Some computer science issues in ubiquitous computing
Communications of the ACM - Special issue on computer augmented environments: back to the real world
The remote processing framework for portable computer power saving
Proceedings of the 1999 ACM symposium on Applied computing
Computation offloading to save energy on handheld devices: a partition scheme
CASES '01 Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for embedded systems
Power conservation strategy for mobile computers using load sharing
ACM SIGMOBILE Mobile Computing and Communications Review
Task Allocation for Distributed Multimedia Processing on Wirelessly Networked Handheld Devices
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
Managing battery lifetime with energy-aware adaptation
ACM Transactions on Computer Systems (TOCS)
Managing battery lifetime with energy-aware adaptation
ACM Transactions on Computer Systems (TOCS)
Studying Energy Trade Offs in Offloading Computation/Compilation in Java-Enabled Mobile Devices
IEEE Transactions on Parallel and Distributed Systems
IEEE Pervasive Computing
A generic software partitioning algorithm for pervasive computing
WASA'06 Proceedings of the First international conference on Wireless Algorithms, Systems, and Applications
Efficient power profiling for battery-driven embedded system design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Energy is a vital resource in pervasive computing. Remote execution, a static approach to energy saving of mobile devices, is not applicable to the constantly varying environment in pervasive computing. This paper presents a dynamic software configuration approach to minimizing energy consumption by moving or/and replicating the appropriate components of an application among the machines. After analyzing three types of energy costs of the distributed applications, we set up a math optimization model of energy consumption. Based on the graph theory, the optimization problem of energy cost can be transformed into the Min-cut problem of a cost graph. Then, we propose two novel optimal software allocation algorithms for saving power. The first makes use of component migration to reasonably allocate the components among the machines at runtime, and the second is to replicate some components among machines to further save more energy than component migration. The simulations reveal that the two proposed algorithms can effectively save energy of mobile devices, and obtain better performance than the previous approaches in most of cases.