Abstract
Abstract Edge-cloud computing infrastructures are becoming a mandate in almost all the possible application areas due to exponential rise in the computational demands over the network. Therefore, adaptive load balancing and workload scheduling is critical in edge-cloud computing for maintaining Quality of Service (QoS) and system agility. To acquire an intelligent task scheduling and load distribution across heterogeneous computing environments, a novel AI-enabled predictive approach is presented in the proposed study for dynamic scheduling framework that integrates a Jacobi Identity-Based Deep Neural Network (JI-DNN) with a Multi-Criteria Grey Wolf Optimizer (MC-GWO). The proposed framework utilizes Jacobi identity properties for enhancing the network’s learning efficiency, stability, and convergence. The JI-DNN is employed for predicting task-specific execution times and heterogeneous resource requirements during run time task allocation in the system and the MC-GWO algorithm adapts the hunting behaviour of grey wolves for optimizing the scheduling decisions based on variant metrics like latency, energy usage, load balance, and overall outcome. The integration of predictive intelligence and multi-objective optimization helps the framework to adapt dynamically to real-time workload variations. Simulation result findings proves the proposed system’s effectiveness, highlighting the crucial improvements in reducing makespan, enhancing energy efficiency, and optimizing resource utilization as compared to the traditional static and single-criteria scheduling strategies. Clinical trial The study is not a clinical trial thus any registration details were not applicable.