5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation
This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle managem...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Moazzeni, Shadi [verfasserIn] Katsaros, Konstantinos [verfasserIn] Ferdosian, Nasim [verfasserIn] Antonakoglou, Konstantinos [verfasserIn] Rouse, Mark [verfasserIn] Kaleshi, Dritan [verfasserIn] Fernández-Fernández, Adriana [verfasserIn] Catalan-Cid, Miguel [verfasserIn] Vrontos, Constantinos [verfasserIn] Nejabati, Reza [verfasserIn] Simeonidou, Dimitra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 241 |
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| Übergeordnetes Werk: |
volume:241 |
| DOI / URN: |
10.1016/j.comnet.2024.110202 |
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| Katalog-ID: |
ELV067157750 |
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| 245 | 1 | 0 | |a 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation |
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| 520 | |a This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. | ||
| 650 | 4 | |a Cross-domain orchestration | |
| 650 | 4 | |a VNF profiling | |
| 650 | 4 | |a Resource minimising | |
| 700 | 1 | |a Katsaros, Konstantinos |e verfasserin |0 (orcid)0000-0001-5372-7201 |4 aut | |
| 700 | 1 | |a Ferdosian, Nasim |e verfasserin |0 (orcid)0000-0003-2014-1831 |4 aut | |
| 700 | 1 | |a Antonakoglou, Konstantinos |e verfasserin |0 (orcid)0000-0002-2703-8864 |4 aut | |
| 700 | 1 | |a Rouse, Mark |e verfasserin |0 (orcid)0009-0004-0680-0807 |4 aut | |
| 700 | 1 | |a Kaleshi, Dritan |e verfasserin |4 aut | |
| 700 | 1 | |a Fernández-Fernández, Adriana |e verfasserin |0 (orcid)0000-0003-1616-5582 |4 aut | |
| 700 | 1 | |a Catalan-Cid, Miguel |e verfasserin |0 (orcid)0000-0001-5171-7771 |4 aut | |
| 700 | 1 | |a Vrontos, Constantinos |e verfasserin |4 aut | |
| 700 | 1 | |a Nejabati, Reza |e verfasserin |4 aut | |
| 700 | 1 | |a Simeonidou, Dimitra |e verfasserin |4 aut | |
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10.1016/j.comnet.2024.110202 doi (DE-627)ELV067157750 (ELSEVIER)S1389-1286(24)00034-3 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Moazzeni, Shadi verfasserin aut 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. Cross-domain orchestration VNF profiling Resource minimising Katsaros, Konstantinos verfasserin (orcid)0000-0001-5372-7201 aut Ferdosian, Nasim verfasserin (orcid)0000-0003-2014-1831 aut Antonakoglou, Konstantinos verfasserin (orcid)0000-0002-2703-8864 aut Rouse, Mark verfasserin (orcid)0009-0004-0680-0807 aut Kaleshi, Dritan verfasserin aut Fernández-Fernández, Adriana verfasserin (orcid)0000-0003-1616-5582 aut Catalan-Cid, Miguel verfasserin (orcid)0000-0001-5171-7771 aut Vrontos, Constantinos verfasserin aut Nejabati, Reza verfasserin aut Simeonidou, Dimitra verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 241 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 241 |
| spelling |
10.1016/j.comnet.2024.110202 doi (DE-627)ELV067157750 (ELSEVIER)S1389-1286(24)00034-3 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Moazzeni, Shadi verfasserin aut 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. Cross-domain orchestration VNF profiling Resource minimising Katsaros, Konstantinos verfasserin (orcid)0000-0001-5372-7201 aut Ferdosian, Nasim verfasserin (orcid)0000-0003-2014-1831 aut Antonakoglou, Konstantinos verfasserin (orcid)0000-0002-2703-8864 aut Rouse, Mark verfasserin (orcid)0009-0004-0680-0807 aut Kaleshi, Dritan verfasserin aut Fernández-Fernández, Adriana verfasserin (orcid)0000-0003-1616-5582 aut Catalan-Cid, Miguel verfasserin (orcid)0000-0001-5171-7771 aut Vrontos, Constantinos verfasserin aut Nejabati, Reza verfasserin aut Simeonidou, Dimitra verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 241 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 241 |
| allfields_unstemmed |
10.1016/j.comnet.2024.110202 doi (DE-627)ELV067157750 (ELSEVIER)S1389-1286(24)00034-3 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Moazzeni, Shadi verfasserin aut 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. Cross-domain orchestration VNF profiling Resource minimising Katsaros, Konstantinos verfasserin (orcid)0000-0001-5372-7201 aut Ferdosian, Nasim verfasserin (orcid)0000-0003-2014-1831 aut Antonakoglou, Konstantinos verfasserin (orcid)0000-0002-2703-8864 aut Rouse, Mark verfasserin (orcid)0009-0004-0680-0807 aut Kaleshi, Dritan verfasserin aut Fernández-Fernández, Adriana verfasserin (orcid)0000-0003-1616-5582 aut Catalan-Cid, Miguel verfasserin (orcid)0000-0001-5171-7771 aut Vrontos, Constantinos verfasserin aut Nejabati, Reza verfasserin aut Simeonidou, Dimitra verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 241 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 241 |
