A hybrid approach to QoS measurements in cellular networks
Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However...
Ausführliche Beschreibung
Autor*in: |
Boz, Eren [verfasserIn] Manner, Jukka [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 172 |
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Übergeordnetes Werk: |
volume:172 |
DOI / URN: |
10.1016/j.comnet.2020.107158 |
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Katalog-ID: |
ELV003977188 |
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520 | |a Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. | ||
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2020 |
allfields |
10.1016/j.comnet.2020.107158 doi (DE-627)ELV003977188 (ELSEVIER)S1389-1286(19)31424-0 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Boz, Eren verfasserin aut A hybrid approach to QoS measurements in cellular networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. Network measurement Cellular network Probe-based measurement Hybrid measurement Manner, Jukka verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172 |
spelling |
10.1016/j.comnet.2020.107158 doi (DE-627)ELV003977188 (ELSEVIER)S1389-1286(19)31424-0 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Boz, Eren verfasserin aut A hybrid approach to QoS measurements in cellular networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. Network measurement Cellular network Probe-based measurement Hybrid measurement Manner, Jukka verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172 |
allfields_unstemmed |
10.1016/j.comnet.2020.107158 doi (DE-627)ELV003977188 (ELSEVIER)S1389-1286(19)31424-0 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Boz, Eren verfasserin aut A hybrid approach to QoS measurements in cellular networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. Network measurement Cellular network Probe-based measurement Hybrid measurement Manner, Jukka verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172 |
allfieldsGer |
10.1016/j.comnet.2020.107158 doi (DE-627)ELV003977188 (ELSEVIER)S1389-1286(19)31424-0 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Boz, Eren verfasserin aut A hybrid approach to QoS measurements in cellular networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. Network measurement Cellular network Probe-based measurement Hybrid measurement Manner, Jukka verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172 |
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10.1016/j.comnet.2020.107158 doi (DE-627)ELV003977188 (ELSEVIER)S1389-1286(19)31424-0 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Boz, Eren verfasserin aut A hybrid approach to QoS measurements in cellular networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. Network measurement Cellular network Probe-based measurement Hybrid measurement Manner, Jukka verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172 |
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title |
A hybrid approach to QoS measurements in cellular networks |
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A hybrid approach to QoS measurements in cellular networks |
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Boz, Eren |
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Boz, Eren |
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10.1016/j.comnet.2020.107158 |
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004 620 |
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verfasserin |
title_sort |
a hybrid approach to qos measurements in cellular networks |
title_auth |
A hybrid approach to QoS measurements in cellular networks |
abstract |
Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. |
abstractGer |
Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. |
abstract_unstemmed |
Due to constantly increasing demand for mobile data, cellular network infrastructures running on limited radio spectrum are struggling to keep up. Constant monitoring and measurements are necessary to ensure service quality as saturated networks are not able to deliver consistent experience. However, measuring mobile networks at scale bears some fundamental problems. Although there are significant improvements in capabilities of mobile networks (e.g. bit rate, latency), measuring them is still rather complicated task compared to fixed networks given that in mobile networks, performance is a result of complex interaction between momentary cell load, adjacent cell interference, shadowing, fading, mobility and user device capabilities. The adoption of commercial 5G networks is expected to increase the variability even more as it depends on smaller cells. Active measurements that inject large amounts of traffic into the network for the sole purpose of measuring are costly in terms of both bandwidth and energy. Passive mechanisms are lightweight but miss the information of why a certain bit rate is received or sent by the end device. They can not tell whether the performance bottleneck is in the network or in the service itself. By combining active and passive measurements in a novel way, this study focuses on a hybrid measurement approach; that is cost-efficient, scalable and comparably accurate. In this paper, we develop a hybrid methodology where we passively measure incoming and outgoing bit rates and augment them with concurrent probe-based latency measurements to enable accurate network capacity estimations. We provide a model and heuristics to overcome issues related to radio access complications, capacity estimation, and optimization. Finally, we implement a prototype, deploy and evaluate it thoroughly to provide a proof-of-concept. We find that the proposed approach is not only highly accurate and a much efficient alternative to active measurements, but also superior in measuring user experienced quality. |
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title_short |
A hybrid approach to QoS measurements in cellular networks |
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