Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Queue - Virtualization
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
A power consumption analysis of decision support systems
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Efficient resource provisioning in compute clouds via VM multiplexing
Proceedings of the 7th international conference on Autonomic computing
Stochastic approximation control of power and tardiness in a three-tier web-hosting cluster
Proceedings of the 7th international conference on Autonomic computing
Energy aware consolidation for cloud computing
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers
IEEE Transactions on Services Computing
Workload management for power efficiency in virtualized data centers
Communications of the ACM
Reducing electricity cost through virtual machine placement in high performance computing clouds
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
A Power and Performance Management Framework for Virtualized Server Clusters
GREENCOM '11 Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications
Comparing VM-Placement Algorithms for On-Demand Clouds
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Towards energy-proportional computing for enterprise-class server workloads
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Experimental analysis of task-based energy consumption in cloud computing systems
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Dynamic Voltage and Frequency Scaling in Multimedia Servers
AINA '13 Proceedings of the 2013 IEEE 27th International Conference on Advanced Information Networking and Applications
Understanding Tradeoffs between Power Usage and Performance in a Virtualized Environment
CLOUD '13 Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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omputing servers generally have a narrow dynamic power range. For instance, even completely idle servers consume between 50% and 70% of their peak power. Since the usage rate of the server has the main influence on its power consumption, energy-efficiency is achieved whenever the utilization of the servers that are powered on reaches its peak. For this purpose, enterprises generally adopt the following technique: consolidate as many workloads as possible via virtualization in a minimum amount of servers (i.e. maximize utilization) and power down the ones that remain idle (i.e. reduce power consumption). However, such approach can severely impact servers' performance and reliability. In this paper, we propose a methodology to determine the ideal values for power consumption and utilization for a server without performance degradation. We accomplish this through a series of experiments using two typical types of workloads commonly found in enterprises: TPC-H and SPECpower ssj2008 benchmarks. We use the first to measure the amount of queries responded successfully per hour for different numbers of users (i.e. Throughput@Size) in the VM. Moreover, we use the latter to measure the power consumption and number of operations successfully handled by a VM at different target loads. We conducted experiments varying the utilization level and number of users for different VMs and the results show that it is possible to reach the maximum value of power consumption for a server, without experiencing performance degradations when running indi- vidual, or mixing workloads.