Load Balancing in Mobile Edge Computing: A Reinforcement Learning Approach

Tahmasebi-Pouya, Niloofar; Sarram, Mehdi-Agha; Mostafavi, Seyedakbar

doi:10.1109/sciot56583.2022.9953602

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A DRL-based solution for load balancing between MEC edge servers and the cloud is proposed by Tahmasebi-Pouya et al (2022) where each MEC server is considered a separate agent. A Qlearning-based approach is adopted in which the agent decided either to offload requests to neighboring MEC servers or the cloud server.…”

Section: Load Balancing For Mobile Edge Computingmentioning

confidence: 99%

Reinforcement learning for communication load balancing: approaches and challenges

Wu,

Li,

Ferini

et al. 2023

Front. Comput. Sci.

View full text Add to dashboard Cite

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

show abstract

Section: Load Balancing For Mobile Edge Computingmentioning

confidence: 99%