WO2024250199A1 - Dispositif et procédé d'analyse de performance de réseau - Google Patents

Dispositif et procédé d'analyse de performance de réseau Download PDF

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Publication number
WO2024250199A1
WO2024250199A1 PCT/CN2023/098935 CN2023098935W WO2024250199A1 WO 2024250199 A1 WO2024250199 A1 WO 2024250199A1 CN 2023098935 W CN2023098935 W CN 2023098935W WO 2024250199 A1 WO2024250199 A1 WO 2024250199A1
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WO
WIPO (PCT)
Prior art keywords
network
performance
performance metrics
data set
data
Prior art date
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Ceased
Application number
PCT/CN2023/098935
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English (en)
Inventor
Shaked LEIBZIRER
Felix GOLDBERG
Leon Bruckman
Ilan Bar
Jiading RAO
Pavel Kisilev
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Publication date
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Priority to CN202380099141.0A priority Critical patent/CN121264012A/zh
Priority to PCT/CN2023/098935 priority patent/WO2024250199A1/fr
Publication of WO2024250199A1 publication Critical patent/WO2024250199A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/082—Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/22—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/02—Capturing of monitoring data
    • H04L43/022—Capturing of monitoring data by sampling

Definitions

  • the present disclosure relates to a device for network performance analysis.
  • the disclosure further provides a corresponding method for network performance analysis and a computer program product to perform the method.
  • this disclosure has the objectives to improve conventional solutions for network performance analysis, to achieve real time assessment of the network performance, to support large-scale networks, and to allow performing an analysis comparison of different network topologies.
  • a device for network performance analysis is provided.
  • the device is configured to: receive network data, parse the received network data into a data set, where the data set comprises a topology of the network; determine one or more performance metrics of the network based on the data set, based on a mean time between failures (MTBF) and based on a time needed to repair faults in the network; and determine a performance of the network based on the one or more performance metrics.
  • MTBF mean time between failures
  • This provides the advantage of providing a real time evaluation of the performance of the network.
  • the network data includes a schematic description of the network or a parametric description of the network.
  • the device is further configured to generate the data set based on the received network data when parsing the network data into the data set, and generate a routing scheme through the network based on the received network data.
  • the routing scheme includes an expected load-balancing of the network when a failure occurs.
  • the time needed to repair faults in the network includes a mean time to repair (MTTR) .
  • MTTR mean time to repair
  • the numerical simulation includes sampling one or more failure occurrences in the network based on the MTBF, additionally or alternatively sampling one or more times to repair faults based on the MTTR.
  • the numerical simulation may decouple a sampling procedure and an evaluation procedure of the performance metrics, thus allowing a flexible assessment of different network topologies, different scenarios and business objectives.
  • the numerical simulation may comprise a discrete event Monte-Carlo simulation.
  • the one or more results of the numerical simulation include at least one of: a list of congestion events of the network, a size of the network, and a duration of the congestion events.
  • the one or more performance metrics include at least one of: a connectivity metric, a total number of failed links and nodes of the network during a simulation period time, a low congestion metric, a maximum congestion level occurrence of a link during the simulation period time, an average congestion area during the simulation period time, a complementary cumulative distribution Function (CCDF) with a congestion duration, and a network expected convergence time.
  • a connectivity metric a total number of failed links and nodes of the network during a simulation period time
  • a low congestion metric a maximum congestion level occurrence of a link during the simulation period time
  • an average congestion area during the simulation period time a complementary cumulative distribution Function (CCDF) with a congestion duration
  • CCDF complementary cumulative distribution Function
  • the one or more performance metrics may incorporate an aging factor of the network and may integrate essential aspects of the network, wherein the essential aspects of the network may comprise both physical (i.e., connectivity) and logical (i.e., congestion level) aspects.
  • determining the performance of the network based on determined one or more performance metrics includes performing an analysis of the determined one or more performance metrics.
