SHF: Small: Energy Saving in Heterogeneous Data Centers

许多关键的在线服务正在转向云基础设施，以满足可伸缩性需求。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数量级的计算吞吐量改进。为了在云规模上管理软件工作负载和硬件资源，越来越多的数据中心被虚拟化，以提供软件和硬件管理的便利性。然而，现有的能效技术并不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。该项目对许多涉及国家利益的工作负载（如天气预报和机器学习）背后的计算引擎具有广泛的好处。这项研究的结果将被纳入现有的本科和研究生课程。该研究包括三个主要重点：1)开发异构基准来评估服务器能源概况和度量，以量化异构服务器能源比例，以调查虚拟化和异构架构的影响；2)开发支持容器动态迁移和性能感知迁移策略的软件，以最小化动态迁移开销；3)开发异构CPU和多加速器系统能量比例最大化的管理技术，如动态负载平衡和自动运行时任务迁移。该奖项反映了美国国家科学基金会的法定使命，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。

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.