Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dynamic Configuration of Resource-Aware Services
Proceedings of the 26th International Conference on Software Engineering
Middleware Services for P2P Computing in Wireless Grid Networks
IEEE Internet Computing
Adaptive Offloading for Pervasive Computing
IEEE Pervasive Computing
The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment
International Journal of High Performance Computing Applications
User-Centric Content Negotiation for Effective Adaptation Service in Mobile Computing
IEEE Transactions on Software Engineering
MobiPADS: A Reflective Middleware for Context-Aware Mobile Computing
IEEE Transactions on Software Engineering
Hi-index | 0.00 |
Inthis paper, a self-growing engine based adaptation system, which automatically decides the more efficiency plan about the assigning of jobs, in a mobile, grid computing environment, is proposed. Recently, research relating to grid computing has become an important issue, achieving certain goals by sharing the idle resources of computing devices, and overcoming various constraints of the mobile computing environment. In this domain, most existing research assigns work only by considering the status of resources. Hence the situation of assigning work to a peer having relatively low work efficiency, is possible. The proposed system considers various contexts and selects the most suitable peer. In addition, the system stores the history of the work result, and if the same request occurs in the future, a peer is selected by analyzing the history. In this paper, a prototype used to evaluate the proposed system is implemented, and the effectiveness of the system is confirmed through two experiments.