A fairness-aware task offloading method in edge-enabled IIoT with multi-constraints using AGE-MOEA and weighted MMF

By providing distributed and ultra-low-latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher-quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness-aware task offloading method, called FOIMAM. Specifically, the p$$ p $$-norm is introduced to accommodate the Pareto plane under the non-Euclidean geometry framework while the evaluation and elimination of low-quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi-constraint problem and resolved using weighted max-min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees.