energy allocation and distribution will be designed and developed. These algorithms will be implemented into a specific framework to demonstrate and validate the usage and benefits of the research. The broader impacts of the project include mechanisms to enhance energy sustainability of data centers and a comprehensive mechanism for software control of large computing systems. The project also augments the research being done in the NSF Industry/University Cooperative Research Center (IUCRC) on intelligent storage and will use it as a conduit for industry adoption of this research.

如今，许多计算和数据密集型应用程序都在数据中心的计算机上运行。因此，数据中心消耗大量能量。在数据中心中，许多计算，存储和网络组件共同协调以支持这些应用程序。该项目研究了如何在数据中心中有效分配资源，以通过保证的服务质量（QoS）满足这些应用的要求，同时利用可用可再生能源（绿色）能源来减少要消耗的总能量。随着数据中心的规模和复杂性的增加，越来越明显的是，对其资源的传统分布式控制在确保企业应用程序所需的所需敏捷性和QoS支持方面构成了艰巨的问题。为了最大程度地减少能源成本，应对功率和热相关的约束，并适应了从当地“绿色”来源产生的能量的可变性，因此需要积极的能源管理，进一步使这个问题更加复杂。 能源的分布式管理特别困难，因为能源本质上是一种中心资源，必须在各种基础设施组件之间正确划分，以产生可接受的应用程序性能。 该项目探讨了一种灵活的，策略驱动的软件定义机制，以管理数据中心的所有级别以及包括网络，存储系统和计算服务器在内的所有主要资源。这项研究的一个关键方面是在不同级别的能源管理决策的协调，这对于在给定能量/热约束下实现最佳性能至关重要。 该项目提出了新兴的软件定义资源管理的概念，以无缝解决数据中心中的能源可持续性和相应的QoS问题。该项目还构成了软件定义的数据中心的试点实现，因为能源适应需要对所有主要数据中心资源进行熟练的管理。资源分配，资源监控，QoS执法，能源分配和分配的一系列新算法将经过设计和开发。这些算法将被实施到一个特定的框架中，以证明和验证研究的使用和好处。 该项目的更广泛影响包括增强数据中心能源可持续性的机制以及大型计算系统软件控制的综合机制。该项目还增加了在NSF行业/大学合作研究中心（IUCRC）上对智能存储进行的研究，并将其用作该研究行业采用的渠道。