Informatics Publishing Limited

S. Sabeetha Saraswathi ¹, S. Mohana ², N. Vijayaraj ³, S. Imavathy ⁴, S. Mahalakshmi ⁴

Affiliations
1 Department of Computer Science and Engineering, Shri Angalamman College of Engineering & Technology, Siruganoor, Trichy, Tamilnadu, IN
2 Department of Computer Science and Engineering, M.I.E.T. College of Engineering, Trichy, Tamilnadu, IN
3 Department of Information Technology, Shri Angalamman college of Engineering & Technology, Siruganoor, Trichy, Tamilnadu, IN
4 Department of IT, E.G.S. Pillay Engineering College, Nagapattinam, IN

Abstract

The core functions of grid computing are Resource allocation and job scheduling. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. The system architecture for grid resource monitoring and prediction has been design. The key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models are discussed. Evaluations are performed on a prototype system. The experimental results indicate that the efficiency and accuracy of the system meet the demand of online system for grid resource monitoring and prediction.

Keywords

Grid Resource, Monitoring and Prediction, Neural Network, Support Vector Machine, Genetic Algorithm, Particle Swarm Optimization.

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S. Imavathy ¹, S. Mahalakshmi ¹, R. Uma Maheswari ², T. Vaishnavi ², P. Vennila ¹

Affiliations
1 Department of Information Technology, E.G.S. Pillay Engineering College, Nagai, IN
2 Department of Information Technology, E.G. Pillay Engineering College, Nagai, IN

Abstract

In a Computer Network, the peer-to-peer (P2P) paradigm has become very popular for loading and distributing information in separate manner that is from one single system to another system. At first, the P2P systems are very useful in sharing the one dimensional information. But nowadays, the need for P2P applications with multidimensional data has risen, inspiring research on P2P systems that can handle such type of data. Now the point to focus is on structured P2P systems that share spatial information (partial) and also we have to found the area and directionality of the multidimensional information. We present SPATIALP2P, which is a totally and inherently distributed P2P network and also a totally separate indexing and searching framework that is suitable for spatial data. SPATIALP2P supports P2P applications in which spatial information of various sizes can be dynamically edited, updated, and peers can leave or join. SPATIALP2P efficiently handles changes in the spatial information and in the network structure caused by joining or leaving peers without the need of load balancing or restructuring. Finally, contrary to existing DHT-based approaches, SPATIALP2P captures the locality and directionality of the 2D space.

Keywords

Peer-To-Peer, P2P Applications, Index Structure.

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