DocumentCode :
2670173
Title :
AppFlow: Autonomic Performance-Per-Watt Management of Large-Scale Data Centers
Author :
Khargharia, Bithika ; Luo, Haoting ; Al-Nashif, Youssif ; Hariri, Salim
Author_Institution :
Cisco Syst., Inc., San Jose, CA, USA
fYear :
2010
fDate :
18-20 Dec. 2010
Firstpage :
103
Lastpage :
111
Abstract :
The characteristic of dramatic fluctuation in the resource provisioning for real-time applications calls for an elastic delivery of computing services. Current data center deployment schemes, which feature a strong tie between servers and applications, are increasingly challenged to ensure power efficiency in terms of multiple peak loads provisioning, optimal average resources utilization, variable runtime workloads profiling, data center manageability and overhead control on the data center Total Cost of Ownership (TCO). Researchers have exploited paradigms such as virtualization and migration for large-scale computing systems, however, there is still a long way before we can optimally address the power-performance trade-off. This paper provides an autonomic power management scheme for the resource provisioning process for large-scale data centers while meeting the Service-Level Agreement (SLA) and power requirements. The system status is continuously monitored using a cross-layered hierarchy to optimally scale up and down the virtual machine resources such that power and performance can be optimized. We have applied our technique to autonomically manage high performance platforms with multi-core processors and multi rank memory subsystems. Our experimental results show around 56.25 percent platform energy savings for memory-intensive workload, 63.75 percent platform energy savings for processor-intensive workload and 47.5 percent platform energy savings for mixed workload while maintaining.
Keywords :
computer centres; energy conservation; microprocessor chips; multiprocessing systems; power aware computing; virtual machines; AppFlow; autonomic performance per watt management; autonomic power management scheme; large scale data centers; multicore processors; service level agreement; total cost of ownership; virtual machine resources; Delay; Mathematical model; Memory management; Monitoring; Optimization; Program processors; Autonomic Computing; Data Center; Power Management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom)
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-9779-9
Electronic_ISBN :
978-0-7695-4331-4
Type :
conf
DOI :
10.1109/GreenCom-CPSCom.2010.103
Filename :
5724818
Link To Document :
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