TL;DR
This work proposes a deep reinforcement learning-based task scheduling approach (DRLTS) for the fog-cloud environment, which outperforms heuristics, metaheuristics, and reinforcement learning-based scheduling techniques when it comes to response time, energy efficiency, deadline satisfaction, and performance overall.
The rapid growth of Internet of Things (IoT) applications has introduced significant challenges in efficient task scheduling within distributed cloud environments, particularly in meeting Quality of Service (QoS) requirements while minimizing operational costs and SLA violations. To address this issue, this paper proposes an AI-Based Hybrid Detective Behavior Optimization (DBA) technique integrated with fuzzy systems for intelligent task scheduling of IoT workloads. The proposed approach leverages the exploration–exploitation capabilities of DBA along with fuzzy logic-based decision-making to dynamically prioritize and allocate tasks under uncertain and heterogeneous cloud conditions. The model is evaluated using the DigitalOcean cloud workload in the WorkflowSim simulation environment and compared against traditional methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Deep Reinforcement Learning (DRL). Experimental results demonstrate that the proposed DBA-Fuzzy approach significantly outperforms baseline methods by reducing SLA violations by 24.6%, improving QoS by 21.3%, minimizing execution cost by 18.9%, and enhancing throughput by 26.7%. These improvements highlight the robustness and adaptability of the proposed model in handling dynamic IoT workloads. The findings suggest that integrating metaheuristic optimization with fuzzy reasoning provides an effective solution for multi-objective task scheduling, making it highly suitable for next-generation distributed cloud environments supporting large-scale IoT applications.
The increasing deployment of latency-sensitive Internet of Things (IoT) applications has intensified the need for intelligent task scheduling mechanisms in fog computing environments. Conventional scheduling approaches, including heuristic and centralized machine learning techniques, often fail to adapt to dynamic workload variations and mobility-induced network changes, resulting in increased task latency. This paper proposes a Predictive Latency-Aware Federated Deep Reinforcement Learning (PLA-FDRL) framework for adaptive task scheduling in IoT-enabled fog networks. The proposed framework integrates latency prediction, mobility-aware fog node selection, and federated deep reinforcement learning to proactively allocate tasks to optimal fog resources. Each fog node independently trains a Deep Q-Network (DQN) using local observations and periodically participates in federated aggregation without sharing raw data. A latency-aware reward function jointly minimizes transmission, queueing, processing, and migration delays. Experimental evaluation under dynamic IoT workloads demonstrates significant reductions in average task latency and response time compared with FCFS, Round Robin, centralized DQN, and conventional federated reinforcement learning schedulers. Results indicate that the proposed framework improves responsiveness and scalability while preserving data privacy.
An AI-enabled dynamic task scheduling framework based on Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) model to dynamically assign tasks to virtual machines and learn the best scheduling policies by continuously interacting with the cloud environment based on system parameters such as resource availability, task queue length, and virtual machine load is introduced.
Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Robust Reinforcement Learning (RL) based task scheduling approaches can address the inherent tradeoff between energy consumption and deadline violation in a Multi-access Edge Computing (MEC) based Internet of Things (IoT) network, while maintaining robustness against changes in the task arrival rate. However, tabular robust RL algorithms suffer from high computational and storage complexity, and therefore are not scalable to a system with a large number of IoT nodes. To this end, in this paper, we propose a robust deep RL based task scheduling algorithm to solve the underlying Robust-Return Constrained Markov Decision Process (R2CMDP) problem. The proposed algorithm introduces a tunable amount of robustness in the solution of the RL framework. Complexity analysis and ns-3 simulation results are presented to demonstrate the efficacy of our algorithm.
An adaptive cloud-edge scheduler using lightweight artificial intelligence models for real-time IoT stream placement that improves scheduling flexibility, transparency, and practical applicability in real-time IoT systems is proposed.
