Comparing algorithm for dynamic speed-setting of a low-power CPU
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
Energy-efficient policies for embedded clusters
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Algorithmic problems in power management
ACM SIGACT News
Energy-Efficient Real-Time Heterogeneous Server Clusters
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Dynamic Voltage Scaling in Multitier Web Servers with End-to-End Delay Control
IEEE Transactions on Computers
Scheduling for reduced CPU energy
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
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Power consumption of HPC cluster is increasingly concerned by HPC designers and users. This paper proposes a multi-tier cluster energy management for reducing energy consumption of the cluster system with minimal effect on performance. The proposed management combines cluster-level and node-level strategies. Cluster-level strategy uses a self-learning load estimation algorithm to predict new-coming task's load and presents a novel PI control theory based node allocation mechanism to decide how many nodes should be selected to execute parallel tasks. The cluster-level strategy also uses an on-demand on/off strategy to decide how the node scales its CPU frequency and whether to be turned off. Node-level strategy uses an enhanced-conservative governor algorithm to improve the sensitivity of the frequency adjustment when load drops. Experiments show that the proposed multi-tier power management is more efficient than other traditional strategies in reducing overall system power consumption.