Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634 - 15 August 2024

Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >

J. Noorul Ameen^1,*, S. Jabeen Begum²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1281-1294, 2022, DOI:10.32604/iasc.2022.025137 - 03 May 2022

Abstract In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed More >