Probabilistic Knowledge-Graph based Workflow Recommender for Network Management Automation

Aumayr, Erik; Wang, Mingxue; Bosneag, Anne-Marie

doi:10.1109/wowmom.2019.8793049

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…Some methods [5]- [7] present operators trouble tickets relevant to a failure by using machine learning techniques such as topic models. Other methods [8]- [10] analyze multiple trouble tickets (or official troubleshooting guidelines if any) and visualize them as a workflow chart that operators can understand easily. These methods shorten recovery time by supporting operators' decision making.…”

Section: Related Work a Trouble-ticket-based Methodsmentioning

confidence: 99%

“…The latter step can be automated using run book automation (RBA) tools, but the former needs to be done by operators and is quite time-consuming. Although there are studies [5]- [10] that suggest recovery actions for operators, most rely on trouble tickets, which do not always include sufficient information to precisely determine recovery commands. As such, we still cannot free operators from the task of recovering from failures in spite of much research on the topic.…”

Section: Introductionmentioning

confidence: 99%

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Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning

Ikeuchi¹,

Watanabe²,

Hirao³

et al. 2020

Preprint

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With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically detect anomalies and analyze root causes of failures with current methods, making decisions on what commands should be executed to recover from failures still depends on manual operation, which is quite time-consuming. Toward automatic recovery, we propose a method of estimating recovery commands by using Seq2Seq, a neural network model. This model learns complex relationships between logs obtained from equipment and recovery commands that operators executed in the past. When a new failure occurs, our method estimates plausible commands that recover from the failure on the basis of collected logs. We conducted experiments using a synthetic dataset and realistic OpenStack dataset, demonstrating that our method can estimate recovery commands with high accuracy.

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Section: Related Work a Trouble-ticket-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning

Ikeuchi¹,

Watanabe²,

Hirao³

et al. 2020

Preprint

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“…Defining and conceptualizing the structure of telecommunication networks can be explicitly delivered through incorporating KGs. Aumayr et al [104] benefited from the unique structure of KG to build a graph that can be used to solving issues that confront telecom operators. The authors populated the KG with entities captured form community knowledge in forms of telecom and products documentations, online sites, engineering reports, etc.…”

Section: Ictmentioning

confidence: 99%

Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

244

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“…• KG based M2M SemCom: KG construction and update [126][127][128][129][130][131], KG based network management [132][133][134], interpretation for cross-domain applications [135][136][137].…”

Section: Kg Based Semcommentioning

confidence: 99%

“…Such a KG paves a foundation for facilitating vehicle-to-vehicle communication (e.g., exchange of state information) to avoid accidents or facilitate platooning as well as a platform for traffic management and operating ride-sharing or car-hailing services. Second, KGs provide a tool for managing SemCom or other types of networks to facilitate resource allocation, work-flow recommendation, and service selection [132][133][134]. The machine intelligence needed for efficient network management can be powered by structured knowledge embedded in a network KG.…”

Section: Kg and M2m Semcommentioning

confidence: 99%

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

Li¹,

Wen²,

Zhang³

et al. 2021

Preprint

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In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-Things (IoT) and artificial intelligence (AI).By broadening the scope of the classic communication-theoretic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and "dialogue". On the other hand, M2M SemCom refers to effectiveness techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: (1) layer-coupling design; and (2) end-to-end design using a neural network. The second part focuses on discussion of specific techniques for different application areas of H2M SemCom (including human and AI symbiosis, recommendation, human sensing and care, and virtual reality (VR)/augmented reality (AR)) and M2M SemCom (including distributed learning, split inference, distributed consensus, and machine-vision cameras). Finally, we discuss the approach for designing SemCom systems based on knowledge graphs. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation (6G) featuring connected intelligence and integrated sensing, computing, communication, and control.

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Probabilistic Knowledge-Graph based Workflow Recommender for Network Management Automation

Cited by 9 publications

References 7 publications

Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning

Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning

Domain-specific knowledge graphs: A survey

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

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