Toward Intelligent Task Offloading at the Edge

With the booming development of IoT and massive smart MDs springing up in daily life, the conflict between resource-hungry IoT applications and resource-constrained MDs becomes increasingly prominent. To cope with compute-intensive applications and big data, MCC combining AI was adopted as a workable solution. Nevertheless, considering MCC's long transmission latency and the ultra-low latency requirements of most IoT applications, traditional MCC combining AI is not applicable any more in the era of IoT. Migrating cloud computing capabilities to the edge, and integrating AI with it, are envisioned to be a promising paradigm, which gives rise to the so-called edge intelligence. As a pivotal technique in edge computing, task offloading can effectively improve the MDs' computation and energy efficiency. However, existing research on task offloading mostly focused on fixed scenarios and cannot deal with varying situations, where user privacy protection was neglected either. Toward this end, we introduce machine learning into task offloading at the edge, and design an intelligent task offloading scheme. Extensive numerical results demonstrate that our proposed scheme cannot only have good adaptability and security, but also achieve high prediction accuracy and low processing delay, compared to traditional offloading schemes.