OICC Press Majlesi Journal of Electrical Engineering 2345-3796 17 3 2024 02 03 An Improved Dingo Optimization for Resource Aware Scheduling in Cloud Fog Computing Environment 10.30486/mjee.2023.1989335.1165 EN Santhosh Kumar Medishetti VIT-AP University, Amaravathi, A.P., India, 522 237. Ganesh Reddy Karri AP, India Journal Article 2024 02 03 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.