  • determining the performance of the network based on determined one or more performance metrics further includes performing a comparison of two or more network architectures based on the determined one or more performance metrics.
  • the device further comprises a display device configured to display the determined performance of the network, additionally or alternatively to display the comparison of the two or more network architectures.
  • a method for network performance analysis includes: receiving network data; parsing the received network data into a data set, where the data set comprises a topology of the network; determining one or more performance metrics of the network based on the data set, based on a MTBF and based on a time needed to repair faults in the network; and determining a performance of the network based on the one or more performance metrics.
  • This provides the advantage of providing a real time evaluation of the performance of the network.
  • the calculation the one or more performance metrics based on the MTBF and the time needed to repair faults of all the network’s hardware provides the advantage of supporting a comprehensive reliability-driven analysis of the network based on its hardware statistics, and provide accurate and realistic results.
  • the determined performance of the network may take into consideration failures of both links and nodes of the network, which can be effectively used in the network design stage in order to recommend or illustrate a preferable architecture.
  • the network data includes a schematic description of the network or a parametric description of the network.
  • the method further comprises generating the data set based on the received network data when parsing the network data into the data set, and generating a routing scheme through the network based on the received network data.
  • the routing scheme includes an expected load-balancing of the network when a failure occurs.
  • the time needed to repair faults in the network includes a MTTR.
  • determining the one or more performance metrics of the network based on the data set, based on the MTBF, and based on the time needed to repair faults in the network includes: performing a numerical simulation over a predefined time of operation of the network using the data set, the MTBF and the MTTR; and determining the one or more performance metrics of the network based on one or more results of the numerical simulation and based on the generated routing scheme.
  • the numerical simulation includes sampling one or more failure occurrences in the network based on the MTBF, additionally or alternatively sampling one or more times to repair faults based on the MTTR.
  • the numerical simulation may decouple a sampling procedure and an evaluation procedure of the performance metrics, thus allowing a flexible assessment of different network topologies, different scenarios and business objectives.
  • the numerical simulation may comprise a discrete event Monte-Carlo simulation.
  • the one or more results of the numerical simulation include at least one of: a list of congestion events of the network, a size of the network, and a duration of the congestion events.
  • the one or more performance metrics include at least one of: a connectivity metric, a total number of failed links and nodes of the network during a simulation period time, a low congestion metric, a maximum congestion level occurrence of a link during the simulation period time, an average congestion area during the simulation period time, a CCDF with a congestion duration, and a network expected convergence time.
  • determining the performance of the network based on determined one or more performance metrics includes performing an analysis of the determined one or more performance metrics.
  • determining the performance of the network based on determined one or more performance metrics further includes performing a comparison of two or more network architectures based on the determined one or more performance metrics.
  • This provides the advantage of enabling the comparison among different configurable large network architectures, taking into consideration multi-objectives performance aspects.
  • the method further comprises displaying, by a display device, the determined performance of the network. Additionally or alternatively, displaying, by the display device, the comparison of the two or more network architectures.
  • the method according to the second aspect comprises the features of the corresponding implementation forms of the device of the first aspect.
  • a computer program product including a program code for carrying out, when implemented on a processor, the method according to the second aspect and its implementation forms.
  • the computer program product according to the third aspect comprises the features of the corresponding implementation forms of the method of the second aspect.
  • FIG. 1 schematically depicts a device for network performance analysis according to this disclosure
  • FIGS. 2A-2B schematically depict an exemplary implementation of the device for network performance analysis according to this disclosure
  • FIG. 3 schematically depicts a device for network performance analysis according to this disclosure
  • FIG. 4 shows an example of a comparison of a performance metric for two different network architectures
  • FIG. 5 shows an example of a comparison of another performance metric for two different network architectures
  • FIG. 6 shows an example of a comparison of a further performance metric for two different network architectures
  • FIG. 7 shows an example of a comparison of a further performance metric for two different network architectures
  • FIG. 8 shows a method for network performance analysis according to this disclosure.
