EP2044541A2 - Procédé de contôle de la capacité d'un système à fonctionner en temps réel - Google Patents

Procédé de contôle de la capacité d'un système à fonctionner en temps réel

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Publication number
EP2044541A2
EP2044541A2 EP07785867A EP07785867A EP2044541A2 EP 2044541 A2 EP2044541 A2 EP 2044541A2 EP 07785867 A EP07785867 A EP 07785867A EP 07785867 A EP07785867 A EP 07785867A EP 2044541 A2 EP2044541 A2 EP 2044541A2
Authority
EP
European Patent Office
Prior art keywords
events
event
elements
occurrence
event sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP07785867A
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German (de)
English (en)
Inventor
Karsten Albers
Frank Bodmann
Frank Slomka
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Inchron GmbH
Original Assignee
Inchron GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Inchron GmbH filed Critical Inchron GmbH
Priority to EP10196021A priority Critical patent/EP2306349A1/fr
Publication of EP2044541A2 publication Critical patent/EP2044541A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3604—Software analysis for verifying properties of programs
    • G06F11/3608—Software analysis for verifying properties of programs using formal methods, e.g. model checking, abstract interpretation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt

Definitions

  • the invention relates to a method for testing the real-time capability of a system, in particular of a computer system.
  • a model of inter-communicating tasks according to FIG. 1 is considered.
  • a "task” is a sequence of processing steps which processes a set of input values and thereby generates calculation results or changes of the system.
  • a task may be represented by an algorithmic description or a description of a hardware performing the functionality.
  • Tasks can activate other tasks, which, for example, further process the calculation results of the activating task.
  • Tasks can also access resources.
  • the activations or resource accesses can take place at any point in the process of the task.
  • the activations of a task or the resource access will be referred to as "event" in the following.
  • T ⁇ TIGUNGSKOPIE stand.
  • a "performance pattern” is a description of the performance behavior to understand.
  • the "performance pattern” consists of a plurality of descriptors.
  • the descriptors may be the same or different; they each represent a part of the "performance behavior of events”.
  • a possible description of a "performance pattern” is the so-called “event density”.
  • the maximum / minimum number of events is specified in time intervals of given length.
  • the event density can be subject to fluctuations.
  • the description should also support a quick analysis of the temporal behavior, in particular the guaranteed compliance with given time limits.
  • the interval lengths are important and maximum or minimum interval lengths are required for certain number of events or maximum or minimum number of events for particular interval lengths. Descriptions that make this determination possible with a linear effort with regard to the number of elements of the description are particularly advantageous.
  • Event stream is described here by a set of tuples, each consisting of a cycle z and a distance a.
  • the tuples can be referred to as "event stream elements".
  • the distance is assumed to be the same as specified for event stream elements.
  • Each event stream element generates its own occurrence pattern, wherein the first event of the event stream element can occur with a distance a from the common origin and then follow the following events offset by the cycle z.
  • the second event may therefore follow with a distance of a + z to the origin, the third event with a + 2z and so on.
  • the entire event stream results from a superposition of the occurrence patterns of all event stream elements. Furthermore, in the work only those event streams are considered valid, which generate an occurrence pattern in which the largest densities of events each start from the origin. Therefore, at least one event stream element must be assigned the initial distance zero.
  • the event stream describes an upper limit for a "maximum occurrence pattern 11 of events.”
  • a “maximum occurrence pattern” is understood as an occurrence pattern which has a maximum possible event density Allow events in a system to be considered as allowed by the event stream Permitted means that time limits that can be guaranteed to be met for the event stream will be met even if the event density is reduced.
  • the event stream model requires that each event within an event batch must be represented by its own event stream element. Only when event bumps occur repetitively within a performance pattern, it is sufficient to assign an event stream element to the events of the first event push. Since bursts of events can include thousands of elements, the event stream model is unsuitable as a form of description for such systems.
  • the appearance patterns are described by a lot of tuples. These consist of an event number and an interval in this model, and indicate the maximum number of events that can occur in an interval of this length.
  • the individual tuples are limited to each other, so that only performance patterns are allowed that meet all tuples.
  • This model can be used to efficiently describe certain event bumps.
  • the problem with the description format is that the proposed analysis Mood requires many linear combinations of all tuples and therefore is not feasible efficiently.
  • many types of event bouts can not be efficiently represented by the model.
  • An occurrence pattern in which, for example, the first two events occur with a distance t and all other events with a distance l> t to their respective predecessor event can not be efficiently represented by this model. Each event except the first would have to be described by its own tuple.
  • performance patterns are not describable, occur in which event bouts with different internal performance patterns.
  • Event member describes a time interval in which the principally periodic appearances can fluctuate. If the event member comprises several periods, all those events that occur within the time interval can occur almost simultaneously. They are only separated by the minimum distance.
