Incorporation of deep learning strategies to improve QOS in VXLAN-based networks
Siriniwansa, Kumarawadu KR (2025)
2025
Master's Programme in Computing Sciences and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
Hyväksymispäivämäärä
2025-10-21
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-2025102010010
https://urn.fi/URN:NBN:fi:tuni-2025102010010
Tiivistelmä
This study examines how to improve Quality of Service (QoS) management in VXLAN-based overlay networks by integrating deep learning (DL) techniques. Due to issues like fluctuating workloads, complicated virtualized settings, and increased data traffic, traditional QoS mechanisms frequently fail to provide optimal performance metrics like packet loss, latency, and jitter in modern networks.
Examining how DL models, such as CNNs, RNNs, autoencoders, and reinforcement learning, can forecast traffic patterns, identify abnormalities, and optimize resource allocation, the study reviews current QoS strategies in VXLAN.
With a focus on situations like SDN, data centers, and NFV, it examines the possible advantages and intrinsic drawbacks of using DL for real-time QoS enhancement. The report also suggests future directions for real-world application while identifying present research limitations in areas like data availability, model explainability, and secure deployment.
In order to improve efficiency, security, and user experience, work ultimately promotes intelligent, adaptive QoS frameworks driven by DL that can handle the complexity of large-scale, virtualized network infrastructures.
Examining how DL models, such as CNNs, RNNs, autoencoders, and reinforcement learning, can forecast traffic patterns, identify abnormalities, and optimize resource allocation, the study reviews current QoS strategies in VXLAN.
With a focus on situations like SDN, data centers, and NFV, it examines the possible advantages and intrinsic drawbacks of using DL for real-time QoS enhancement. The report also suggests future directions for real-world application while identifying present research limitations in areas like data availability, model explainability, and secure deployment.
In order to improve efficiency, security, and user experience, work ultimately promotes intelligent, adaptive QoS frameworks driven by DL that can handle the complexity of large-scale, virtualized network infrastructures.