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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using cloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Efficient VM Placement Policy for Data Centre in cloud Environment 3rd

Efficient VM Placement Policy for Data Centre in Cloud Envir...

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3rd International Conference on Soft computing: Theories and Applications, SoCTA 2018

作者： Dubey, Kalka Nasr, Aida A. Sharma, S.C. El-Bahnasawy, Nirmeen Attiya, Gamal El-Sayed, Ayman Cloud Computing and Wireless sensor Lab Indian Institute of Technology Roorkee RoorkeeUttarakhand247664 India Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9789811507502

cloud computing now becomes the accomplished infrastructure model for delivering the cloud service to its user over the Internet. Virtualization is one of the key concepts, which makes the dynamic sharing of physical resources of cloud more effective. In virtualization, virtual machine is placed over the physical machine in data centre and VM placement is one of the challenging issues in cloud computing, so cloud service provider needs good VM placement policy to increase the utilization of physical resources as well as reduce the load-balancing problem. In this paper, we proposed a new VM placement policy called Efficient of service Best Fit Decreasing (EBFD). From the simulation experiments result, we see that the proposed policy performs much better performance compared with first-fit and best-fit policy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： cloud computing

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