A Machine Learning Based Management System for Network Services

Türk, Yekta; Zeydan, Engin; Bilgin, Zeki

doi:10.1109/wimob.2019.8923572

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The border gateway protocol (BGP) data on autonomous systems (ASes) are introduced in [10], [11] for anomaly detection. ML-enabled automation in network service management has been presented in [12], where service quality states on a small network with six routers are projected using decision tree and gradient boosting algorithms. However, the current ML models target limited network resources and datasets on a special and small-scale network, which can hardly be applied to SICNs requiring high accuracy performance with efficient executions.…”

Section: A Employing ML Methodsmentioning

confidence: 99%

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

2021

IEEE Commun. Mag.

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Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world. However, an indispensable part for CNs is network management, where responsive and autonomous maintenance is much needed. With the technological advancement in telecommunications networks, a classical satellite-dependent CN is envisioned to be transformed into a satellite-integrated CN (SICN), which will embrace significant autonomy, intelligence, and scalability in network management. This article discusses the machine-learning (ML) based hierarchical approach to enabling autonomous selfmaintenance for SICNs. The approach is split into the anomaly identification and anomaly mitigation phases, where the related ML methods, data collection means, deployment options, and mitigation schemes are presented. With the case study, we discuss a typical scenario using satellite and fixed connections as backhaul options and show the effectiveness and performance improvements of the proposed approach with recurrent neural network and ensemble methods.

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Section: A Employing ML Methodsmentioning

confidence: 99%

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

2021

IEEE Commun. Mag.

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“…Such a system depends on ML methods and BDA to reduce human interaction with network status and policy data. For example, the EL-based root cause analysis proposed by Turk et al [196] must automatically discover problems and initiate suitable treatments using ML techniques without human participation. Since the RL and DRL approaches do not rely on previous information and instead gain the needed knowledge via trial and error, they are often employed to manage large and distributed networks.…”

Section: 51mentioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

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The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.

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“…may need to be designed to a specific problem, require high computational complexities and lack scalability features for large scale network optimization problems. Therefore, combining network orchestration with emerging ML concepts has been the focus of many research papers in recent years using reinforcement learning strategies in [5], slice admission policy control in [6], root cause analysis in [7] and network-aware scheduling policies to consider network parameters at deployment time in [8]. In this paper, we continually monitor network parameters during the whole service operation lifetime.…”

Section: A Management and Orchestration Of Cloud And Transport Networkmentioning

confidence: 99%

Intelligent Service Orchestration in Edge Cloud Networks

2021

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The surge in data traffic is challenging for the network infrastructure owners coping with stringent service requirements (e.g. high bandwidth, ultra-low latency) as well as shrinking per GB revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of Software Defined Networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct Edge Clouds (ECs) (i.e. with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud based parameters. At the end of the paper, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-toend mobile network architecture and H2020 5Growth project's baseline management platform.

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A Machine Learning Based Management System for Network Services

Cited by 4 publications

References 12 publications

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

Intelligent Service Orchestration in Edge Cloud Networks

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