Enhanced Task Scheduling and Resource Allocation in Edge-Cloud Continuum Using Modified Flower Pollination Algorithm

The edge-cloud continuum integrates edge computing and cloud computing to optimize resource use and improve performance by bringing computation closer to data sources while leveraging vast cloud resources. Efficient resource allocation within this continuum is crucial for optimizing performance and minimizing delays. This paper proposes a novel approach to resource allocation using a modified Flower Pollination Algorithm (FPA). By incorporating dynamic switch probability, inertia weight for Levy flight balancing, and enhancing the initial solution using a chaotic map, the modified FPA enhances exploration and exploitation capabilities. Additionally, a network model is introduced to monitor network performance metrics, and a task dependency model ensures tasks are executed based on their dependencies. Experimental results demonstrate that the proposed approach significantly reduces delay and improves resource utilization compared to existing methods. This work provides a comprehensive framework for addressing the challenges.