Paper Info
Title
Ann Based Execution Time Prediction Model And Assessment Of Input Parameters Through Ism
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.
Year
DOI
Venue
2020
10.34028/iajit/17/5/1
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
Keywords
DocType
Volume
Cloud computing, neural network, Prediction model, Resource selection
Journal
17
Issue
ISSN
Citations 
5
1683-3198
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Anju Shukla100.34
Shishir Kumar27817.06
Harikesh Singh321.06