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http://dx.doi.org/10.3837/tiis.2020.09.005

Deep Learning Based Security Model for Cloud based Task Scheduling  

Devi, Karuppiah (Department of CSE, SRM Valliammai Engineering College)
Paulraj, D. (Department of CSE, RMD Engineering College)
Muthusenthil, Balasubramanian (Department of CSE, SRM Valliammai Engineering College)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3663-3679 More about this Journal
Abstract
Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.
Keywords
Cloud Computing; Distributed computing; Task scheduling; deep learning; makespan;
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