Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management

Bashar, Abul; Parr, Gerard; McClean, Sally; Scotney, Bryan; Nauck, Detlef

doi:10.1007/978-3-642-15280-1_47

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…In this scenario, users request "lines" by making calls which must be served by the network. There exist many heuristic algorithms for this problem, and also ML has been applied, e.g., leveraging Supervised Learning (SL) methods [9]- [13] for estimation of performance metrics or Reinforcement Learning (RL) for admission policy optimization [14]- [16]. Bashar et al [12] give a concrete example for an SL approach.…”

Section: A Call Admission Controlmentioning

confidence: 99%

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Zerwas

Kalmbach

Schmid

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fireand-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm that faces repeating problem instances, it may be beneficial to learn from network states and the outcome of acceptance decisions of the past. In this paper, we propose ISMAEL, a Machine Learning framework for predicting the acceptance of Virtual Clusters, one of the most common virtual network abstractions in data centers. ISMAEL can be configured with, and learn from, different existing algorithms by combining fixed-size feature representations for graphs with a Convolutional Neural Network or a Fully Connected Deep Neural Network. We report on extensive simulations, which demonstrate that it is possible to mimic existing, computationally-intensive admission control algorithms with an accuracy of up to 94 %, while significantly reducing runtime.

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Section: A Call Admission Controlmentioning

confidence: 99%

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Zerwas

Kalmbach

Schmid

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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“…Support Vector Machine (SVM) based CAC algorithm utilises service vector and network vector to predict admission state for admission decisions [12]. A more recent BN-based CAC framework has been proposed by us which implements delay prediction (about 97% prediction accuracy) based call admission decisions [13].…”

Section: Background and Related Workmentioning

confidence: 99%

Machine learning based Call Admission Control approaches: A comparative study

Bashar

Parr

McClean

et al. 2010

2010 International Conference on Network and Service Management

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The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches -Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.

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“…Efficient monitoring, control and management of telecommunication networks [9], and network fault diagnosis, analysis and predictions [10,11] are two main categories of applications of Bayesian network formalism in mobile communications. In addition to the above applications, we introduced a new application of Bayesian networks for modeling user preferences for radio access selection [12,13] which was later extended [14,15].…”

Section: Bayesian Networkmentioning

confidence: 99%

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

Homayounvala¹

2015

ijacsa

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Abstract-In cognitive radio networks where secondary users (SUs) use the time-frequency gaps of primary users' (PUs) licensed spectrum opportunistically, the experienced throughput of SUs depend not only on the traffic load of the PUs but also on the PUs' service type. Each service has its own pattern of channel usage, and if the SUs know the dominant pattern of primary channel usage, then they can make a better decision on choosing which service is better to be used at a specific time to get the best advantage of the primary channel, in terms of higher achievable throughput. However, it is difficult to inform directly SUs of PUs' dominant used services in each area, for practical reasons. This paper proposes a learning mechanism embedded in SUs to sense the primary channel for a specific length of time. This algorithm recommends the SUs upon sensing a free primary channel, to choose the best service in order to get the best performance, in terms of maximum achieved throughput and the minimum experienced delay. The proposed learning mechanism is based on a Bayesian approach that can predict the performance of a requested service for a given SU. Simulation results show that this service selection method outperforms the blind opportunistic SU service selection, significantly.

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Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management

Cited by 6 publications

References 14 publications

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Machine learning based Call Admission Control approaches: A comparative study

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

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