TY - EJOUR
AU - Medishetti, Santhosh Kumar
AU - Karri, Ganesh Reddy
PY - 2024
DA - February
TI - An Improved Dingo Optimization for Resource Aware Scheduling in Cloud Fog Computing Environment
T2 - Majlesi Journal of Electrical Engineering
VL - 17
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/an-improved-dingo-optimization-for-resource-aware-scheduling-in-cloud-fog-computing-environment/
DO - 10.30486/mjee.2023.1989335.1165
N2 - Task scheduling in Cloud-Fog computing environments is a critical aspect of optimizing resource allocation and enhancing performance. This study presents an improved version of the Dingo Optimization Algorithm (IDOA) specifically designed for task scheduling in Cloud-Fog computing. The enhanced IDOA incorporates novel modifications to address the limitations of the original algorithm and improve the efficiency and effectiveness of task allocation. The algorithm incorporates modifications to the fitness evaluation function, a dynamic update mechanism, and a neighborhood search technique to enhance task allocation efficiency. Extensive simulations and comparisons with existing algorithms are conducted to evaluate the performance of the IDOA. The results demonstrate its superiority in terms of task makespan time, VM failure rate, and degree of imbalance. Overall, the improved Dingo Optimization Algorithm offers a promising solution for efficient task scheduling in Cloud-Fog computing environments. The algorithm effectively balances exploration and exploitation, facilitating efficient task scheduling in Cloud-Fog computing environments and optimizing cloud-based applications and services.
IS - 3
PB - OICC Press
KW - Dingo Optimization Algorithm., Task scheduling, Cloud-Fog computing, makespan, degree of imbalance 
EN -