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Ranking servers based on energy savings for computation offloading
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International Symposium on Low Power Electronics and Design archive
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design table of contents
San Fancisco, CA, USA
SESSION: Energy-aware client-server computing table of contents
Pages: 267-272  
Year of Publication: 2009
ISBN:978-1-60558-684-7
Authors
Karthik Kumar  Purdue University, West Lafayette, IN, USA
Yamini Nimmagadda  Purdue University, West Lafayette, IN, USA
Yung-Hsiang Lu  Purdue University, West Lafayette, IN, USA
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Offloading may save energy for battery-powered devices by migrating computation to grid-powered servers. Offloading can be provided as a service and the servers charge the devices' users based on the consumed resources. In this paper, we propose a scheme to rank the servers based on the amounts of energy savings. The ranking depends on two factors: (1) the energy saved due to offloading and (2) the energy consumed while waiting for the results. We instrument the offloaded programs to estimate the amounts of computation performed by the servers, and use this information to determine the amounts of saved energy. When the servers perform the offloaded computation, the battery-powered devices wait for the results and consume energy. The ratio of the two factors determines the rank of a server. If a server performs more computation within a shorter duration, the server is ranked higher. We implement our method on an HP iPAQ and demonstrate that our method can effectively rank servers based on energy savings.


REFERENCES

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1
ME Anagnostou, A. Juhola, and ED Sykas. Context Aware services as a step to pervasive computing. In Lobster Workshop on Location based Services, pages 4--5, 2002.
 
2
S. Sivavakeesar, O.F. Gonzalez, and G. Pavlou. Service Discovery Strategies in Ubiquitous Communication Environments. IEEE Communications Magazine, 44(9):106--113, 2006.
 
3
Shumao Ou, Kun Yang, and Liang Hu. Cross: A combined routing and surrogate selection algorithm for pervasive service offloading in mobile ad hoc environments. In IEEE GLOBECOM, pages 720--725, November 2007.
 
4
 
5
 
6
H. Zeng, C.S. Ellis, A.R. Lebeck, and A. Vahdat. Currentcy: Unifying policies for resource management. In USENIX Annual Technical Conf, 2003.
 
7
H. Sonobe, S. Takagi, and F. Yoshimoto. Mobile computing system for fish image retrieval. In International Workshop on Advanced Image Technology, pages 33--37, 2004.
 
8
M. Noda, H. Sonobe, S. Takagi, and F. Yoshimoto. Cosmos: convenient image retrieval system of flowers for mobile computing situations. In IASTED, pages 25--30, 2002.
 
9
J.M. Hammersley and D.C. Handscomb. Monte Carlo Methods. Methuen Young Books, 1964.
 
10
Mark Kritzman, Simon Myrgren, and Sebastien Page. Portfolio Rebalancing: A Test of the Markowitz-Van Dijk Heuristic. SSRN eLibrary, 2007.
 
11
Y. Zhang, S. Zhang, and H. Tong. Adaptive Service Delivery for Mobile Users in Ubiquitous Computing Environments. Lecture Notes in Computer Science, 4159:209, 2006.
 
12
R. Wolski, S. Gurun, C. Krintz, and D. Nurmi. Using bandwidth data to make computation offloading decisions. In International Symposium on Parallel and Distributed Processing, pages 1--8, April 2008.
 
13
14
15
16
 
17
 
18
R. Chandramouli and N. Memon. Analysis of LSB based image steganography techniques. In ICIP, volume 3, pages 1019--1022, 2001.
 
19
Yung-Hsiang Lu, Luca Benini, and Giovanni De Micheli. Dynamic Frequency Scaling with Buffer Insertion for Mixed Workloads. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pages 1284--1305, 2002.
 
20
Yu-Ju Hong, Karthik Kumar, and Yung-Hsiang Lu. Energy Conservation by Adaptive Feature Loading for Mobile Content-based Image Retrieval. In ISCAS, 2009.
21

Collaborative Colleagues:
Karthik Kumar: colleagues
Yamini Nimmagadda: colleagues
Yung-Hsiang Lu: colleagues