What question did this study set out to answer?

Investigate the timing of computation offloading from a performance viewpoint in dynamic environments.

April 30, 2026Open Access

Investigation and RL-based Optimization of Offloading Decision Making in Industrial Edge Computing Scenarios

EVEugen VolkUniversity of Stuttgart AAAlexander ArtemenkoRobert Bosch Hospital IFIndrit FejzaSystems, Applications & Products in Data Processing (Germany)

Key Points

Investigate the timing of computation offloading from a performance viewpoint in dynamic environments.
Designed and trained a Reinforcement Learning (RL) agent for decision making.
Emulated real hardware in industrial environments using 5G New Radio.
Tested optimization under various conditions.
RL agent outperformed existing closed-form solutions.
Improved total completion time of emulated applications.
Reduced power consumption for client devices.

Abstract

Abstract. Computation Offloading (CO) transfers computationally intensive tasks from hardware-limited devices (clients) to more powerful servers. This enablesclients to overcome performance and power limitations and meet Quality of Service requirements. Research in CO focuses on "Why", “What”, "If/When", "Where", and"How" to offload. This work investigates the "If/When" question from a performance perspective in dynamic environments. To optimize decision making, a ReinforcementLearning (RL) agent was designed, implemented, and trained to make efficient offloading decisions. The practicality of our solution was tested by emulating real hardwarein industrial environments using 5G New Radio. Experimental results show that the proposed RL agent outperforms the existing closed-form solution from the literature, improves the total completion time of emulated apps, and reduces client device power consumption.

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