Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 4
Abstract
Workflow scheduling is a key problem to be solved in the cloud to increases the quality of services. Few research works have been designed for performing workflow scheduling using different techniques. But, scheduling performance of existing techniques was not effective when considering a larger number of user tasks. Besides, the makespan of workflow scheduling was higher. In order to solve such limitations, Gene Optimized Deep Neural Round Robin Scheduling (GODNRRS) Technique is proposed. The designed GODNRRS Technique contains three layers namely input, hidden and output layer to efficiently perform workflow scheduling in the cloud. The GODNRRS Technique initially gets the number of user tasks as input in the input layer and forwards it to the hidden layer. After taking input, GODNRRS Technique initializes gene population with the assist of virtual machines in Amazon cloud server at the first hidden layer. Next, GODNRRS Technique determines fitness function for each virtual machine using their energy, memory, CPU time, bandwidth capacity at the second hidden layer. Afterward, GODNRRS Technique defines a weight for each virtual machine at the third hidden layer depends on their fitness function estimation. Consequently, GODNRRS Technique distributes the user tasks to optimal virtual machines according to their weight value at the fourth hidden layer in a cyclic manner. At last, the output layer renders the scheduled tasks result. Thus, GODNRRS Technique handles workflows in the cloud with improved scheduling efficiency and lower energy and makespan. The GODNRRS Technique conduct the experimental evaluation using metrics such as scheduling efficiency, makespan, and energy consumption with respect to a different number of user tasks from LIGO , Montage and cybershake real-time applications. The experimental result show that the GODNRRS Technique is able to increases the efficiency and also reduces the makespan of workflows scheduling in the cloud as compared to state-of-the-art works.
Authors and Affiliations
Shanmugasundaram M, Kumar R, Kittur H M
Speech Recognition System based on Discrete Wave Atoms Transform Partial Noisy Environment
Automatic speech recognition is one of the most active research areas as it offers a dynamic platform for human-machine interaction. The robustness of speech recognition systems is often degraded in real time application...
An Efficient Rule based Decision Support System using Semantic Web Technology
The Semantic Web technology is an efficient mechanism to query or infer knowledge on a global scale using the internet by providing logical reasoning through rule based system. In this paper application of semantic web...
An Efficient Segmentation of Retinal Blood Vessels using Singular Value Decomposition and Morphological Operator
The extensive study on retinal fundus images has become an essential part in medical domain to detect pathologies including diabetic retinopathy, cataract, glaucoma, macular degeneration,etc.which are the major causes of...
A New Type Method for the Structured Variational Inequalities Problem
In this paper, we present an algorithm for solving the structured variational inequality problem, and prove the global convergence of the new method without carrying out any line search technique, and the global R-conver...
Automatic Fuzzy-based Hybrid Approach for Segmentation and Centerline Extraction of Main Coronary Arteries
Coronary arteries segmentation and centerlines extraction is an important step in Coronary Artery Disease diagnosis. The main purpose of the fully automated presented approaches is helping the clinical non-invasive diagn...