Jul 2026· International Conference Computing Methodologies and Communication· pp. 444-451· 0 citations· 20 references
Abstract
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 complexity of Internet of Things (IoT) environments requires adaptive mechanisms for efficient service allocation across distributed infrastructures. Existing approaches are often focused on specific optimization algorithms or isolated Quality of Service (QoS) parameters, lacking a unified framework for decision-making across IoT, Fog/Edge, and Cloud layers. This paper proposes a modular QoS-aware decision-making framework that integrates QoS profiles, key performance indicators (KPIs), utility functions, and multi-criteria decision-making mechanisms to support both static and dynamic service allocation. The framework considers service execution latency, energy consumption, network throughput, and network coverage as decision criteria and enables adaptive balancing of conflicting QoS requirements. Its applicability is demonstrated through a smart transportation use case involving multiple service allocation scenarios across CRU, Fog, and Cloud infrastructures. The results confirm that different allocation strategies exhibit distinct QoS trade-offs and demonstrate the suitability of the proposed framework for adaptive and resource-efficient service allocation in layered IoT architectures.
Alem Čolaković, Bakir Karahodža, Samir Causevic et al.· Journal of Information and O...· 0 citations
Experimental results demonstrate that the proposed method achieves superior efficiency, resource utilization, and scalability, making it a promising approach for optimizing task scheduling in dynamic cloud computing environments.
Ratnakumari Neerukonda, B. Hariharan· Discover Computing· 0 citations
The rapid advancement of technology like Internet of Things (IoT) and Cloud computing (CC)based heterogeneous environment required dynamic resource management system. Thecomplexity of IoT-Cloud is increasing due to abundance of dynamic data dissemination thatcreate poor performance like high energy consumption (EC), workload imbalancing, pooradaptability, and fail to handle SLA violations. The two primary contributions of the proposedwork are the Optimized Priority-Aware Hierarchical Multi-Agent Deep Q Network (OPHMDQN)and the Adaptive Multi-Objective Dung Beetle Optimization Algorithm (ADBOA). Themulti-agent strategies increase the scalability and adaptability of resource managementthrough local and global hierarchies. The approach integrates information-based decisionmakingand priority-aware allocation while accounting for SLA requirements, systemconstraints, and job complexity to optimise resource generation, utilisation, allocation, andtask scheduling. In comparison to existing optimization and Reinforcement Learning (RL)techniques, experimental results show that the proposed OPHM-DQN-ADBOA frameworkconsistently reduces EC (up to 30 % lower), execution delay, and SLA violations whileimproving resource utilisation and LB. The ADBOA enhances the proposed model throughoptimal multi-objective training, reducing EC, SLA violation, and cost while improvingresource utilization and scheduling efficiency. As a results, the model achieves high scalabilityand adaptability in heterogeneous IoT-Cloud resource management.
A. Ali, A. R. Mohamed Shanavas· THE SCIENTIFIC TEMPER· 0 citations
Maintaining a stable Quality of Service (QoS) in oneM2M deployments is challenging because edge-to-cloud traffic in IoT systems is bursty and resource demand changes rapidly. We propose a fuzzy-logic QoS controller, integrated into a MAPE-K autonomic loop, that adaptively decides the share of traffic offloaded from the local oneM2M platform to the cloud as a function of CPU usage, Round-Trip Time (RTT), and incoming traffic rate. The controller uses a 27-rule Mamdani inference engine, formally defined trapezoidal membership functions, and centroid defuzzification, and is integrated with the open-source Mobius platform. Compared with an unmanaged baseline under peak load, our approach reduces operating cost by 43.5%, RTT by 55.9%, and increases the request success rate by 19.4%, while keeping CPU and RAM usage in the 40–50% range. A qualitative comparison with static-threshold and recent fuzzy/learning-based offloading methods, together with a discussion of scalability to hundreds of edge nodes, positions the controller as a practical and cost-effective option for oneM2M-compliant IoT platforms.
A. Zyane, Jamal Et-Tousy· International Conference on...· 0 citations
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.
Pallavi Mettupalli Venkata, Thatikonda Supraja, Priyanka Chawla et al.· Journal of Supercomputing· 0 citations
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.
Karnam Sreenu, G. Prasadu, K. Premnadh et al.· VFAST Transactions on Softwa...· 0 citations
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