Power consumption costs takes upto half of operational expenses of datacenters making power management a critical concern. Advances in processor technology provide fine-grained control over operating frequency and voltage of processors and this control can be used to tradeoff power for performance. Although many power and performance models exist, they have a significant error margin while predicting the performance of memory or file-intensive tasks and HPC applications. Our investigations reveal that the prediction error is due in part to the fact that they do not take frequency AND CPU variations account, rather they just depend on the CPU by itself. In this paper, we empirically derive power and completion time models using linear regression with CPU utilization and operating frequency as parameters. We validate our power model on several Intel and AMD processors by predicting within 2-7 of measured power. We validate our completion time model using five kernels of NASA Parallel Benchmark suite and five CPU, memory and file-intensive benchmarks on four heterogeneous systems and predicting within 1-6 of observed performance. We then show how these models can be employed to realize as much as 15 savings in power while delivering 44 better performance for applications deployed in a virtualized environment.
@cite_14 addressed this concern when they proposed a power model that support multiple frequency steps. The power @math at any given utilization @math is given as @math @math @math
@cite_14
2121574851
This paper proposes and evaluates an approach for power and performance management in virtualized server clusters. The major goal of our approach is to reduce power consumption in the cluster while meeting performance requirements. The contributions of this paper are: (1) a simple but effective way of modeling power consumption and capacity of servers even under heterogeneous and changing workloads, and (2) an optimization strategy based on a mixed integer programming model for achieving improvements on power-efficiency while providing performance guarantees in the virtualized cluster. In the optimization model, we address application workload balancing and the often ignored switching costs due to frequent and undesirable turning servers on off and VM relocations. We show the effectiveness of the approach applied to a server cluster test bed. Our experiments show that our approach conserves about 50 of the energy required by a system designed for peak workload scenario, with little impact on the applications' performance goals. Also, by using prediction in our optimization strategy, further QoS improvement was achieved.