TY - GEN
T1 - Network Abnormality Diagnosis Visualization for Computing and Network Convergence Environment
AU - Gao, Jiachang
AU - Gao, Jing
AU - Feng, Lei
AU - Hong, Sheng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - With the development of the computing and networking convergence environment, service deployment has gradually adopted a graph-structured service function chaining (SFC) approach. In this context, service anomalies may arise from multiple factors such as nodes, networks, and service components. Due to the close correlation among various levels of indicators, it is often difficult to identify the root cause of the anomaly among them, making anomaly analysis and insight more challenging. Therefore, this paper aims to study a network telemetry-based anomaly diagnosis visualization method to improve the efficiency of network administrators in locating the root cause of anomalies in complex computing and networking convergence environments. Through the anomaly diagnosis visualization module developed in this study, network administrators can intuitively understand the abnormal conditions and impact scope in the system, thus locating the root cause of the anomalies more effectively.
AB - With the development of the computing and networking convergence environment, service deployment has gradually adopted a graph-structured service function chaining (SFC) approach. In this context, service anomalies may arise from multiple factors such as nodes, networks, and service components. Due to the close correlation among various levels of indicators, it is often difficult to identify the root cause of the anomaly among them, making anomaly analysis and insight more challenging. Therefore, this paper aims to study a network telemetry-based anomaly diagnosis visualization method to improve the efficiency of network administrators in locating the root cause of anomalies in complex computing and networking convergence environments. Through the anomaly diagnosis visualization module developed in this study, network administrators can intuitively understand the abnormal conditions and impact scope in the system, thus locating the root cause of the anomalies more effectively.
KW - Data visualization
KW - Network abnormality diagnosis
KW - Visual analysis
UR - https://www.scopus.com/pages/publications/105012487321
U2 - 10.1007/978-981-96-6462-7_33
DO - 10.1007/978-981-96-6462-7_33
M3 - 会议稿件
AN - SCOPUS:105012487321
SN - 9789819664610
T3 - Communications in Computer and Information Science
SP - 368
EP - 379
BT - Information Processing and Network Provisioning - 3rd International Conference, ICIPNP 2024, Proceedings
A2 - Kadoch, Michel
A2 - Cheriet, Mohamed
A2 - Qiu, Xuesong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Information Processing and Network Provisioning, ICIPNP 2024
Y2 - 14 June 2024 through 16 June 2024
ER -