Enabling autonomic power-aware management of instrumented data centers

Authors:
Nanyan Jiang; Manish Parashar
Affiliations:
Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway NJ 08855, USA;Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway NJ 08855, USA
Venue:
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Year:
2009

Citing 0
Cited 5

On-line sensing for healthier FPGA systems

Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
Low-cost sensing with ring oscillator arrays for healthier reconfigurable systems

ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Optimal Server Allocation and Frequency Modulation on Multi-Core Based Server Clusters

International Journal of Green Computing
Review of performance metrics for green data centers: a taxonomy study

The Journal of Supercomputing
Thermal camera networks for large datacenters using real-time thermal monitoring mechanism

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sensor networks support flexible, non-intrusive and fine-grained data collection and processing and can enable online monitoring of data center operating conditions as well as autonomic data center management. This paper describes the architecture and implementation of an autonomic power aware data center management framework, which is based on the integration of embedded sensors with computational models and workload schedulers to improve data center performance in terms of energy consumption and throughput. Specifically, workload schedulers use online information about data center operating conditions obtained from the sensors to generate appropriate management policies. Furthermore, local processing within the sensor network is used to enable timely responses to changes in operating conditions and determine job migration strategies. Experimental results demonstrate that the framework achieves near optimal management, and in-network analysis enables timely response while reducing overheads.