Hybrid Fuzzy Archimedes‐based Light GBM‐XGBoost model for distributed task scheduling in mobile edge computing

Mobile edge computing (MEC) mainly offers strong computing capabilities and functions to finish the delay‐sensitive task in time with the help of 5G wireless networks. Task scheduling is a technique for managing the increasing number of mobile edge users, decreasing task execution time, and improving the system's load‐balancing capabilities. To achieve these goals, a distributed task scheduling system is developed in this research to satisfy multi‐objectives such as cost, total execution time, overhead, and energy consumption for large‐scale MEC tasks. First, a Hybrid Fuzzy Archimedes (HFA) algorithm is proposed to select the MEC node, which finishes the tasks with minimal cost and a higher security level. In the second step, the Hybrid LGBM and XGBoost architecture is formed to minimize the energy consumption and latency of each node for distributed task scheduling. The HFA algorithm modifies the search behavior of the Archimedes optimization algorithm using the fuzzy tendency factor and a normalized objective function. The HFA algorithm mainly selects the rule with an improved security value and lower cost for delay‐sensitive applications. The main aim of the hybrid LGBM‐XGBoost architecture is to minimize energy consumption and latency by taking the makespan and energy values. The efficiency of the proposed methodology is evaluated in terms of resource utilization, average completion time, completion rate, and Computation Workload Completion Rate. The proposed model offers a 20% improvement in average completion time and a 30% improvement in the energy consumption ratio. When 64 users are present in the system, the proposed model offers a CPU usage of 22% whereas MOCOSC, ADMM, and ANNIDS approaches offer CPU utilization of 62%, 78%, and 82%, respectively. To propose a novel fuzzy Archimedes optimization‐based Hybrid LGBM–XGBoost for distributed task scheduling thereby enhancing the computation efficiency of a MEC system. The HFA algorithm always selects a task with a low cost to lower the running time of each operation, and the solution with the lowest cost resembles a higher fitness. Different Quality of Service (QoS) variables including cost, security, and time overhead is also incorporated into the HFA algorithm. The LGBM and XGBoost approach has primarily been hybridized in order to take into account the high‐level non‐linear weight of the model. The Hybrid LGBM‐XGBoost architecture gives non‐linear weights to the low‐level predictors in the model t