Future Generation Computer Systems - Special issue on metacomputing
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Authentication algorithm based on grid environment
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
A Process Scheduling Analysis Model Based on Grid Environment
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
Certificate and authority application based on grid environment
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A process schedule analyzing model based on grid environment
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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In this paper, we proposed a dynamic supervising model based on grid environment. This model can utilize the grid resources, e.g., CPU, storages, etc., more flexible and optimal. There are three modules in this model, saying, connecting authentication module (CAM), trusting module (TM), and resource monitor module (RMM). CAM can authenticate the demander. TM can adjust trust degrees of the other collaborators by fuzzy inferences, and provide these trust degrees for RMM to schedule the works process. RMM can discover the resources, schedule and distribute works to others nodes. Once the demander passes the authentication, RMM will schedule and distribute works depending on the trust degrees from TM. RMM will dynamically monitor the resource variation of these collaborators. This model not only can make the collaboration more flexible and reliable, but also can optimize the grid resources.