  • FIG. 1 shows an exemplary embodiment of a device 100 for network performance analysis according to this disclosure.
  • the device 100 may comprise a processor or processing circuitry (not shown) configured to perform, conduct or initiate the various operations of the device 100 described herein.
  • the processing circuitry may comprise hardware and/or the processing circuitry may be controlled by software.
  • the hardware may comprise analog circuitry or digital circuitry, or both analog and digital circuitry.
  • the digital circuitry may comprise components such as application-specific integrated circuits (ASICs) , field-programmable arrays (FPGAs) , digital signal processors (DSPs) , or multi-purpose processors.
  • the device 100 may further comprise memory circuitry, which stores one or more instruction (s) that can be executed by the processor or by the processing circuitry, in particular under control of the software.
  • the memory circuitry may comprise a non-transitory storage medium storing executable software code which, when executed by the processor or the processing circuitry, causes the various operations of the device 100 to be performed.
  • the processing circuitry may comprise one or more processors and a non-transitory memory connected to the one or more processors.
  • the non-transitory memory may carry executable program code which, when executed by the one or more processors, causes the device 100 to perform, conduct or initiate the operations or methods described herein.
  • the device 100 may be implemented in, or may be part of, an orchestrator of a network.
  • the device 100 is configured to receive (or obtain) network data 101.
  • the network data 101 may comprise a schematic description of the network or a parametric description of the network.
  • the schematic description of the network may comprise one or more JSON files.
  • the device is configured to parse the received network data 101 into a data set 102, the data set 102 comprising a topology of the network.
  • the device 100 While parsing the network data 101 into the data set 102, the device 100 is configured to generate the data set 102 based on the received network data 101, and to generate a routing scheme through the network based on the received network data 101.
  • the routing scheme comprises an expected load-balancing of the network when a failure occurs.
  • the generated routing scheme may be related to a location-based clustering of one or more components of the network for which a set of routes can be generated.
  • the set of routes can be derived from an equal-cost multi-path routing (ECMP) strategy.
  • ECMP equal-cost multi-path routing
  • the routing scheme hence, may illustrate the expected network load-balancing upon a failure in a correspondent element of the network.
  • routing scheme may exploit a hierarchical structure of common telecommunication networks to cluster the network components based on their location.
  • the routing scheme may comprise one or more of the following features:
  • a type of hardware that may lead to a failure in the observed component e.g., a link can fail due to the failure in the fiber itself or in the edges ports.
  • the device 100 is configured to determine one or more performance metrics 105 of the network based on the data set 102, based on a MTBF 103 and based on a time needed to repair faults in the network 104.
  • the time needed to repair faults in the network 104 comprises a MTTR.
  • the device 100 may run a Discrete Event Simulator (DES) for one or more failure or repair events in one or more links or nodes of the network that occur over a specified period (nominally 20 years) of operation.
  • DES Discrete Event Simulator
  • the device 100 is further configured to determine the one or more performance metrics 105 of the network based on the data set 102, based on the MTBF 103 and based on the time needed to repair faults in the network 104 by performing a numerical simulation over a predefined time of operation of the network using the data set 102, the MTBF 103 and the MTTR. Further, the device is configured to determine the one or more performance metrics 105 of the network based on one or more results of the numerical simulation and based on the generated routing scheme.
  • the numerical simulation comprises sampling one or more failure occurrences in the network based on the MTBF 103. Additionally or alternatively, the numerical simulation comprises sampling one or more repair faults based on the MTTR.
  • sampling may refer to a time series generated from a distribution function that depends on one or more input features of the network.
  • Each element in the time series may represent an event that is either a failure or a repair of a component of the network.
  • a failure probability for each element (link and/or node) of the network is defined as where t is a time from the last repair event or from the beginning of the simulation. For each failure event there is a matching repair event, occurring at a MTTR period after the failure event.
