Power-aware optimization for heterogeneous multi-tier clusters
Journal of Parallel and Distributed Computing
Automated analysis of performance and energy consumption for cloud applications
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Energy efficiency is an important issue for data centers given the amount of energy they consume yearly. However, there is still a gap of understanding of how exactly the application type and the heterogeneity of servers and their configuration impact the energy efficiency of data centers. To this end, we introduce the notion of Application Specific Energy Efficiency (ASEE) in order to rank energy efficiency of heterogeneous servers based on the hosted applications. We conducted extensive sets of experiments using three benchmarks: TPC-W, BS Seeker, and Matrix Stress mark. We observed that each server has different ASEE value based on the type of application running, the size of the virtual machine, the application load, and the scalability factor. In some cases, we witnessed 70% of ASEE improvement by changing the virtual machine size within the same node while keeping an identical load. In different cases, we witnessed up to 86% of ASEE improvement by running the same application with the same load within the same size of virtual machine but on different nodes. Our observation has many implications which include but are not limited to improving virtual machine scheduling based on the ASEE rank of the node. Another implication stresses on the importance of accurate prediction of application load and selecting the appropriate virtual machine size in order to improve the ASEE.