WO2003043264A2 - Dispositif et procede d'analyse resau a prediction autonome - Google Patents
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- WO2003043264A2 WO2003043264A2 PCT/FR2002/003825 FR0203825W WO03043264A2 WO 2003043264 A2 WO2003043264 A2 WO 2003043264A2 FR 0203825 W FR0203825 W FR 0203825W WO 03043264 A2 WO03043264 A2 WO 03043264A2
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000003012 network analysis Methods 0.000 title description 4
- 239000000654 additive Substances 0.000 claims abstract description 13
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Classifications
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- 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
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5003—Managing SLA; Interaction between SLA and QoS
- H04L41/5019—Ensuring fulfilment of SLA
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- 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
- H04L41/085—Retrieval of network configuration; Tracking network configuration history
- H04L41/0853—Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information
- H04L41/0856—Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information by backing up or archiving configuration information
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- 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
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- 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
- H04L41/147—Network analysis or design for predicting network behaviour
Definitions
- the invention relates to the monitoring and simulation of complex systems.
- [1] proposes a formula for evaluating the throughput of a source controlled by TCP as a function of the probability of packet loss.
- [2] proposes a representation in so-called "MAX-PLUS” algebra of complex systems, such as communication networks, and in particular flow and congestion control.
- This "MAX-PLUS” algebra makes it possible to integrate the random nature of the parameters of the networks, while considering a plurality of nodes.
- [2] takes into account only one source using a protocol of the TCP type.
- [3] and [4] is proposed an elementary model allowing to apprehend the joint evolution of the rates of a set of TCP sources sharing a common router.
- the proposed additive growth and multiplicative decrease (AIMD) model makes it possible to assess the performance degradation due to loss synchronization in the shared router.
- AIMD additive growth and multiplicative decrease
- one parameter remains unknown: the probability function of this synchronization.
- the models offered are limited to the representation, either of several routers and a single controlled source, or of a single router shared between several sources controlled by TCP.
- the monitoring and simulation systems of these networks controlled by TCP are not autonomous when predicting the throughput obtained by the sources. That is to say that they cannot get rid of the need for physical observations made on a real network such as for example that of the probability of losses in [1] or [2], or the law of synchronizations in [3] or [4]; in turn, these can hardly cover all possible cases with a reasonable degree of reliability, especially on a wide area network.
- the invention improves the situation.
- the invention relates to a method for testing and predicting the behavior of a computer network, comprising the following steps: a. memorize on the one hand a representation of the network, comprising routers, their own transmission properties, and transit times between routers, on the other hand a configuration of use of the network comprising traffic classes, for each of these classes is associated with a number of sources and a path through the routers, b. from the initial conditions chosen, repeatedly apply a traffic evolution model, of the additive growth and multiplicative decay type, to simulate an evolution of flows in the network, by memorizing each time a set of flow variables of classes or sources, and c. if repeating step b. produces a periodic orbit, returning appreciably to a set of flow variables of the classes or sources already encountered, examine the series of routers encountered, as responsible for the losses to evaluate the flow obtained by each class or source.
- the invention also relates to a device for testing and predicting the behavior of a computer network.
- the device comprises - a memory for storing: * network parameters including routers, their own transmission properties, and transit times between routers,
- network usage configuration parameters including traffic classes, each of these classes associated with a number of sources and a route through the routers,
- FIG. 1 illustrates a computer device comprising a processing module according to the invention
- FIG. 2 illustrates a computer network made up of routers shared between a set of sessions
- FIG. 3 represents a flowchart of the simulation method for a network analysis according to the invention
- FIG. 4 shows the graph of the evolution of the average speed in a router belonging to the path of a class.
- Appendix A includes the network parameters, source parameters, and model variables to which the description below refers.
- Annex B includes steps for calculating algorithms linked to a model to which the description below refers.
- Appendix C includes estimates of the synchronization rate under certain assumptions.
- Annex D contains the mathematical formula to which the description below refers.
