WO2003039070A2 - Procede et dispositif d'analyse de la robustesse d'un reseau - Google Patents

Procede et dispositif d'analyse de la robustesse d'un reseau Download PDF

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Publication number
WO2003039070A2
WO2003039070A2 PCT/GB2002/005029 GB0205029W WO03039070A2 WO 2003039070 A2 WO2003039070 A2 WO 2003039070A2 GB 0205029 W GB0205029 W GB 0205029W WO 03039070 A2 WO03039070 A2 WO 03039070A2
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WIPO (PCT)
Prior art keywords
network
node failure
performance
simulations
nodes
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Application number
PCT/GB2002/005029
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English (en)
Other versions
WO2003039070A3 (fr
Inventor
Fabrice Tristan Pierre Saffre
Robert Alan Ghanea-Hercock
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British Telecommunications Public Limited Company
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Application filed by British Telecommunications Public Limited Company filed Critical British Telecommunications Public Limited Company
Publication of WO2003039070A2 publication Critical patent/WO2003039070A2/fr
Publication of WO2003039070A3 publication Critical patent/WO2003039070A3/fr

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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
    • H04L41/14—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
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H04L41/0645—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis by additionally acting on or stimulating the network after receiving notifications
    • 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]

Definitions

  • the present invention relates to analysis of the structure of networks.
  • the invention related to assessing the robustness of a network topology when exposed to cumulative node failure.
  • Such node failure may result from a node going out of service as a result of maintenance or a directed attack.
  • a node may go out of service because it is out of range of nodes that it was previously in communication with.
  • apparatus for determining the response of a network to node failure comprising: means for inputting a representation of a network; means for measuring the performance of the network in simulations of node failure; and means for comparing the performance of the network in simulations to one or more models of network response to node failure.
  • Embodiments of the present invention provide a network analyser that quantifies a complex networks' behaviour when submitted to cumulative node failure.
  • the analyser tests the robustness of any given network topology in an automated fashion, computing the values for a set of global variables after performing a statistical analysis of simulation results. Those variables characterise the decay of the network's largest component and effectively summarise the system's resilience to stress.
  • the analyser provides a user-friendly interface to specify key simulation parameters and a graphical representation of the results. The results are also made available as text files.
  • Figure 1 is a representation of the topology of a network
  • Figure 2 is a representation of the topology of the network of figure 1 after being subjected to cumulative node failure
  • Figure 3 is a flow diagram illustrating the analysis method used by the analysis apparatus according to an embodiment of the present invention.
  • Figure 4a & 4b are graphs illustrating specific steps in the analysis illustrated in figure 3;
  • Figure 5 is an annotated screen shot of the graphical user interface (GUI) of the analysis apparatus;
  • GUI graphical user interface
  • Figures 6 and 9 to 12 are screen shots of the display by the analysis apparatus of the results of its analysis
  • Figure 7 is a graph representing features of the network whose analysis results are shown in figure 6;
  • Figure 8 is a further screen shot of the GUI showing the inputs used to generate the analysis shown in figure 9.
  • a network 101 is made up of a number of nodes 103 interconnected by links 105 (Nodes 103 may also be referred to as vertices and links 105 as edges).
  • Nodes 103 may also be referred to as vertices and links 105 as edges).
  • S the relative size of the largest intact component to the total number of nodes.
  • Figure 2 illustrates the same network 101 after cumulative node failure. This node failure has resulted in 50% or less of the remaining nodes (i.e. the nodes left after the failed nodes have been removed) are still connected together in the largest component.
  • the other 50% of the nodes are attached in smaller groups or not attached at all.
  • a network may have one hundred nodes of which five fail leaving 95 nodes in the network.
  • S For a relatively resilient network topology this could result in a value of S of 0.80.
  • 80% or 76 of the remaining 95 nodes would still be connected together.
  • S For a relatively brittle network topology this could result in a value of S of 0.20. In other words, 20% or 19 of the remaining 95 nodes would still be connected together.
  • the decay of the average relative size of the largest component ⁇ S> of a given network can be modelled using one of two basic non-linear equations.
