SHF: Small: Energy Saving in Heterogeneous Data Centers

SHF：小型：异构数据中心的节能

• 批准号: 1815643
• 负责人: Daniel Wong
• 金额: $ 49.94万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815643&HistoricalAwards=false
• 关键词: SHF; Small; Energy; Saving; Heterogeneous

Many critical online services are turning to cloud infrastructure to meet scalability demands. In order to sustain cloud computing growth, it is necessary to scale the computational capacity, and improve the energy efficiency, of data centers. Modern data centers increasingly integrate accelerators, such as Graphical Processing Units (GPUs), with traditional CPUs to provide unprecedented parallelism and order-of-magnitude improvement to computational throughput. In order to manage software workloads and hardware resources at cloud-scale, data centers are increasingly being virtualized to provide ease of software and hardware management. However, existing energy efficiency techniques are not well-suited for virtualization technology and heterogeneous hardware. This project provides fundamental insights and solutions towards achieving energy-efficient computing for emerging virtualized heterogeneous data centers. This project has wide-reaching benefits for the computational engines behind many workloads of national interest, such as weather forecasting and machine learning. Results of this research are being integrated into the existing undergraduate and graduate courses.The research consists of three main thrusts: 1) development of heterogeneous benchmarks to evaluate server energy profile and metrics to quantify heterogeneous server energy proportionality in order to investigate the implications of virtualization and heterogeneous architectures, 2) development of software support for container live migration and performance-aware migration strategies to minimize live migration overhead, 3) development of management techniques to maximize energy proportionality of heterogeneous CPU and multi-accelerator systems, such as dynamic load balancing and automatic runtime task migration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多关键的在线服务正在转向云基础设施，以满足可扩展性需求。为了维持云计算的增长，有必要扩展数据中心的计算能力并提高能源效率。现代数据中心越来越多地将加速器（如图形处理单元（GPU））与传统CPU集成在一起，以提供前所未有的并行性和计算吞吐量的数量级改进。为了管理云规模的软件工作负载和硬件资源，数据中心越来越多地被虚拟化，以提供软件和硬件管理的便利性。然而，现有的能量效率技术并不很适合虚拟化技术和异构硬件。该项目为新兴的虚拟化异构数据中心实现节能计算提供了基本见解和解决方案。该项目对许多国家感兴趣的工作负载背后的计算引擎具有广泛的好处，例如天气预报和机器学习。这项研究的成果正在纳入现有的本科生和研究生课程。这项研究包括三个主要方面：1）开发异构基准以评估服务器能量概况和度量以量化异构服务器能量比例，以便调查虚拟化和异构架构的含义，2）开发对容器实时迁移和性能感知迁移策略的软件支持，以最大限度地减少实时迁移开销，3）开发管理技术以最大化异构CPU和多加速器系统的能量比例，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers

• DOI: 10.1109/icdcs.2019.00072
• 发表时间: 2019-07
• 期刊: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Liang Zhou;L. Bhuyan;K. K. Ramakrishnan-K.

PowerMorph: QoS-Aware Server Power Reshaping for Data Center Regulation Service

• DOI: 10.1145/3524129
• 发表时间: 2022-03
• 期刊: ACM Transactions on Architecture and Code Optimization (TACO)
• 作者: Ali Jahanshahi;N. Yu;Daniel Wong

BOW: Breathing Operand Windows to Exploit Bypassing in GPUs

BOW：呼吸操作数窗口以利用 GPU 中的绕过

• 发表时间: 2020
• 期刊: Proceedings of the 53rd IEEE/ACM International Symposium on Microarchitecture (MICRO'20
• 作者: Asghari Esfeden, Hodjat;Abdolrashidi, Amirali;Rahman, Shafiur;Wong, Daniel;Abu-Ghazaleh, Nael
• 通讯作者: Abu-Ghazaleh, Nael

DREAM: DistRibuted Energy-Aware traffic Management for Data Center Networks

• DOI: 10.1145/3307772.3328291
• 发表时间: 2019-06
• 期刊: Proceedings of the Tenth ACM International Conference on Future Energy Systems
• 作者: Liang Zhou;L. Bhuyan;K. K. Ramakrishnan-K.

Cottage: Coordinated Time Budget Assignment for Latency, Quality and Power Optimization in Web Search

Cottage：协调时间预算分配，以优化网络搜索中的延迟、质量和功耗

• DOI: 10.1109/hpca53966.2022.00017
• 发表时间: 2022
• 期刊: IEEE International Symposium on High-Performance Computer Architecture (HPCA
• 作者: Zhou, Liang;Bhuyan, Laxmi N.;Ramakrishnan, K. K.
• 通讯作者: Ramakrishnan, K. K.