Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

TL;DR

This paper proposes a distributionally robust optimization framework for computation offloading in aerial access networks, effectively handling uncertain task data sizes to optimize latency and energy consumption.

Contribution

It introduces a novel distributionally robust approach using historical data to improve offloading decisions in aerial MEC networks under uncertainty.

Findings

The proposed algorithm reduces system latency more effectively.

It balances latency reduction and energy consumption.

Simulation results outperform existing benchmark methods.

Abstract

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next,we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature.