SHF: Small: Energy Saving in Heterogeneous Data Centers

University Of California-Riverside, Riverside CA

Investigators

Abstract

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.