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POPL '03 Proceedings of the 30th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proceedings of the 40th annual Design Automation Conference
A Grid Service Infrastructure for Mobile Devices
SKG '05 Proceedings of the First International Conference on Semantics, Knowledge and Grid
Extending Pervasive Devices with the Semantic Grid: A Service Infrastructure Approach
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Dynamic graph-based software fingerprinting
ACM Transactions on Programming Languages and Systems (TOPLAS)
Energy conservation by adaptive feature loading for mobile content-based image retrieval
Proceedings of the 13th international symposium on Low power electronics and design
Adaptive computation offloading for energy conservation on battery-powered systems
ICPADS '07 Proceedings of the 13th International Conference on Parallel and Distributed Systems - Volume 01
Establishing Trust for Computation Offloading
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
Adaptive service delivery for mobile users in ubiquitous computing environments
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Service discovery strategies in ubiquitous communication environments
IEEE Communications Magazine
Dynamic frequency scaling with buffer insertion for mixed workloads
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Tradeoff between energy savings and privacy protection in computation offloading
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
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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.