FGO: Cost and Energy-Aware Scheduling in Cloud Environment Using Meta-Heuristic Algorithm
Santhosh Kumar Medishetti, Rameshwaraiah Kurupati, Jaideep Amrabad, Sai Teja Guguloth, Bandari Bhanu Prasad
Abstract
Efficient task scheduling in cloud computing is essential to ensure optimal resource utilization, reduced operational costs, and enhanced performance. This research introduces a novel metaheuristic-based approach employing the Fungal Growth Optimizer (FGO) algorithm for task scheduling in cloud environments. Inspired by the adaptive and exploratory behavior of fungal networks, FGO dynamically allocates tasks to virtual machines by optimizing key performance parameters. The proposed approach is evaluated using the CEA-Curie workload, a realistic benchmark that simulates complex cloud tasks. Simulation experiments are conducted in the CloudSim environment to assess the efficacy of the FGO algorithm. Results demonstrate that the proposed method significantly outperforms traditional scheduling techniques by achieving a 19.6% reduction in energy consumption, a 21.8% decrease in total cost, and an 18.2% improvement in throughput. These outcomes highlight the potential of FGO as a powerful and sustainable solution for workload scheduling in cloud infrastructures, making it highly suitable for large-scale, energy-aware, and cost-effective computing systems.