  • the first equation performs better when modelling networks with relatively resilient topology and has the form:
  • Equations [1a] and [1b] obey a very similar logic and are relative efficient in describing the network's behaviour under cumulative node failure. They can be used to discriminate between 2 qualitatively different categories of architecture. Since expression [1a] or [1b] give an approximation of the decay of a given network's largest component, then the corresponding X and ⁇ global variables are a suitable measurement for quantifying its resilience to cumulative node failure.
  • a further useful indicator derived from an adjusted value of X is X c .
  • This is defined as the value of x for which the average relative size ( ⁇ S>) of the largest component is equal to 0.5.
  • X c is the critical fraction of "missing" nodes above which, on average, less than 50% of the surviving nodes are still interconnected.
  • the value of ⁇ provides an approximation of the slope of the curve around the critical value X c .
  • X c is defined for networks described by equation [1a] as:
  • FIG 3 is a flow diagram illustrating the analysis method carried out by a computer program embodying the present invention running on a general purpose computer.
  • the program provides an analysis apparatus that takes as input a description of the topology of the network to be analysed.
  • the topology is described in a text file that lists the total number of nodes, the total number of links and paired node identification numbers (IDs) thereby specifying which nodes are directly connected to which other nodes.
  • IDs paired node identification numbers
  • GUI graphical user interface
  • the GUI 501 comprises a number of user definable fields, a check box and two buttons in addition to the standard Windows TM control buttons.
  • the #Sims box 503 enables the user to determine how many simulations should be performed on the supplied topology data.
  • the Sample box 505 enables the user to determine how many points there should be during each simulation where the effect of node losses should be calculated i.e. S measured.
  • the File box 507 enables the user to define the file in which the topology of the network to be analysed is stored.
  • the Seed box 509 is used to define a number that is used by the analyser to initialise its random number generator.
  • the Attack check box 511 enables the user to choose between a random node failure simulation or a directed attack simulation.
  • the Start button 513 begins the simulation process while the Exit button 515 closes the program.
  • the analyser creates two separate text files, bearing the same name as the original topology file, but with different extensions.
  • One is contains the values for the global variables and a measurement of fitting quality (r 2 ) and has a ".gvr” suffix.
  • the other contains a table of numerical values as shown in table 2 below and has a ".rst" suffix.
  • the first column of table 2 contains the fraction of nodes that have failed, the second contains the corresponding average relative size ⁇ S> of the largest component, and the third is the standard deviation for S.
  • the fourth column is the value of ⁇ S> as predicted by expression [1a], and the fifth is the value of ⁇ S> as predicted by [1b].
  • step 301 the program is initiated and extracts the topology data from the topology file described above. Processing then moves to step 303 at which the topology data is used to simulate network decay either by random node loss or by directed attack as determined by the user via the GUI as noted above.
  • the random node loss is simulated by the system randomly choosing one or more nodes from the supplied topology and removing it from the network. This is repeated until all nodes have been removed.
  • the number simulations that are carried out and the number of nodes that are removed at each iteration can be varied by the user via the GUI which is described in further detail below.
  • the size S of the largest component of the remaining network is calculated by known methods and stored.
  • the average value between simulations (where there is more than one) of S is calculated along with its standard deviation SDEV and stored in the manner noted above with reference to table 2.
  • Figure 4a is a graph of ⁇ S> (relative size of the largest component) derived from the simulations plotted against x (proportion of original number of nodes removed).
  • ⁇ S> relative size of the largest component
  • step 311 a fitting function is used to compare the results of the calculations of S using expressions [1a] & [1b] against the empirical results for S from the simulations carried out in step 303.
  • the fitting function gives a measure (r 2 ) for each of the curves derived from expressions [1a] & [1b] relative to the curve from the simulation.
  • the analyser displays the data it has calculated. An example display is shown in figure 6.
  • the results window 601 is displayed which includes values for all global variables and a graph showing simulation data (average S +/- standard deviation) as well as the results from each of the expressions [1a] & [1b] (referred to in the window 601 as Option 1 and Option 2).
  • the analyser is arranged to remove nodes using a "best guess" strategy in the simulation carried out at step 303 of figure 3.
