2015 International Conference on Parallel Architecture and Compilation (PACT) (2015)
San Francisco, CA, USA
Oct. 18, 2015 to Oct. 21, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACT.2015.59
Nowadays, data centers consume about 2% of the worldwide energy production, originating more than 43 million tons of CO2 per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability, and SLA constraints among others. Also, workload variation impacts on the performance of two of the main strategies for energy-efficiency in Cloud data centers: Dynamic Voltage and Frequency Scaling (DVFS) and Consolidation. Our work proposes two contributions: 1) a DVFS policy that takes into account the trade-offs between energy consumption and performance degradation; 2) a novel consolidation algorithm that is aware of the frequency that would be necessary when allocating a Cloud workload in order to maintain QoS. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 39.14% for scenarios under dynamic workload conditions.
Cloud computing, Optimization, Energy efficiency, Servers, Energy consumption, Heuristic algorithms, Quality of service,Cloud Computing; Dynamic Voltage and Frequency Scaling; Dynamic Consolidation; Energy Efficiency,
"DVFS-Aware Consolidation for Energy-Efficient Clouds", 2015 International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 494-495, 2015, doi:10.1109/PACT.2015.59