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