- v [j] designates an array or vector variable ("array") having a value for each value of j.
- array an array or vector variable having a value for each value of j.
- FIG. 1 illustrates a computer environment comprising a central unit 1 connected to a screen 7 and input means 6 such as a keyboard or a mouse.
- the central unit 1 is also connected to a GUI graphics card 2 adapted to control the display of data on the screen 7.
- the central unit 1 is suitable for working in relation to the processing module 3 connected to memory 4.
- Memory 4 stores data linked to the network representation 8 and data 9 linked to the configuration of use of the network. These data will be described more fully below.
- the memory 4 includes a calculation module 5 which works in correspondence with the processing module 3.
- This processing module 3 is capable of repeatedly applying a traffic evolution model, of the additive growth and multiplicative decrease type, to simulate an evolution throughputs in the routers of the network.
- the processing module is specific to request a memorization of a set of flow variables in the network so as to predict the next epoch of congestion and to remedy the expected congestion.
- the description relates in particular to the prediction of the performance of flows, for example of the TCP type, and the quantity of service (QoS) in a multi-router topology.
- the device and the method of the invention are used inter alia when a large number of sources controlled by a protocol, of the TCP type for example, share several routers.
- This simulation is based on a fluid description of the additive growth and multiplicative decrease (AIMD) type model.
- AIMD additive growth and multiplicative decrease
- FIG. 2 illustrates a computer network of the type used in the invention.
- the network consists of several routers rO, ri, r2, r3 and r4, the router rO being an access router.
- the routers are linked together by links such as Tl connects rO to r3, T2 connects rO to r2, T3 connects r3 to r4, T4 connects rO to r4, T5 connects r3 to r4, T6 connects rO to ri.
- Sources 1, 2, 3 referenced by II, 12, 13 and destinations 1, 2, 3 referenced by Dl, D2, D3 are represented in FIG. 2.
- the sources are connected to the network by the access router rO.
- the destinations are linked to the network by an access router r4, r2, r3 respectively.
- a class "type" corresponds to the set of classes defining the same path and or the same end-to-end path of a class (appendix A2-3).
- a class "type" corresponds to the set of classes defining the same path and or the same end-to-end path of a class (appendix A2-3).
- For a connecting journey one of the sources S to D3, at least two types of class are again defined, the type of class defining the path (T5) and the type of class defining the path (T4, T3) and passing through the intermediate router r4.
- a class is defined by a path, a type of session, propagation delays and a number of sources.
- the classes are designated by the variable s and the routers are designated by the variable r.
- the same source can be designated as being a source of class s and a source of class s 'if the classes s and s' each define a path going from the same source to the same destination as previously illustrated but having a different session.
- there are several sources for the same class that is to say several sources whose path is identical to reach their destination.
- Congestion time n designates an instant n at which the flows of each class s are calculated (flows equal to the flow of each source i of class s). This "congestion time n” also designates an instant for which a network router is said to be “congested” or in “congestion state", that is to say a router which will have one or more packet losses.
- the network parameters designate the parameters of the routers defined in Al-1, Al-2, Al-3, Al-4, Al-5.
- the routers can be of different types such as for example: - of the FIFO (Fird In First Out) or FQ (Fair Queing) type designating types of routers liable to lose packets in queue overflow, also called Tail Drop,
- AIMD additive growth and multiplicative decrease
- FIG. 3 illustrates an exemplary embodiment of a simulation method for a network analysis according to the invention.
- the process refers to appendices A, B, C and D related to the AIMD traffic evolution model proposed as an example.
- each element of the matrix p [s, r] designates a probability of synchronization of packet losses, called synchronization rate defined between 0 and 1 which is here assumed to be predefined according to Bernoulli's law.