  • Each sampling represents a failure or a repair event that changes the network physical topology and its load distribution.
  • the numerical simulation may assess a new state of the network in terms of the one or more performance metrics 105, e.g., whether the network remains connected, what are the current links bandwidth utilization levels, and the like.
  • the samplings are used to emulate the physical changes occurring in the network hardware over time, and the simulation evaluates the mentioned metrics for a resultant sub-network.
  • the final results are one or more cumulative measurements derived from each event.
  • the numerical simulation may enable to decouple the sampling and the evaluation (determination) of the one or more performance metrics 105. That is, the device 100 may allow to separate the mathematical modelling and the determination procedure, thus allowing a flexible assessment of different network scenarios (or architectures) and business objectives.
  • the numerical simulation may be a discrete event Monte-Carlo simulation.
  • the one or more results or output of the numerical simulation comprise at least one of a list of congestion events of the network, a size of the network, and a duration of the congestion events.
  • the one or more results of the numerical simulation may comprise a list of congestion events together with a size (number of links) , duration, and associated convergence time (if applicable) of the network.
  • the one or more performance metrics 105 comprise at least one of: a connectivity metric, a total number of failed links and nodes of the network during a simulation period time, a low congestion metric, a maximum congestion level occurrence of a link during the simulation period time, an average congestion area during the simulation period time, a CCDF with a congestion duration, and a network expected convergence time.
  • the device 100 may calculate two sets of metrics for any network topology, wherein the two sets of metrics may comprise physical network (protocol excluded) metrics and logical network (protocol included) metrics.
  • the physical network metrics may comprise, for example and not as a limitation, one or more of:
  • the logical network metrics may comprise, for example and not as a limitation, one or more of:
  • the maximum congestion level (in percentage) occurrence for one or more links during the simulation period time may be determined.
  • a maximum congestion extent (number of simultaneously congested links) during the simulation period time may be determined.
  • an average congestion extent (number of simultaneously congested links) during the simulation period time may be determined.
  • the CCDF with congestion duration of the topology suffering from a congestion area (congestion link number) greater than a stated congestion area may be determined.
  • routing scheme may be used during the determination of the low congestion metric 105 and the network expected convergence time over the course of the simulation period time metric 105.
  • the device 100 of FIG. 1 is configured to determine a performance of the network 106 based on the one or more performance metrics 105.
  • the determination of the performance of the network 106 based on the one or more performance metrics 105 comprises performing, by the device 100, an analysis of the determined one or more performance metrics 105.
  • the analysis may be, for example, a statistical analysis of the one or more performance metrics 105.
  • the device 100 may be further configured to generate one or more appropriate graphs, each graph illustrating the determined one or more performance metrics 105. Additionally or alternatively, the device 100 may be further configured to generate one or more appropriate graphs, each graph illustrating the determined performance of the network 106.
  • the device 100 may calculate specific reliability-oriented performance metrics 105 for a certain network topology.
  • the determination of the performance of the network 106 based on the one or more performance metrics 105 further comprises performing a comparison of two or more network architectures based on the determined one or more performance metrics 105.
  • the device 100 may determine a respective set of performance metrics, each set comprising the one or more performance metrics 105. The device 100 may then compare the determined two or more sets of performance metrics, thereby comparing the performance of the respective two or more network architectures.
  • the device 100 may use a four-stage mechanism to provide a comprehensive statistical analysis of the performance of the network.
  • An exemplary implementation of the above-disclosed four stages of the device 100 is depicted in FIGS. 2A and 2B.
  • same elements are labelled with the same reference signs.
  • the left side of FIG. 2A depicts an example of the network data 101 received by the device 100.
  • the device 100 may generate the data set 102 by, for example and not as a limitation, converting the network topology into a directed multi-graph, as shown in the right part of FIG. 2A.