  • This strategy emulates an attacker's strategy where the attacker possesses partial information about network topology which is used to chose which node to target next.
  • This strategy is modelled by attributing to each surviving node a probability of being selected that is linearly proportional to its degree k (i.e. the number of links it has to other nodes):
  • the analyser recalculates P,- after each attack in order to take into account the changing probability distribution caused by the elimination of one of the nodes. This increased complexity means that testing a network's resilience for directed attack is more intensive and time consuming than for random failure.
  • the "Attack” scenario because of its stochastic nature, can also be used to model special forms of accidental damage where connectivity level is involved. For example, in a network where congestion is a cause for node failure, key relays (highly connected nodes) are more likely to suffer breakdown, which can be modelled using expression [5].
  • the example network is a relatively large 3000 nodes system.
  • the cheapest way to have them all such nodes interconnected involves 2999 links. They could all be arranged in a single "star” or in a closed “loop", but more realistic architectures would involve inter-connected sub-domains of different size and/or topology.
  • the network used for this example is a scale-free network of the appropriate size (3000 nodes, one link per node except the first one) to use as the basic blueprint.
  • Figure 7 indicates that the example network's topology is scale-free (power law relationship between node frequency and degree).
  • the most highly connected node has a direct link with 45 other nodes, 9 "secondary hubs" have more than 20 connections, and 28 have between 10 and 20 direct "affiliates”.
  • the network designer can use the analyser to compute statistics about its resilience to node failure, in terms of the cohesion of its largest component (initially including all nodes).
  • the GUI entries to provide this are shown in figure 8.
  • the analyser displays the results window as shown in figure 9 from which it can be seen that the r 2 value for Option 2 correlates closest to the simulation results indicating that expression [1a] models the network best.
  • the analyser shows that, on average, removing only about 14% of all nodes (equivalent to severing all their links) is enough to reduce the size of the largest component to 50% of the surviving population (X c ⁇ 0.14).
  • the analyser tells the designer that if 500 nodes out of 3000 are malfunctioning, chances are the largest sub-set of relays that are still interconnected contains less than a half of the 2500 surviving nodes. In other words, it is likely that in this situation, around 1250 operational nodes are cut from (and unable to exchange any information with) the core of the network.
  • a straightforward way of increasing robustness is to add at least some backup links, so that alternative routes are available between nodes in case the primary (presumably most efficient) path becomes unavailable due to node failure(s).
  • the designer could want to test the influence of doubling the total number of connections (raising it to 5999 links).
  • Doubling the number of links may however be an unacceptable solution because of financial considerations.
  • the network designer may look for alternative ways of improving robustness, perhaps by testing the benefit of partial route redundancy. Again, the analyser would allow the making of projections on the basis of another blueprint. For example, if only 1000 extra-connections are added to the original topology, bringing it to 3999. The results of this are shown in figure 12. As can be seen, the robustness is not increased in the same proportion as before. However, even though 33% extra links were created instead of 100%, the critical size X c is shifted to -0.44. In other words, the modified network is three times more robust on this measure relative to the original blueprint. Since the doubling of the number of connections described above only results in the robustness increasing four times then the second choice may be more cost-effective.
  • the apparatus described above is a combined simulation and analysis tool designed to study topological robustness. It does not take into account other critical aspects of network operation like traffic or routing management. Its purpose is to provide a suitable way of estimating the speed and profile of the largest component's decay under cumulative node failure, a necessary step in assessing a system's ability to withstand damage.
  • the apparatus that embodies the invention could be a general purpose device having software arranged to provide the an embodiment of the invention.
  • the device could be a single device or a group of devices and the software could be a single program or a set of programs.
  • any or all of the software used to implement the invention can be contained on various transmission and/or storage mediums such as a floppy disc, CD-ROM, or magnetic tape so that the program can be loaded onto one or more general purpose devices or could be downloaded over a network using a suitable transmission medium.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Human Computer Interaction (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