- the value of the random variable gamma [s, r] equal to 0.5 signifies a probability law of the "head or toe" type, - in BO-2, each element of the vector c [r] designates the capacity (in packets per second) that a source could have from a router if the total capacity of this router was ideally shared between the sources using this router r,
- each element of the matrix a [s, r] designates a proportion of the number of sources of class s on the number of sources using the router r, - in BO-4, each element of the vector m-rtt [r] denotes the sum over s of a [s, r] each divided by their minimum round trip time of the class considered squared,
- each element of the matrix g [s, r] designates an integer between 0 and 1 calculated as a function of the synchronization rate
- the initialization phase consists in initializing the average flow rates for all classes and for all periods of congestion n varying from 1 to N (n and N being integers), with the function f () given describing the initial condition.
- tau_l [r] of step 1, defined in appendix B0, must be positive or zero. This means that the initial charge must be compatible with the capacities of the network.
- the formulation B0-1 is predefined in the case where the following assumption is made: - the routers have a buffer memory of zero size.
- the type of router used is of FIFO type and in A2-2, the type of session is of FTP type.
- Steps 410, 420, 430 represent the iteration of appendix B1-0 on each time of congestion of steps 1, 2 and 3 defined below.
- V step 1 in annex Bl-1 defines, for each router r at a time of congestion n, a sum of the class source bit rates over all the classes according to the time of congestion n-1.
- som_n [r] represents the total load (or bit rate) on the router 'r' at the end of the previous iteration (during the first iteration, som_l [r] represents the total load on the router 'r' defined by the initial condition).
- the calculation of tau -n [r] defines a time between the given time of congestion n-1 and the consecutive time of congestion n, called virtual inter-congestion time for each router r.
- tau_n [r] is the virtual duration of the inter-congestion of router V which would be effective if for example all the other routers were of infinite capacity (c [r]).
- U step 2 of annex Bl-2 makes it possible to determine the minimum inter-congestion time over the set of virtual inter-congestion times of the routers r, also called inter-congestion time of the network tau_n.
- This minimum inter-congestion time designates the time between the old time of congestion and the current time of congestion.
- the value of tau_n gives the n-th inter-congestion duration of the network.
- step 420 a processing of average flow rates is carried out for each class s.
- step 2 of appendix B1-2 it is calculated in (1) the average current flow x_n [s] of each class s at the n th epoch of congestion.
- (1) applies the linear growth mechanism (AI developed in the documents presenting the AIMD model) to all sources.
- the absolute date of the nth network congestion is given by the time value j n.
- n th router or congested router at time n whose virtual inter-congestion time is equal to the inter-loss time of the network and which belongs to the class path (also called end-to-end path of the class)
- the new average flow x_n [s] of each class s defined at the time of congestion n This new calculated average speed x_n [s] is lower than the previous average speed to avoid congestion, packet loss or other at the level of the congested router (s) at the time of congestion n.
- the new average bit rate x__n [s] is calculated based on the synchronization rate and the corresponding old average bit rate.
- (2) of appendix Bl-3 applies to the sources crossing the congested router the mechanism of multiplicative decrease (MD) of the mechanism AIMD on average.
- step 430 it is checked that the calculated flows correspond to a state of the flows already encountered beforehand or that the number of iterations of steps 410 and 420 reaches a determined number of iterations (number defined by the variable Max_iter of Y step 0 in Bl-0). In the case of a negative response, the iteration of steps 410 and 420 continues to determine the next minimum inter-virtual time of the routers and the corresponding bit rates.
- This iteration of steps 410 and 420 illustrates the mathematical formula of appendix D comprising sums on the classes for calculating the inter-congestion time of a router, a minimum to be found among the inter-congestion times of the routers and these operations are repeating for each time of congestion.
- the average flow rates are calculated using the synchronization matrix represented by ⁇ n + 1 .
- the vector recurrence equation has a solution (under certain assumptions) which is a periodic orbit in step 440.
- the theory guarantees the uniqueness and the existence of 'such an orbit finished. Indeed, if congestions (causing losses) occur very often on each router, a single solution exists to the equation of appendix D which has a finite period n independent of the initial conditions of the vector x_n []. This solution is defined as a so-called periodic orbit because it would be found in the case of a new cycle of iterations.
