Freshness-Driven Resource Allocation for Partial Task Offloading in IoT Networks

Abstract

The evolution towards 6G communication technology heightens the demand for data freshness. Consequently, Age of Information (AoI), a key metric quantifying data freshness, has raised significant attention from academia and industry. This paper investigates a Multi-access Edge Computing (MEC) network with multiple servers designed to support mission-critical, low-latency computational services. We characterize the transmission reliability with FBL codes in the communication phase. Using extreme value theory, we analyze the occurrence of extreme queue length violations during the computation time phase. Based on the characterizations, we develop an optimal framework incorporating server selection and scheduling strategies for minimizing the average AoI. Via numerical simulations, we validate our algorithm’s effectiveness in enhancing AoI performance, demonstrate how varying parameters affect system performance, and illustrate the potential of our method for guiding future MEC system designs.

Authors 5

1. Jingrui Wei

   Wuhan University

   Affiliation as printed

   Wuhan University,School of Electronic Information,Wuhan,China,430000

2. Chenhao Shi

   Wuhan University

   Affiliation as printed

   Wuhan University,School of Electronic Information,Wuhan,China,430000

3. Yao Lin Zhu

   Wuhan University

   Affiliation as printed

   Wuhan University,School of Electronic Information,Wuhan,China,430000

4. Yuanming Hu

   Wuhan University

   Affiliation as printed

   Wuhan University,School of Electronic Information,Wuhan,China,430000

5. Anke Schmeink Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics,Aachen,Germany,52068