Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Accurate and Efficient Monitoring for Virtualized SDN in Clouds

Authors
Yang, G.Yoo, Y.Kang, M.Jin, H.Yoo, C.
Issue Date
2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Cloud computing; Control systems; Delays; Distributed systems; Monitoring; Network management; Network monitoring; Payloads; Virtual machine monitors; Virtualization
Citation
IEEE Transactions on Cloud Computing
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Cloud Computing
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138450
DOI
10.1109/TCC.2021.3089225
ISSN
2168-7161
Abstract
This paper presents V-Sight, a network monitoring framework for programmable virtual networks in clouds. Network virtualization based on software-defined networking (SDN-NV) in clouds makes it possible to realize programmable virtual networks; consequently, this technology offers many benefits to cloud services for tenants. However, to the best of our knowledge, network monitoring, which is a prerequisite for managing and optimizing virtual networks, has not been investigated in the context of SDN-NV systems. As the first framework for network monitoring in SDN-NV, we identify three challenges: non-isolated and inaccurate statistics, high monitoring delay, and excessive control channel consumption for gathering statistics. To address these challenges, V-Sight introduces three key mechanisms: 1) statistics virtualization for isolated statistics, 2) transmission disaggregation for reduced transmission delay, and 3) pCollector aggregation for efficient control channel consumption. The evaluation results reveal that V-Sight successfully provides accurate and isolated statistics while reducing the monitoring delay and control channel consumption in orders of magnitude. We also show that V-Sight can achieve a data plane throughput close to that of non-virtualized SDN. IEEE
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yoo, Chuck photo

Yoo, Chuck
Department of Computer Science and Engineering
Read more

Altmetrics

Total Views & Downloads

BROWSE