  • This model can be used to describe a simple initial occurring burst of events with a simple minimum distance between events within the event. More complex event boosts, which z. For example, events with a more complex occurrence pattern of events within the event batch can not be represented by this description.
  • the object of the present invention is to eliminate the aforementioned disadvantages of the prior art.
  • a method is proposed for analysis, in particular real-time analysis, of a system, in particular of a computer system, in which a set of different tasks ( ⁇ ) is provided,
  • an occurrence pattern of the events requesting the tasks or generated by the task during the analysis is at least partially represented by a description of event densities
  • control flow which is represented by a control flow graph
  • a step is provided in which a further occurrence pattern for the control flow graph is determined from occurrence patterns of the events determined for a plurality of contiguous subgraphs and / or nodes of the control flowchart
  • this additional occurrence pattern represents a possible occurrence pattern of events in time dependence on certain pass points of the subgraphs.
  • resources are understood as means of work in a system which are available only to a limited extent or are allocated in quantity or time, such as, for example, Memory, capacity on communication media such as buses, processing time on processors and application specific switching logic.
  • occurrence pattern is understood to mean a temporal occurrence pattern that describes the occurrence of events on a timeline.
  • a "further control flow graph" in the sense of the present invention can be a subset of the "control flow graph”.
  • cost are understood to mean the types of costs required for the analysis, such as computing time or energy expenditure. These are available in limited time intervals only limited. An analysis that determines a minimum cost of certain outcomes, such as the maximum number of occurrences, thus also determines the minimum time required for that outcome.
  • An "event sequence” describes the "occurrence pattern” of events by their “event density”, an “event sequence element” is a subelement of this description.
  • An occurrence pattern of events is described by a "hierarchical event sequence ⁇ ". This consists of a set of hierarchical event sequence elements.
  • the limit n limits the maximum number of events that can be generated in a period of the embedded hierarchical event sequence ⁇ 1 .
  • a hierarchical event sequence element ⁇ may include an embedded hierarchical event sequence or even a simple event. In this case, only one limit with the value one makes sense. The period can also be occupied by the value ⁇ , which represents a one-time generation of the occurrence pattern.
  • a concrete hierarchical event sequence whose event occurrence patterns have the largest densities of events always at the beginning is called a "hierarchical event stream". It enables efficient methods for real-time analysis. To this extent, for example, in Karsten Albers, Frank Slomka, An Event Stream Driven Approximation for the Analysis of the Real-Time System, IEEE Proceedings of the 16th Euromicro Conference on Real-Time Systems 2004 (ECRTS '04), Catania, Italy, p 187-195 real-time analysis methods are applied mutatis mutandis.
  • a value of the limit is represented by a number of events.
  • a maximum interval length as the limit specify.
  • at most as many events in a period are generated by the hierarchical event sequence element as can be generated by the recursively embedded event sequence at most in one interval with the length of the boundary.
  • the occurrence pattern of the events of a hierarchical event sequence is formed by a superimposition of the occurrence patterns of the individual hierarchical event sequence elements.
  • the first event in the occurrence pattern of a hierarchical event sequence element can take place after the initial distance a.
  • the occurrence pattern of this and the following events is described by the occurrence pattern of the embedded hierarchical event sequence ⁇ ', which, however, is shifted from the origin of the occurrence patterns by the initial distance a. It includes only the first n events of the embedded hierarchical event sequence ⁇ 1 . These are referred to below as "event group”.
  • This embedded hierarchical event sequence ⁇ 1 is now repeated with the period p, thus starting at a + p, a + 2p, a + 3p, etc.
  • a hierarchical event sequence ⁇ can in turn be recursively embedded in a hierarchical event sequence. In this case, the above procedure is repeated.
  • Hierarchical event streams ⁇ in which the event groups generated by the individual event stream elements are separated from one another, ie must not overlap (separation condition).
  • the occurrence is determined by the recursively embedded event stream ⁇ c .
  • Two events can occur for the first time in a cost interval of length three, three events in a cost interval of length seven, four events in a cost interval of length 10, and five events in a cost interval of length 17.
  • the total amount of events that can be generated in a period of the second element of ⁇ A is limited to five, which can thus be generated in a cost interval of length 17. Since the second element of ⁇ A is assigned an initial distance of 30, the events of the first period of this element are generated in an interval between the cost points 30 and 47.
  • the events of the first period of the first element of ⁇ A are generated in the case of the maximum occurrence pattern in an interval of 18 cost units starting at the cost point zero.
  • the intervals at which the events of the second period are generated are [100,118] and [130,147], respectively.
  • the intervals do not overlap, so ⁇ A satisfies the separation condition.
  • identical periods homogeneous HES
  • HES homogeneous HES
  • the introduction of the separation condition enables a simple and efficient determination of the number of occurrences occurring in an interval I starting at the origin of the occurrence pattern as a function of the length of the interval. It can be determined, for example, by the following formula:
  • the method enables an exact determination of the set of events originating from a task separately for each permissible interval length.
