ANN Based Execution Time Prediction Model and Assessment of Input Parameters through ISM

ANN Based Execution Time Prediction Model and

Assessment of Input Parameters through ISM

Anju Shukla, Shishir Kumar, and Harikesh Singh

Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, India

Abstract: Cloud computing is on-demand network access model which provides dynamic resource provisioning, selection and scheduling. The performance of these techniques extensively depends on the prediction of various factors e.g., task execution time, resource trust value etc., As the accuracy of prediction model absolutely depends on the input data that are fed into the network, Selection of suitable inputs also plays vital role in predicting the appropriate value. Based on predicted value, Scheduler can choose the suitable resource and perform scheduling for efficient resource utilization and reduced makespan estimates. However, precise prediction of execution time is difficult in cloud environment due to heterogeneous nature of resources and varying input data. As each task has different characteristic and execution criteria, the environment must be intelligent enough to select the suitable resource. To solve these issues, an Artificial Neural Network (ANN) based prediction model is proposed to predict the execution time of tasks. First, input parameters are identified and selected through Interpretive Structural Modeling (ISM) approach. Second, a prediction model is proposed for predicting the task execution time for varying number of inputs. Third, the proposed model is validated and provides 21.72% reduction in mean relative error compared to other state-of-the-art methods.

Keywords: Cloud computing, neural network, Prediction model, Resource selection.

Received September 20, 2018; accepted January 28, 2020

https://doi.org/10.34028/iajit/17/5/1

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