  • FIG. 2B depicts an exemplary numerical simulation 208 performed by the device 100, for example the discrete event Monte-Carlo simulation, which integrates the real specifications of the components of the network, the time to repair faults, i.e., the MTBF 103, and the MTTR 207. Further, the numerical simulation 208 takes into account the aging of the network.
  • the discrete event Monte-Carlo simulation which integrates the real specifications of the components of the network, the time to repair faults, i.e., the MTBF 103, and the MTTR 207.
  • the numerical simulation 208 takes into account the aging of the network.
  • FIG. 2B depicts the determined performance of the network 106, by showing two exemplary comparisons 209-1, 209-2 of two network architectures, each comparison 209-1, 209-2 being based on a different performance metric 105.
  • FIG. 3 shows an exemplary embodiment of the device 100 for network performance analysis according to this disclosure, which builds on the device 100 of FIG. 1. Same elements are labelled with the same reference signs.
  • the device 100 further comprises a display device 310 configured to display the determined performance of the network 106, additionally or alternatively the comparison 209 of the two or more network architectures.
  • the device 100 of FIG. 1 and FIG. 3 can be used to compare, for example and not as a limitation, two backbone network configurations (or network architectures) .
  • a first backbone configuration may comprise a large centralized switch, referred to as "Big-boxes" configuration
  • a second backbone configuration may comprise of a set of small switch boxes, referred to as "Spine-Leaf” configuration.
  • the analysis of the network performance for each network configuration determined by the device 100 proves and showcases an advantage of the Big-Box configuration in contrast with the Spine-Leaf configuration regarding reliability.
  • FIG. 4 shows an exemplary comparison 209 of a performance metric 105 determined by the device 100 for the Big-Box architecture and for the Spine-Leaf architecture. From FIG. 4, it can be concluded that for every case of bandwidth utilization, the maximum number of affected links upon a congestion occurrence in the Big–Box configuration is almost negligible while in the Spin-Leaf configuration there are events in which hundreds of links are impaired. These results may be crucial for the network, since a higher number of congested links can be related to a higher number of impaired services.
  • FIG. 5 shows an exemplary comparison 209 of the above-disclosed logical performance metric b) as determined by the device 100 for the Big-Box architecture and for the Spine-Leaf architecture.
  • the exemplary comparison 209 of FIG. 5 reveals that for every case of bandwidth utilization, the maximum congestion of a link is significantly smaller in the Big–Box configuration than in the Spine-Leaf configuration.
  • comparisons 209 are shown in FIGs. 6 and 7.
  • FIG. 6 an exemplary comparison 209 of the above-disclosed logical performance metric d) determined by the device 100 for the Big-Box architecture and for the Spine-Leaf architecture is depicted.
  • FIG. 7 shows an exemplary comparison 209 of the above-disclosed logical performance metric e) determined by the device 100 for both the Big-Box architecture and the Spine-Leaf architecture.
  • the device 100 of the embodiments of FIG. 1 and FIG. 3 may allow a real time (of the order of seconds to a few minutes) and an effective assessment of load-driven network performance metrics, including the convergence time. This further may provide with location-awareness routing scheme generation. Further, the device 100 may support the performance analysis of very large networks (for example but not restricted to networks comprising up to 500,000 links and 20,000 nodes) .
  • the device 100 may be applied to any type of telecommunication network (wired, wireless) .
  • the device 100 can be adapted to compute and compare reliability of different industrial production lines that are based on MTBF and MTTR.
  • FIG. 8 shows an exemplary embodiment of a method 800 for network performance analysis.
  • the method 800 may be performed by the exemplary embodiments of the device 100 of FIG. 1 and FIG. 3 as disclosed above.
  • the method 800 comprises a step 801 of receiving network data 101.
  • the method 800 comprises a step 802 of parsing the received network data 101 into a data set 102.
  • the data set 102 comprises a topology of the network.
