TOA: Optimizing VM Failure Rate and Energy Consumption in Task Scheduling Through Cloud-Fog Environment
K. Helini, Sankoju Bhavani, Kaushik Shivakumar, B. Gayathri, Spandana Mande, Santhosh Kumar Medishetti
Abstract
Task Scheduling (TS) in Cloud-Fog Computing (CFC) environments is crucial for optimizing resource utilization, reducing energy consumption, and minimizing Virtual Machine (VM) failures. Inefficient scheduling can lead to increased operational costs, excessive energy usage, and higher failure rates, ultimately affecting system reliability and service quality. This study proposes a Teamwork Optimization Algorithm (TOA) for efficient task scheduling, focusing on reducing VM failure rates and minimizing energy consumption. Inspired by cooperative team-based problem-solving strategies, TOA dynamically adjusts workload distribution, ensuring fault tolerance and optimal resource allocation. The proposed TOA approach incorporates adaptive learning mechanisms to predict potential VM failures and reallocate tasks efficiently. By leveraging cooperative decision-making, the algorithm enhances load balancing, reduces execution delays, and prevents resource overutilization. Additionally, TOA optimizes energy consumption by minimizing redundant computational operations and distributing tasks across energy-efficient nodes. Extensive simulations conducted in a cloud-fog computing environment demonstrate the effectiveness of TOA in improving system performance. Experimental results reveal that TOA reduces VM failure rates by 27.8% and lowers energy consumption by 22.5% compared to conventional algorithms such as PSO, ACO, and GA. Furthermore, TOA improves execution time, reduces makespan, and ensures seamless task completion in heterogeneous cloud-fog architectures. The proposed approach enhances the reliability and sustainability of cloud-fog systems by optimizing resource allocation and energy efficiency. This research contributes to developing intelligent scheduling techniques that mitigate VM failures and support energy-efficient cloud-fog computing operations, ensuring better performance and cost-effectiveness in real-world applications.