TL;DR
The results demonstrate that QoS-aware distributed work stealing matches the makespan of the evaluated centralised dynamic scheduler without placing it on the critical path, scales without performance degradation across the evaluated range of up to 64 devices, and achieves the highest and most consistent cluster utilisation.
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 key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum.
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,...
LEAOT is proposed, a lightweight load- and deadline-aware task offloading method for IoT edge–cloud systems that reduces deadline violation to 0.69% and maintains low energy under the reference setting, while remaining competitive with delay-resource and drift-plus-penalty baselines.
A framework is proposed for collaborative decision-making learning to select secure, load-balanced routing paths, leveraging multiple-level distributed computation to achieve trustworthy communication.
DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management, is presented.
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