  • the method 800 comprises determining one or more performance metrics 105 of the network based on the data set 102, based on a mean time between failures, MTBF 103, and based on a time needed to repair faults in the network 104.
  • the method 800 further comprises a step 804 of determining a performance of the network 106 based on the one or more performance metrics 105.
  • the method 800 may further comprise actions according to the exemplary embodiments of the device 100 of FIG. 1 and FIG. 3. Hence, the method 800 achieves the same advantages as the device 100 disclosed above.
  • This disclosure further provides a computer program product comprising a program code for carrying out, when implemented on a processor, the method 800.
  • the computer program may be included in a computer readable medium of the computer program product.
  • the computer readable medium may comprise essentially any memory, such as a ROM (Read-Only Memory) , a PROM (Programmable Read-Only Memory) , a 15 EPROM (Erasable PROM) , a Flash memory, an EEPROM (Electrically Erasable PROM) , or a hard disk drive.

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Environmental & Geological Engineering (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

L'invention concerne un dispositif et un procédé d'analyse de performance de réseau. Le dispositif est configuré pour recevoir des données de réseau. Ensuite, le dispositif analyse les données de réseau reçues en un ensemble de données, l'ensemble de données comprenant une topologie du réseau. Le dispositif est en outre configuré pour déterminer une ou plusieurs métriques de performance du réseau sur la base de l'ensemble de données, sur la base d'un temps moyen entre des défaillances (MTBF) et sur la base d'un temps nécessaire pour réparer des défaillances dans le réseau. En outre, le dispositif est configuré pour déterminer une performance du réseau sur la base de la ou des métriques de performance.
PCT/CN2023/098935 2023-06-07 2023-06-07 Dispositif et procédé d'analyse de performance de réseau Ceased WO2024250199A1 (fr)

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CN202380099141.0A CN121264012A (zh) 2023-06-07 2023-06-07 用于网络性能分析的设备和方法
PCT/CN2023/098935 WO2024250199A1 (fr) 2023-06-07 2023-06-07 Dispositif et procédé d'analyse de performance de réseau

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PCT/CN2023/098935 WO2024250199A1 (fr) 2023-06-07 2023-06-07 Dispositif et procédé d'analyse de performance de réseau

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015091784A1 (fr) * 2013-12-19 2015-06-25 Bae Systems Plc Surveillance des performances de communications de données
WO2018049649A1 (fr) * 2016-09-18 2018-03-22 华为技术有限公司 Procédé et dispositif de mesure de performance de réseau
CN108494625A (zh) * 2018-03-21 2018-09-04 上海精鲲计算机科技有限公司 一种网络性能分析系统
US20220138081A1 (en) * 2020-11-02 2022-05-05 Innovate5G, Inc. Systems and Methods for Optimization of Application Performance on a Telecommunications Network
CN115460482A (zh) * 2021-06-09 2022-12-09 中国联合网络通信集团有限公司 网络性能预测方法和装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7426554B2 (en) * 2002-03-06 2008-09-16 Sun Microsystems, Inc. System and method for determining availability of an arbitrary network configuration
KR100450415B1 (ko) * 2002-11-15 2004-09-30 한국전자통신연구원 가용도를 이용한 망 관리방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015091784A1 (fr) * 2013-12-19 2015-06-25 Bae Systems Plc Surveillance des performances de communications de données
WO2018049649A1 (fr) * 2016-09-18 2018-03-22 华为技术有限公司 Procédé et dispositif de mesure de performance de réseau
CN108494625A (zh) * 2018-03-21 2018-09-04 上海精鲲计算机科技有限公司 一种网络性能分析系统
US20220138081A1 (en) * 2020-11-02 2022-05-05 Innovate5G, Inc. Systems and Methods for Optimization of Application Performance on a Telecommunications Network
CN115460482A (zh) * 2021-06-09 2022-12-09 中国联合网络通信集团有限公司 网络性能预测方法和装置

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