A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry

Meiming FU; Qingyang LIU; Jiayi LIU; Xiang WANG; Hongyan YANG

doi:10.1587/transinf.2021EDP7138

IEICE TRANSACTIONS on Information

A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry

Meiming FU, Qingyang LIU, Jiayi LIU, Xiang WANG, Hongyan YANG

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Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.

Publication: IEICE TRANSACTIONS on Information Vol.E105-D No.2 pp.344-354

Publication Date: 2022/02/01

Publicized: 2021/10/27

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2021EDP7138

Type of Manuscript: PAPER

Category: Information Network

Authors

Meiming FU
  Shenzhen Smart-chip Microelectronics Technology Co., Ltd.
Qingyang LIU
  Shenzhen Guodian Technology Communication Co., Ltd.
Jiayi LIU
  Xidian University
Xiang WANG
  Beijing Smart-chip Microelectronics Technology Co., Ltd.
Hongyan YANG
  Xidian University

Keyword

SFC, fault analysis, INT, LSTM, SVM

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Meiming FU, Qingyang LIU, Jiayi LIU, Xiang WANG, Hongyan YANG, "A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry" in IEICE TRANSACTIONS on Information, vol. E105-D, no. 2, pp. 344-354, February 2022, doi: 10.1587/transinf.2021EDP7138.
Abstract: Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7138/_p

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@ARTICLE{e105-d_2_344,
author={Meiming FU, Qingyang LIU, Jiayi LIU, Xiang WANG, Hongyan YANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry},
year={2022},
volume={E105-D},
number={2},
pages={344-354},
abstract={Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.},
keywords={},
doi={10.1587/transinf.2021EDP7138},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry
T2 - IEICE TRANSACTIONS on Information
SP - 344
EP - 354
AU - Meiming FU
AU - Qingyang LIU
AU - Jiayi LIU
AU - Xiang WANG
AU - Hongyan YANG
PY - 2022
DO - 10.1587/transinf.2021EDP7138
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.
ER -

IEICE TRANSACTIONS on Information