Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Convex Optimization
Energy conservation in heterogeneous server clusters
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Boosting Data Center Performance Through Non-Uniform Power Allocation
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
A performance-conserving approach for reducing peak power consumption in server systems
Proceedings of the 19th annual international conference on Supercomputing
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Optimal power allocation in server farms
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
On the Interplay of Parallelization, Program Performance, and Energy Consumption
IEEE Transactions on Parallel and Distributed Systems
Energy-efficient server clusters
PACS'02 Proceedings of the 2nd international conference on Power-aware computer systems
Fundamentals of Queueing Theory
Fundamentals of Queueing Theory
Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions
IEEE Transactions on Parallel and Distributed Systems
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Data centers generally consume an enormous amount of energy, which not only increases the running cost but also simultaneously enhances their greenhouse gas emissions. Given the rising costs of power, many companies are looking for the solutions of best usage of the available power. However, most of the previous works only address this problem in the homogeneous environments. Considering the increasing popularity of heterogeneous data centers, this paper investigates how to distribute limited power among multiple heterogeneous servers in a data center so as to maximize performance. Specifically, we optimize the power allocation in two case: single-class service case and multiple-class service case. In each case, we develop an algorithm to find the optimal solution and demonstrate numerical data of the analytical method respectively. The simulation results show that our proposed approach is efficient and accurate for the performance optimization problem at the data center level.