- This orbit is characterized by a finite sequence of type B 1 -4 in which r_n is the sequence of routers where congestion has taken place (causing the loss or loss of packets), x_n [s] is the sequence of average bit rates of class s, tau_n is the sequence of inter-congestion times of the network, the sequences being defined at n times of congestion.
- This discrete time orbit completely defines the continuous trajectory by linearization of the flow rates defined in annex Bl-5. It defines the 'skeleton' of the instant flow process.
- this orbit can be determined using a traffic evolution model other than the additive growth and multiplicative decrease type model.
- step 450 the average flow rates are analyzed.
- instantaneous flows are calculated by a stochastic approach if the number of sources per class is high (SN).
- the instantaneous flow rates are calculated from the sequences of average flow rates obtained.
- the instantaneous rates X_n [s, i] are calculated according to formula B2-1 in which the inter-congestion time of the network at time n is added to the previous instantaneous rate X_ ⁇ n-1 ⁇ [s, i] and this for each class s and for each source i.
- the new instantaneous speed X_n [s, i] is calculated according to the formula B2-2.
- the random variable gamma [s, r] corresponds to the ratio of the flow rates X_n [s, i] just after and before congestion.
- Max_iter When a periodic orbit is found before a maximum number of iterations, Max_iter, the results on the steady state result from the exploitation of the orbit thus obtained. Results on the transient regime (for example the time required to reach the steady state) can also be obtained. If the iteration stops when a maximum number of iterations is reached, Max_iter, without a periodic orbit being found, it is visually possible to observe if the steady state is approximately reached by plotting the evolution of x_n. In the case where this visualization is difficult, a transient regime is obtained which is always usable. In this case also, the convergence time towards the steady state is greater than the simulation time time_ ⁇ Max_iter ⁇ .
- a typical value for the maximum number of iterations is between 10 3 and 10 6 depending on the size of the network and the number of sources.
- the graph of evolution of the average speed x in a router belonging to the path of a class s is an illustration of the average speeds calculated on a number of iterations equal to 4.
- the average flow rate of class s is x-1.
- the average speed is calculated at point A.
- the virtual inter-congestion time of the router considered is equal to the inter-congestion time of the network between the epochs of congestion 1 and 2.
- the average bit rate x-2 of the router is calculated as a function of the synchronization rate and corresponds to point A '.
- the average speed at point B is calculated the average speed at point B.
- the virtual inter-congestion time of the router considered is equal to the inter-congestion time of the network between the epochs of congestion 2 and 3.
- the average bit rate x-3 of the router is calculated at point B 'as a function of the synchronization rate.
- the average speed at point C is calculated.
- the virtual inter-congestion time of the router is not equal to the inter-congestion time of the network between the periods of congestion 3 and 4.
- the average speed x-4 is that calculated at point C.
- the average speed of the router considered is equal to the inter-congestion time of the network between the epochs of congestion 4 and 5.
- the average speed of the router is calculated at point D 'as a function of the synchronization rate.
- this average bit rate x-5 is equal to the average bit rate x-1, and the same is true for the other classes to which this router belongs and for other routers on the network, a set of mean bit rate value defines l '' periodic orbit sought.
- each instantaneous flow X-n [s, i] takes the value of a function F (s, i). This value is either a given fixed value or a value obtained by random drawing.
- the iteration of appendix B4-0 corresponds to the iteration of steps 1, 2 and 3 of appendices B4-1, B4-2, B4-3.
- the value of the maximum number of Max_iter iterations varies depending, for example, on the duration over which the network (time_ ⁇ Max_iter ⁇ ) is studied or practical constraints, such as simulation time. As before, a posteriori graphical visual observation provides an idea of whether or not you have reached steady state.
- step 1 of appendix B4-1 calculates the virtual inter-congestion times of each router, the bit rates x_n being stochastic (in 0b).
