Details

Autor(en) / Beteiligte

• Khatri, Anshu
• Mathi, Senthilkumar
• A, Ashiq Sheriff
• R, Deepthika
• G, Naveen Raaghavendran
• Ramalingam, Venkadeshan

Titel

An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility

Ist Teil von

• Procedia computer science, 2024, Vol.235, p.2135-2143

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods.