Internet of Things-Centric Optimized Service Provisioning in Multi-Cloud Environment

A Multi‐Layer Adaptive Resource Allocation Approach to enhance Resource Utilization for SDN‐Based Edge Computing

Today, with the rapid growth of the Internet of Things (IoT), the volume of data generated and processed has increased significantly, and there is an urgent need to handle tasks quickly and efficiently in real‐time applications. Existing cloud‐based models are not capable of meeting these demands due to their deployment model that adds latency. The separation of the control and data planes has the potential to provide a solution to Software‐Defined Networking (SDN) that offers centralised control, programmability, and dynamic network management. However, efficient scheduling of activities and workloads among the different edge servers is one of the imperative issues since an inefficient allocation of the resources may lead to server congestion, high latency rates, and resource wastages. To overcome this, the article presents the Average‐Based Load Balancing and Resource Allocation Mechanism (ALBRAM), which identifies suitable nodes and dynamically allocates resources to maintain the load balance among all the servers. It recommends a three‐layer SDN‐based edge computing architecture providing a bridge between the IoT devices, middle nodes, and the edge servers, considering both the communication and computation time. The strategy uses a least‐load server selection mechanism to provide fairness, optimal resource utilisation, and balanced workloads. The results of the evaluation indicate that ALBRAM decreases the Makespan, total completion time and latency and increases resource utilization, and the efficiency of load‐balancing in comparison to existing methods.

Task Scheduling in the Fog to Cloud Continuum for IoT Services: A Taxonomy and Structured Synthesis of Distributed Resource Management

The paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems, and identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization.

Topology-Aware GNN-DQN for Optimal Task Offloading in Edge–Fog–Cloud Systems

The Internet of Things (IoT) has grown rapidly in recent years, enabling the interconnection of a large number of heterogeneous and distributed devices. This number is expected to exceed 70 billion according to Statista. With this massive scale, fulfilling complex IoT applications that require combinations of multiple objects remains a real challenge. Moreover, several Quality of Service (QoS) requirements must be satisfied, making the problem of selecting appropriate IoT services NP-hard. In such environments, task offloading is a key mechanism to efficiently distribute computational workloads across edge, fog, and cloud resources. However, selecting the optimal offloading decision remains a difficult NP-hard problem due to system heterogeneity and conflicting objectives. In this paper, we propose a GNN-DQN-based approach for task offloading in edge–fog–cloud environments. Unlike prior GNN-DQN approaches limited to single- or dual-tier architectures, our framework explicitly models heterogeneous node types and inter-tier communication links, enabling more balanced and scalable resource allocation. Experimental results show that GNN-DQN achieves a mean latency of 2.64 s, representing improvements of 70.2% over Random, 7.8% over DQN-only, and 3.5% over Greedy. A GNN-A2C baseline is also included to broaden the comparison with a modern DRL method. Despite sharing the same GNN encoder, it underperforms GNN-DQN across all metrics, confirming the superiority of the DQN learning backbone. These results highlight the effectiveness of integrating graph-based representation with reinforcement learning, while also revealing a trade-off between latency optimization and energy efficiency.

Multi-application operator placement in cloud-edge infrastructure for big data stream processing

A two-layer optimized architecture for energy- and QoS-aware fog computing in distributed IoT ecosystems

Findings confirm that system stability and service quality are bounded by fog density, QoS-aware routing, and real-time load regulation, rather than by mere resource scaling.

Edge Computing Architectures for Ultra-Low Latency Applications

The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.