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10.1016/j.comnet.2024.110202 doi (DE-627)ELV067157750 (ELSEVIER)S1389-1286(24)00034-3 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Moazzeni, Shadi verfasserin aut 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. Cross-domain orchestration VNF profiling Resource minimising Katsaros, Konstantinos verfasserin (orcid)0000-0001-5372-7201 aut Ferdosian, Nasim verfasserin (orcid)0000-0003-2014-1831 aut Antonakoglou, Konstantinos verfasserin (orcid)0000-0002-2703-8864 aut Rouse, Mark verfasserin (orcid)0009-0004-0680-0807 aut Kaleshi, Dritan verfasserin aut Fernández-Fernández, Adriana verfasserin (orcid)0000-0003-1616-5582 aut Catalan-Cid, Miguel verfasserin (orcid)0000-0001-5171-7771 aut Vrontos, Constantinos verfasserin aut Nejabati, Reza verfasserin aut Simeonidou, Dimitra verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 241 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 241 |
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10.1016/j.comnet.2024.110202 doi (DE-627)ELV067157750 (ELSEVIER)S1389-1286(24)00034-3 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Moazzeni, Shadi verfasserin aut 5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. Cross-domain orchestration VNF profiling Resource minimising Katsaros, Konstantinos verfasserin (orcid)0000-0001-5372-7201 aut Ferdosian, Nasim verfasserin (orcid)0000-0003-2014-1831 aut Antonakoglou, Konstantinos verfasserin (orcid)0000-0002-2703-8864 aut Rouse, Mark verfasserin (orcid)0009-0004-0680-0807 aut Kaleshi, Dritan verfasserin aut Fernández-Fernández, Adriana verfasserin (orcid)0000-0003-1616-5582 aut Catalan-Cid, Miguel verfasserin (orcid)0000-0001-5171-7771 aut Vrontos, Constantinos verfasserin aut Nejabati, Reza verfasserin aut Simeonidou, Dimitra verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 241 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 241 |
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Moazzeni, Shadi @@aut@@ Katsaros, Konstantinos @@aut@@ Ferdosian, Nasim @@aut@@ Antonakoglou, Konstantinos @@aut@@ Rouse, Mark @@aut@@ Kaleshi, Dritan @@aut@@ Fernández-Fernández, Adriana @@aut@@ Catalan-Cid, Miguel @@aut@@ Vrontos, Constantinos @@aut@@ Nejabati, Reza @@aut@@ Simeonidou, Dimitra @@aut@@ |
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2024-01-01T00:00:00Z |
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5g-vios: towards next generation intelligent inter-domain network service orchestration and resource optimisation |
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5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation |
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This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. |
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This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. |
| abstract_unstemmed |
This paper introduces an intelligent network service orchestration platform, referred to as 5G-VIOS for 5G networks and beyond in accordance with the Zero-touch Network and Service Management (ZSM) paradigm. The proposed solution is responsible for an automated network slicing and life-cycle management of network applications and services, across multiple administrative and technological domains. An Artificial Intelligent (AI) model utilising Machine Learning (ML) techniques is exploited to intelligently and efficiently profile the network services and predict the efficient configuration of resources needed to meet the performance targets and Service Level Agreements (SLAs) of these network services across multiple domains. We test and validate the performance of the prediction models for both resource configuration and utilisation in various settings for different resources and data rates. We also showcase how resource utilisation predictions of a virtualised network service can significantly assist in its life cycle management by proactively preventing unnecessary actions such as its migration. |
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5G-VIOS: Towards next generation intelligent inter-domain network service orchestration and resource optimisation |
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Katsaros, Konstantinos Ferdosian, Nasim Antonakoglou, Konstantinos Rouse, Mark Kaleshi, Dritan Fernández-Fernández, Adriana Catalan-Cid, Miguel Vrontos, Constantinos Nejabati, Reza Simeonidou, Dimitra |
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| score |
7.4017773 |