Improved multi-strategy secretary bird optimization for efficient IoT task scheduling in fog cloud computing

Applications designed for real-time IoT operations improve cloud-based service utilization due to their rapid scalability. Though cloud computing appears to be more effective for data processing and storage in a range of IoT applications, its real-time scalability presents issues in fulfilling the demands of network bandwidth and latency-sensitive applications. In this context, fog computing is shown to be a complementary paradigm to cloud computing, providing extra benefits and capabilities aimed at extending cloud services to end users and edge devices. Due to the restricted capabilities of fog nodes, only lightweight activities can be conducted locally, while jobs requiring more processing time are handled in the cloud. As a result, an Improved Multi-Strategy Enhanced Secretary Bird Optimization Algorithm using Reinforcement Learning (IMSESBOA + RL) for IoT Task Scheduling (TS) mechanism is presented to reduce data processing time and enhance Quality of Service (QoS) in fog-cloud computing. This IMSESBOA + RL approach is designed as an efficient scheduling model that investigates and processes various scalable quantities of tasks while minimizing latency and energy costs. It used a multi-objective methodology based on Secretary Bird Optimization Algorithm's (SBOA) balanced exploration and exploitation capabilities, which has multi-strategy benefits in terms of maximizing resource consumption rate and shortening makespan. It further uses RL for dynamically adapting to the new workloads by excelling in learning optimal strategies using the interaction of trial and error with the environment. The simulation findings of the IMSESBOA + RL approach verified that it reduced makespan by 19.42% and execution time by 18.32% compared to the baseline approaches with various jobs originating from IoT applications.