- V step 2 of appendix B4-2 calculates the virtual inter-congestion time of the network and, in (lb), the current instantaneous flow X_n [s] of each class s at the n th period of congestion, (lb) applies from all sources the linear growth mechanism (AI developed in the documents presenting the AIMD model).
- step 3 of appendix B4-3 the multiplicative decay mechanism (MD) of the AIMD mechanism is applied to the instantaneous bit rates of the sources passing through the congested router (in 2b).
- Algorithm 3 corresponds to algorithm 2 in which algorithm elements preceded by a star have been added.
- the following variables have also been added regarding the router buffer: - bb_n [r]: intermediate queue size in step n.
- - tauO_n [r] represents the tau_n [r] of algorithm 2, that is to say the virtual inter-congestion time obtained if the other routers are of infinite capacity c [r] and if the router 'r' considered n has no buffer.
- step 0 of appendix B5 the algorithm elements added to algorithm 2 are the initialization to zero of the queue sizes bb_n [r] and bn_n [r].
- step 1 of appendix B5-1 the calculation of a virtual inter-congestion time of a router at the time of congestion n takes into account the virtual inter-congestion time of the router without buffer memory and the calculation of the intermediate queue size of the router.
- step 2 of appendix B5-2 after the calculation of the inter-congestion times of the tau_n network at the times of congestion n and the updating of the flow rates, the queues bn-n [r] are put on the next day that, for a given period of congestion n and a router, the network inter-congestion time is greater than the time tau0_n [r] or not.
- the synchronization rate instead of being predefined, is estimated in the following. Several estimates are possible.
- the variables of the formula Cl-1 are calculated from the as follows: -the probability L of packet loss is calculated according to Cl -2 and - delta [s, r] is calculated as a proportion of round trip time rtt [s] of a class s which depends on the position of the router r.
- the random variable gamma [s, r] is determined according for example to Bernoulli's law as previously seen. Assuming the rate-dependent synchronization (RI case), the synchronization rate is calculated by an approximation of the C2-1 type.
- the synchronization rate is estimated for each router and for each class to which this router belongs.
- the random variable gamma [s, r] is determined according to for example a linear model, or a model depending on the flow rate in an exponential or polynomial manner.
- the method described assumes given the network parameters and the parameters of each TCP source.
- the device and the corresponding method can have the following typical applications.
- Direct application is the prediction of connection performance for a typical user in a given network configuration.
- the performance sought is for example the average speed obtained by a user, and more generally a guaranteed speed during a certain percentage of the connection time, a loss rate or any other values which depend on the temporal fluctuation of the instantaneous flow.
- Another direct application is the creation of an optimized TCP traffic generator.
- the invention covers the software product comprising the functions used in the test method.
- the invention also covers the software product defining the elements of the processing module of the device according to the invention.
- the sources are of the HTTP type.
- the synchronization rate is directly estimated by independent simulation.
- the routers are of the fair queing (FQ) type.
