2014 IEEE 7th International Conference on Cloud Computing (CLOUD) (2014)
Anchorage, AK, USA
June 27, 2014 to July 2, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2014.23
This paper explores the opportunity for energy saving in data centers using the flexibility from the Service Level Agreements (SLAs) and proposes a novel approach for scheduling workload that incorporates use of renewable energy sources. We investigate how much renewable power to store and how much workload to delay for increasing renewable usage while meeting latency constraints. We present an LP formulation for mitigating variability in renewable generation by dynamic deferral and give two online algorithms to determine optimal balance of workload deferral and power use. We prove the feasibility of the online algorithms and show that their worst case performances are bounded by constant factors with respect to the offline formulation. We validate our algorithms by trace-driven simulation on MapReduce workload and collected and publicly available wind and solar power generation data. Results show that the algorithms give 20-30% energy-savings compared to the naive 'follow the workload' policy.
Optimization, Renewable energy sources, Heuristic algorithms, Algorithm design and analysis, Power generation, Prediction algorithms, Predictive models
M. A. Adnan and R. K. Gupta, "Workload Shaping to Mitigate Variability in Renewable Power Use by Data Centers," 2014 IEEE 7th International Conference on Cloud Computing (CLOUD), Anchorage, AK, USA, 2014, pp. 96-103.