Advanced Grasshopper Optimization Scheduling Algorithm (AGOSA) for Multi-Cloud Environments

Abstract

The multi-cloud scheduling problem refers to the challenging task of efficiently allocating and managing resources across multiple cloud environments, involving the optimization of task scheduling, resource allocation to minimize makespan, reduce resource costs, and handle the complexity of multi-cloud environments while making optimal scheduling decisions. To overcome this issue. The paper proposes a novel scheduling algorithm, the Advanced Grasshopper Optimization Scheduling Algorithm (AGOSA), designed specifically for multi-cloud environments. AGOSA integrates a cloud model with a data center model, enabling the optimization of task scheduling and resource allocation across both cloud and data center resources. The algorithm leverages the principles of grasshopper optimization, adapting their behavior in searching to efficiently explore the solution space and identify the optimal scheduling strategy. Moreover, AGOSA addresses the challenges of multi-cloud scheduling, including minimizing makespan, reducing resource costs, and ensuring efficient resource utilization. The algorithm's adaptive nature allows it to dynamically adjust to changing workload demands and resource availability, ensuring optimal scheduling decisions. The proposed AGOSA method was evaluated through CloudSim 3.0 simulation, demonstrating its effectiveness in optimizing multi-cloud scheduling and comparing its performance with existing scheduling algorithms.