NSF funded research for developing energy saving techniques in heterogeneous data centers
Professor Daniel Wong (PI) and Distinguished Professor Laxmi Bhuyan (Co-PI) received a three-year $500K grant (CCF-1815643) from the National Science Foundation for developing new evaluation methodology to quantify heterogeneous server energy proportionality, container live migration support and strategies, and management for heterogeneous CPU and multi-accelerator systems.
More information about this new award can be found at https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815643
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.