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A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain

A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain

Abhilasha Rangra, Vivek Kumar Sehgal, Shailendra Shukla
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 17
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522566625|DOI: 10.4018/IJISMD.2019070104
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MLA

Rangra, Abhilasha, et al. "A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain." IJISMD vol.10, no.3 2019: pp.59-75. http://doi.org/10.4018/IJISMD.2019070104

APA

Rangra, A., Sehgal, V. K., & Shukla, S. (2019). A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain. International Journal of Information System Modeling and Design (IJISMD), 10(3), 59-75. http://doi.org/10.4018/IJISMD.2019070104

Chicago

Rangra, Abhilasha, Vivek Kumar Sehgal, and Shailendra Shukla. "A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain," International Journal of Information System Modeling and Design (IJISMD) 10, no.3: 59-75. http://doi.org/10.4018/IJISMD.2019070104

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Abstract

Cloud computing represents a new era of using high quality and a lesser quantity of resources in a number of premises. In cloud computing, especially infrastructure base resources (IAAS), cost denotes an important factor from the service provider. So, cost reduction is the major challenge but at the same time, the cost reduction increases the time which affects the quality of the service provider. This challenge in depth is related to the balance between time and cost resulting in a complex decision-based problem. This analysis helps in motivating the use of learning approaches. In this article, the proposed multi-tasking convolution neural network (M-CNN) is used which provides learning of task-based deadline and cost. Further, provides a decision for the process of task scheduling. The experimental analysis uses two types of dataset. One is the tweets and the other is Genome workflow and the comparison of the method proposed has been done with the use of distinct approaches such as PSO and PSO-GA. Simulated results show significant improvement in the use of both the data sets.

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