- Al-1 N_Router number of routers
- A1-3 B router buffer size (unit in packet / s);
- N_Router transmission delay (pure propagation);
- A3 -5 tau_n n-th network inter-congestion time
- : (c [r] - somjn [r]) / (m_rtt [r]);
- X_n [s, i]: gamma [s, r] * X_n [s, i];
- X_n [s, i]: F (s, i);
- ⁇ tau_n [r]: (c [r] - som_n [r]) / (m_rtt [r]);
- X_n [s, i]: F (s, i);
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EP02793213A EP1444808A2 (fr) | 2001-11-12 | 2002-11-07 | Dispositif et procede d'analyse resau a prediction autonome |
JP2003544970A JP2005510129A (ja) | 2001-11-12 | 2002-11-07 | 独自の予測ネットワーク分析を行う装置および方法 |
CA002466314A CA2466314A1 (fr) | 2001-11-12 | 2002-11-07 | Dispositif et procede d'analyse reseau a prediction autonome |
US10/494,966 US20050251702A1 (en) | 2001-11-12 | 2002-11-07 | Device and method for autonomous prediction network analysis |
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FR0114608A FR2832276B1 (fr) | 2001-11-12 | 2001-11-12 | Dispositif et procede d'analyse reseau a prediction autonome |
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EP (1) | EP1444808A2 (fr) |
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US7426569B2 (en) | 2004-02-25 | 2008-09-16 | Research In Motion Limited | System and method for maintaining a network connection |
DE602004008415T2 (de) * | 2004-02-25 | 2007-12-20 | Research In Motion Ltd., Waterloo | System und Verfahren zum Aufrechterhalten der Netzwerkverbindung |
JP4514501B2 (ja) * | 2004-04-21 | 2010-07-28 | 株式会社日立製作所 | ストレージシステム及びストレージシステムの障害解消方法 |
WO2006012211A2 (fr) * | 2004-06-24 | 2006-02-02 | Meshnetworks, Inc. | Systeme et procede pour le choix de debit adaptatif pour des reseaux sans fil |
US7975184B2 (en) * | 2006-04-03 | 2011-07-05 | Donald Goff | Diagnostic access system |
US7961605B2 (en) * | 2006-07-31 | 2011-06-14 | International Business Machines Corporation | System and method for enabling management of a plurality of messages in a communication network |
US9552550B2 (en) * | 2014-05-13 | 2017-01-24 | Cisco Technology, Inc. | Traffic shaping based on predicted network resources |
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US6028846A (en) * | 1997-09-11 | 2000-02-22 | U S West, Inc. | Method and system for testing real-time delivery of packets of data |
US6515967B1 (en) * | 1998-06-30 | 2003-02-04 | Cisco Technology, Inc. | Method and apparatus for detecting a fault in a multicast routing infrastructure |
US6480892B1 (en) * | 1998-12-16 | 2002-11-12 | Siemens Information And Communication Networks, Inc. | Apparatus and method for inserting predetermined packet loss into a data flow |
FR2805945B1 (fr) * | 2000-03-01 | 2002-05-03 | Inst Nat Rech Inf Automat | Surveillance et simulation perfectionnees de systemes complexes, notamment de mecanismes et de controles de flux et de congestions dans des reseaux de communication |
US6842427B1 (en) * | 2000-05-09 | 2005-01-11 | Itxc Ip Holdings S.A.R.L. | Method and apparatus for optimizing transmission of signals over a packet switched data network |
US7111073B1 (en) * | 2000-05-30 | 2006-09-19 | Cisco Technology, Inc. | Apparatus for estimating delay and jitter between network routers |
US6868068B1 (en) * | 2000-06-30 | 2005-03-15 | Cisco Technology, Inc. | Method and apparatus for estimating delay and jitter between network routers |
US6912203B1 (en) * | 2000-07-31 | 2005-06-28 | Cisco Technology, Inc. | Method and apparatus for estimating delay and jitter between many network routers using measurements between a preferred set of routers |
-
2001
- 2001-11-12 FR FR0114608A patent/FR2832276B1/fr not_active Expired - Fee Related
-
2002
- 2002-11-07 EP EP02793213A patent/EP1444808A2/fr not_active Withdrawn
- 2002-11-07 US US10/494,966 patent/US20050251702A1/en not_active Abandoned
- 2002-11-07 JP JP2003544970A patent/JP2005510129A/ja active Pending
- 2002-11-07 WO PCT/FR2002/003825 patent/WO2003043264A2/fr active Application Filing
- 2002-11-07 CA CA002466314A patent/CA2466314A1/fr not_active Abandoned
Also Published As
Publication number | Publication date |
---|---|
JP2005510129A (ja) | 2005-04-14 |
EP1444808A2 (fr) | 2004-08-11 |
US20050251702A1 (en) | 2005-11-10 |
CA2466314A1 (fr) | 2003-05-22 |
WO2003043264A3 (fr) | 2003-12-18 |
FR2832276A1 (fr) | 2003-05-16 |
FR2832276B1 (fr) | 2005-02-25 |
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