LEAOT: Load-, Deadline-, Latency-, and Energy-Aware Task Offloading for Scalable IoT Edge–Cloud Systems
TL;DR
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.
Abstract
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.