TL;DR
A resource-aware multi-application operator placement method that optimizes both end-to-end latency and network usage, while meeting QoS constraints and application owners’ preferences in heterogeneous cloud-edge environments is proposed.
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
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.
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded. This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay. Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Effective emergency management demands realtime processing and fusion of massive, heterogeneous data streams from IoT sensors, video surveillance, social media, and wireless platforms distributed across disaster-affected regions. Existing cloud-centric architectures suffer from prohibitive latency, while static edge-cloud deployments fail to exploit the complementary nature of multi-source emergency data for coherent situational awareness. This paper presents Emerald, an edgeto-cloud data pipeline with integrated multi-source fusion, architected over JointCloud infrastructure for real-time emergency situational awareness. Emerald introduces three key components: (1) an urgency-aware adaptive computation offloading strategy that dynamically redistributes workloads between edge, fog, and cloud layers according to a multi-dimensional disaster urgency model; (2) a fault-tolerant data transmission protocol with breakpoint resume that preserves critical data integrity under the evaluated degraded and intermittent network conditions common in disaster zones; and (3) a conflict-penalized quality-aware data fusion method at the fog layer featuring context-adaptive dynamic credibility assessment and adaptive conflict-penalized belief aggregation based on modified Dempster-Shafer evidence theory. Experimental evaluation using the iFogSim2 simulator with parameters drawn from the 2021 Henan floods and the 2023 Turkey-Syria earthquake demonstrates that Emerald achieves 11.1% lower end-to-end latency, 99.2% system availability, 7.7% higher fusion F1-score, and preserves all critical data items under the evaluated failure settings. Scalability experiments further show that Emerald's fusion F1-score improves from 68.3% to 87.8% as infrastructure scales from 50 to 200 edge nodes.
In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
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 to provide fairness, optimal resource utilisation, and balanced workloads.
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