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, 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.
Internet of Things (IoT) applications increasingly offload sensing and analytic tasks to edge and cloud resources. Cloud processing offers high computational capacity but can increase round-trip delay and wireless energy consumption, while edge processing reduces access delay but can suffer from queue buildup when many devices select the same nearby node. This study proposes LEAOT: Load-, Deadline-, Latency-, and Energy-Aware Task Offloading for Scalable IoT Edge–Cloud Systems, a lightweight load- and deadline-aware task offloading method for IoT edge–cloud systems. Unlike black-box learning methods, LEAOT uses an explainable online score that combines the estimated communication delay, the First-Come First-Served (FCFS) aggregate queue delay, the execution delay, the device-side energy, the soft deadline pressure, and a dimensionless infrastructure-pressure term. The method also uses an exponentially weighted link estimate and a virtual workload correction step so that stale bandwidth and queue estimates are not treated as fixed constants. A controlled discrete-event evaluation is conducted across 10 independent trials, heterogeneous task sizes, and scalable edge topologies ranging from 2 to 16 edge nodes. Results show that LEAOT reduces deadline violation to 0.69% and maintains low energy of 0.117 J/task under the reference setting, while remaining competitive with delay-resource and drift-plus-penalty baselines. The results also quantify the effect of node scalability and the resource-pressure weight.
DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management, is presented.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
