Paper Info
Title
TERN: A Self-Adjusting Thermal Model for Dynamic Resource Provisioning in Data Centers
Abstract
Dynamic resource provisioning becomes a practical approach to achieving high thermal and energy efficiency, improving scalability, and optimizing reliability for e-commercial applications running in modern data centers. In this paper, we propose a self-adjusting model called TERN to predict thermal behaviors of hardware resources for client sessions. Our TERN contains two major components: (1) a resource utilization model being responsible for estimating hardware usage based on the number of running client transactions, and (2) a thermal model that discovers correlation between resource utilization and their temperatures. TERN is conducive to predicting thermal trends of diverse workload conditions with a changing transaction mix. We apply the TPC-W benchmark to characterize the resource demands of each type of transactions. Then, we conduct a thermal profiling study of the benchmark with various transaction mixes. TERN judiciously adjusts the models to maintain prediction accuracy for dynamically changing request patterns. Experimental results show that TERN provides a simple yet powerful solution for resource provisioning in thermal-aware data centers where exist rapidly changing workload conditions.
Year
DOI
Venue
2015
10.1109/HPCC-CSS-ICESS.2015.183
2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems
Keywords
Field
DocType
Thermal Model,Dynamic Resource Provisioning,Data Center,E-commercial
Data modeling,Workload,Computer science,Server,Provisioning,Real-time computing,Tern,Database transaction,Data center,Distributed computing,Scalability
Conference
ISSN
Citations 
PageRank 
2576-3504
0
0.34
References 
Authors
20
6
Name
Order
Citations
PageRank
Yuanqi Chen163.92
mohammed i alghamdi25710.85
Xiao Qin31836125.69
Jifu Zhang49519.42
MingHua Jiang51310.96
Meikang Qiu63722246.98