Static and dynamic processor scheduling disciplines in heterogeneous parallel architectures
Journal of Parallel and Distributed Computing
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Performance Prediction Technology for Agent-Based Resource Management in Grid Environments
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Time Series Prediction using Adaptive Association Rules
DFMA '05 Proceedings of the First International Conference on Distributed Frameworks for Multimedia Applications
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
An adaptive scheme for distributed dynamic security assessment of large scale power systems
WSEAS Transactions on Circuits and Systems
A grid computing based approach for the power system dynamic security assessment
Computers and Electrical Engineering
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
The deployment and evaluation of a bioinformatics grid platform - The HUST_Bio_Grid
Computers and Electrical Engineering
Towards enabling Cyberinfrastructure as a Service in Clouds
Computers and Electrical Engineering
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In order to manage the grid resources more effectively and provide a more suitable job scheduling strategy, the prediction information is needed for applications in the Grid computing system, such as the high performance computing and sharing computational resources, etc. In this paper, we propose a prediction system that can predict most information in the grid environment. Whether the repetitive time series pattern of the information exists or not, the proposed system can provide prediction results. We label the environment information in the grid and use the periodicity detector to detect the iterative patterns. The detected patterns can be used to predict several future values. Before the repetitive patterns have been found, a simple scheme that does not require a lot of resource has been used to generate prediction values. A prototype of this model is developed and tested with several test cases. The experimental results by the simulation show that our prediction system is able to capture different kinds of time series patterns and provide accurate prediction for the grid environment.