L'invention concerne un procédé et un dispositif permettant de déterminer la robustesse d'un réseau contre les pannes de noeuds cumulées. Le système prend comme entrée une description de la topologie du réseau, simule des pannes de noeuds cumulées et produit un modèle de robustesse du réseau pouvant servir de mesure relative par rapport à des topologies réseau modifiées ou d'autres topologies réseau.
PCT/GB2002/005029 2001-11-01 2002-11-01 Procede et dispositif d'analyse de la robustesse d'un reseau WO2003039070A2 (fr)

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EP01309304.2 2001-11-01
EP01309304 2001-11-01

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WO2003039070A2 true WO2003039070A2 (fr) 2003-05-08
WO2003039070A3 WO2003039070A3 (fr) 2003-08-14

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1624397A1 (fr) * 2004-08-02 2006-02-08 Microsoft Corporation Validation et calibrage automatique d'un modèle de performance fondé sur des transactions
CN100465918C (zh) * 2004-08-02 2009-03-04 微软公司 基于事务处理的性能模型的自动化确认和校准***和方法
US7797425B2 (en) 2005-12-22 2010-09-14 Amdocs Systems Limited Method, system and apparatus for communications circuit design
US8018860B1 (en) * 2003-03-12 2011-09-13 Sprint Communications Company L.P. Network maintenance simulator with path re-route prediction
US20180351814A1 (en) * 2015-03-23 2018-12-06 Utopus Insights, Inc. Network management based on assessment of topological robustness and criticality of assets
CN112350312A (zh) * 2020-10-29 2021-02-09 广东稳峰电力科技有限公司 电力线路鲁棒性分析方法及装置
US20230214304A1 (en) * 2021-12-30 2023-07-06 Juniper Networks, Inc. Dynamic prediction of system resource requirement of network software in a live network using data driven models
US11855866B1 (en) 2022-09-29 2023-12-26 The Mitre Corporation Systems and methods for assessing a computing network's physical robustness

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KANT L ET AL: "Modeling and simulation study of the survivability performance of ATM-based restoration strategies for the next generation high-speed networks" COMPUTER COMMUNICATIONS AND NETWORKS, 1999. PROCEEDINGS. EIGHT INTERNATIONAL CONFERENCE ON BOSTON, MA, USA 11-13 OCT. 1999, PISCATAWAY, NJ, USA,IEEE, US, 11 October 1999 (1999-10-11), pages 469-473, XP010359621 ISBN: 0-7803-5794-9 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8018860B1 (en) * 2003-03-12 2011-09-13 Sprint Communications Company L.P. Network maintenance simulator with path re-route prediction
EP1624397A1 (fr) * 2004-08-02 2006-02-08 Microsoft Corporation Validation et calibrage automatique d'un modèle de performance fondé sur des transactions
CN100465918C (zh) * 2004-08-02 2009-03-04 微软公司 基于事务处理的性能模型的自动化确认和校准***和方法
US7797425B2 (en) 2005-12-22 2010-09-14 Amdocs Systems Limited Method, system and apparatus for communications circuit design
US20180351814A1 (en) * 2015-03-23 2018-12-06 Utopus Insights, Inc. Network management based on assessment of topological robustness and criticality of assets
US10778529B2 (en) * 2015-03-23 2020-09-15 Utopus Insights, Inc. Network management based on assessment of topological robustness and criticality of assets
US11552854B2 (en) 2015-03-23 2023-01-10 Utopus Insights, Inc. Network management based on assessment of topological robustness and criticality of assets
CN112350312A (zh) * 2020-10-29 2021-02-09 广东稳峰电力科技有限公司 电力线路鲁棒性分析方法及装置
CN112350312B (zh) * 2020-10-29 2022-10-04 广东稳峰电力科技有限公司 电力线路鲁棒性分析方法及装置
US20230214304A1 (en) * 2021-12-30 2023-07-06 Juniper Networks, Inc. Dynamic prediction of system resource requirement of network software in a live network using data driven models
US11797408B2 (en) * 2021-12-30 2023-10-24 Juniper Networks, Inc. Dynamic prediction of system resource requirement of network software in a live network using data driven models
US11855866B1 (en) 2022-09-29 2023-12-26 The Mitre Corporation Systems and methods for assessing a computing network's physical robustness

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