Data Access Prediction and Optimization in Data Grid Using SVM and AHL Classifications

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In recent years, scientific applications have the need to effectively handle huge volume of data. The processing and handling of such large quantity of data requires large scale computing infrastructure such as grid structures. The main objective of the proposed system is to improve the performance of the grid system by predicting the behavior of the application and its future events. Time Series classification technique models the behavior of the application based on three properties namely Stochasticity, Linearity and Stationarity. The major drawback of time series classification is that it is a complex process and not suitable for long-term forecasting. In order to overcome the above mentioned difficulties, in this work, two techniques, namely, Support Vector Machines (SVM) and Adaptive Hypergraph Learning (AHL) are used for predicting the behavior of user applications. SVM is a supervised learning algorithm which is used in many domains such as classification and regression analysis. AHL is a graph-based learning algorithm which uses K-Nearest Neighbors algorithm (KNN) to construct hypergraph. Confusion matrix is constructed to determine the accuracy of the prediction using the proposed algorithms based on SVM and AHL. The jobs are executed in the grid simulator, OptorSim, with the predicted events. Experimental results show that the proposed data prediction schemes using SVM and AHL decrease the application execution time by more than 50%.

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Classification; Time Series; Support Vector Machine; Adaptive Hypergraph Learning; Adaptive Sliding Window; Prediction

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