Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment
Journal Title: International Journal of Intelligent Engineering and Systems - Year 2017, Vol 10, Issue 1
Abstract
Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.
Authors and Affiliations
Gobalakrishnan Natesan
Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment
Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud com...
Reliable and Efficient Distribution of Multicast Session Key for Deduplicated Data in Cloud Computing
Data deduplication is one of the fascinating features of any cloud computing storage service which is generally realized as Cross User Data Deduplication (CUDD). Although it provides optimization which is challenging to...
Base Station Positioning in Wireless Sensor Network to aid Cluster Head Selection Process
In this paper, we propose an (SAPSO) Self-Adaptive Particle Swarm Optimization algorithm to solve the base station positioning problem. This algorithm is used to minimize the distance between the base station and cluster...
Optimal Decision Tree Based Unsupervised Learning Method for Data Clustering
Clustering is an investigative data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. Our investigation using a pattern based clustering on nume...
Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data
Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of da...