LEAOT: Load-, Deadline-, Latency-, and Energy-Aware Task Offloading for Scalable IoT Edge–Cloud Systems

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.