BOLT: A Bayesian Online Learning Framework for Time Sensitive Networks in Metaverse

Document Type

Conference Proceeding

Source of Publication

2023 International Conference on Intelligent Metaverse Technologies & Applications (iMETA)

Publication Date

1-20-2023

Abstract

Efficient load balancing in wireless networks is a critical challenge that directly impacts resource utilization and user experience for time-sensitive networks in the Metaverse. This paper addresses this challenge by proposing a Bayesian online learning approach, enhanced by Software-Defined Networking (SDN) principles and the OpenFlow API. The objective is to dynamically optimize traffic distribution across multiple access points (APs) in wireless networks using real-time observations. The proposed solution integrates SDN concepts and leverages the OpenFlow API to enable centralized control and dynamic configuration of network elements. By employing Bayesian online learning, the load-balancing policy is continuously updated based on real-time observations and historical data. This approach minimizes total load imbalance across APs, accounting for uncertainties inherent in network dynamics. The Bayesian online learning framework incorporates proba-bilistic models to capture load fluctuations and estimation errors. By utilizing historical data and observed network behavior, the model refines its load-balancing decisions over time. MATLAB is utilized for simulations, demonstrating the effectiveness of the proposed approach in achieving load-balancing objectives for time-sensitive networks in the Metaverse. The results underscore the adaptability and robustness of the system in managing load variations while leveraging the power of SDN and Bayesian learning.

ISBN

979-8-3503-2845-5

Publisher

IEEE

Volume

00

First Page

1

Last Page

7

Disciplines

Computer Sciences

Keywords

Adaptation models, Metaverse, Wireless networks, Bandwidth, Fasteners, Load management, Real-time systems

Indexed in Scopus

no

Open Access

no

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