Affiliation:
1. University of California, Riverside, USA
Abstract
Adoption of renewable energy in power grids introduces stability challenges in regulating the operation frequency of the electricity grid. Thus, electrical grid operators call for provisioning of frequency regulation services from end-user customers, such as data centers, to help balance the power grid’s stability by dynamically adjusting their energy consumption based on the power grid’s need. As renewable energy adoption grows, the average reward price of frequency regulation services has become much higher than that of the electricity cost. Therefore, there is a great cost incentive for data centers to provide frequency regulation service.
Many existing techniques modulating data center power result in significant performance slowdown or provide a low amount of frequency regulation provision. We present
PowerMorph
, a tight QoS-aware data center power-reshaping framework, which enables commodity servers to provide practical frequency regulation service. The key behind
PowerMorph
is using “complementary workload” as an additional knob to modulate server power, which provides high provision capacity while satisfying tight QoS constraints of latency-critical workloads. We achieve up to 58% improvement to TCO under common conditions, and in certain cases can even completely eliminate the data center electricity bill and provide a net profit.
Funder
NSF
California Energy Commission
University of California, Riverside
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Information Systems,Software
Reference96 articles.
1. Baris Aksanli and Tajana Rosing. 2014. Providing regulation services and managing data center peak power budgets. In Proceedings of the Conference on Design, Automation & Test in Europe (DATE’14). European Design and Automation Association, 3001 Leuven, Belgium, Belgium, 143:1–143:4. http://dl.acm.org/citation.cfm?id=2616606.2616782.
2. The Datacenter as a Computer
3. The need for speed and stability in data center power capping
4. Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS
5. AME-WPC: Advanced model for efficient workload prediction in the cloud
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Data center and load aggregator coordination towards electricity demand response;Sustainable Computing: Informatics and Systems;2024-04
2. An End-to-End HPC Framework for Dynamic Power Objectives;Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis;2023-11-12