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Policy-Induced Unsu...
Policy-Induced Unsupervised Feature Selection : A Networking Case Study
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- Taghia, J. (författare)
- Ericsson Res, Stockholm, Sweden.
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- Moradi, F. (författare)
- Ericsson Res, Stockholm, Sweden.
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- Larsson, H. (författare)
- Ericsson Res, Stockholm, Sweden.
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- Lan, X. (författare)
- Ericsson Res, Stockholm, Sweden.
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- Ebrahimi, Masoumeh (författare)
- KTH,Elektronik och inbyggda system,Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.;KTH, Div Elect & Embedded Syst, Stockholm, Sweden.
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- Johnsson, Andreas (författare)
- Uppsala universitet,Avdelningen för datorteknik,Ericsson Res, Stockholm, Sweden.
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Ericsson Res, Stockholm, Sweden Elektronik och inbyggda system (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: Proceedings - IEEE INFOCOM. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 750-759, s. 750-759
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- A promising approach for leveraging the flexibility and mitigating the complexity of future telecom systems is the use of machine learning (ML) models that can analyze the network performance, as well as taking proactive actions. A key enabler for ML models is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network. However, excessive monitoring is associated with network overhead. Considering domain knowledge may provide clues to find a balance between overhead reduction and meeting requirements on future ML use cases by monitoring just enough features. In this work, we propose a method of unsupervised feature selection that provides a structured approach in incorporation of the domain knowledge in terms of policies. Policies are provided to the method in form of must-have features defined as the features that need to be monitored at all times. We name such family of unsupervised feature selection the policy-induced unsupervised feature selection as the policies inform selection of the latent features. We evaluate the performance of the method on two rich sets of data traces collected from a data center and a 5G-mmWave testbed. Our empirical evaluations point at the effectiveness of the solution.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Feature Selection
- Case-studies
- Domain knowledge
- Machine learning models
- Machine-learning
- Measurement points
- Network overhead
- Overhead reductions
- Structured approach
- Telecom systems
- Unsupervised feature selection
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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