  • the method enables a determination of the temporal density of the events originating from the task from the activations entering the task and the description of the control flow of the task.
  • "Outbound events" may be activations of other tasks, access to resources such as memory and buses, or other actions on the system, parts of it, or the environment. Activations and events may preferably be described using the hierarchical event sequence model.
  • a method is proposed for determining from the internal control flow of a task and the hierarchical event sequence entering this task the hierarchical event sequences emanating from this task.
  • the hierarchical event sequence describes the possible temporal densities of the events.
  • the information about the temporal relationships is abstracted from the internal control flow of the task. This makes it possible to determine the cases of maximum occurrence patterns across all execution paths within the task.
  • the event dependency analysis will be described in detail.
  • a task is modeled by a control flow graph, which can contain both branches and loops.
  • the individual nodes of the control flow graph can represent basic blocks.
  • a "basic block” is a sequence of consecutive instructions and is characterized in that branching and the generation of events can only take place at the end of the basic block. Events are generated on certain excellent base blocks.
  • Essential for the advantage of the analysis according to the invention is the existence of tasks which generate several events in one run. It is particularly advantageous if these events lead to activations of the same task or are otherwise in competition with each other for the same resource. The goal is to efficiently determine the possible temporal behavior of these events relative to one another from the control flow graph of the task.
  • the analysis consists of the following steps:
  • control flow includes branches, they will be traversed separately. Within a branch, the outgoing event sequences and event sequences for each branch are described separately. The individual descriptions are combined when the different branches come together. The details of this method will be described in more detail below.
  • FIG. 2 illustrates a step by step execution of a task graph.
  • the control flow graph represents the control data flow of a task. It consists of the basic blocks of the task and their activation or communication relationships.
  • a basic block contains an execution section and is characterized in that it can only be activated as a whole and branches and activations other basic blocks or tasks only at the end of the basic block. The concrete execution of a task thus results from wandering through the control flow graph along the activation relationships of the individual basic blocks.
  • Pass starts at a startup node of the task. This is activated by the events arriving in the task. At each step of the run, a follower node from an already analyzed node is considered. For branches, the different branches are processed separately.
  • Nodes that have several predecessor nodes are not processed until all predecessor nodes have been processed.
  • the control flow graph is thus reconstructed step by step during the run.
  • another node is added to the already analyzed subgraph. added.
  • This reconstruction need not be done in the procedure, it only determines the execution order of the nodes.
  • the event stream resulting from the previous subgraph is determined.
  • the information for the node itself such as execution costs, for example in the form of calculation time, whether an event is generated, and the event streams determined for the predecessor nodes are used.
  • Information from the other nodes or structure information about the partial graphs that have passed so far is no longer relevant. As basic operations for this step-by-step processing methods are needed to
  • Information is understood to mean the maximum and minimum event streams resulting from the subgraph and the event sequences required for their determination.
  • the event sequences are in particular the start event sequence, the end event sequence, the inside event sequence and the total event sequence. Not all event sequences are always needed. They are defined by the outcome of their event sequence analysis.
  • the "start event sequence” describes, for each possible number of events, the cost involved in activating the first node of the event Subgraphs are needed to generate the number of events in question from the subgraph.
  • the "end event sequence" describes how much cost was last needed to generate a certain number of events.
  • the "inside event sequence" determines, for each possible number of events, the minimum cost needed in any run of the subgraph to generate that number of events. It corresponds to the resulting event stream of the subgraph.
  • total event sequence describes, for each possible number of events, the minimum cost needed to pass through the subgraph from the start node activation to the final node completion. As the number of events increases, only monotonically increasing costs have to be considered. Are the costs to
  • generating four events lower than generating three events also assumes the cost of four events for generation for the three events.
  • sequences can be described, for example, efficiently in the form of an event sequence, advantageously a hierarchical event sequence (HES).
  • HES hierarchical event sequence
  • the costs of a potential path s should preferably be determined by the subgraph, in which none
  • the smallest possible unit to be considered as it passes through the control flow graph is a single node.
  • Each node of the control flow graph can itself be interpreted as a graph again.
  • the node may be characterized by an initial start, end, in and total event sequence.
  • StS represents the start event sequence, ES the end event sequence, IS the inner event sequence and TS the total event sequence, the value of k is the cost of the node. To determine the maximum event densities, the minimum execution cost of the node is required here. TS is described as a tuple from the minimum throughput costs even without event generation and the event sequence.
  • each step is the union of two subgraphs into a new one, which includes both subgraphs. The problem is thus reduced to the problem of how the event sequences of the subgraph can be used to determine the event sequences of the resulting subgraph.
  • the individual subgraphs may include different paths through the control flow graph in which the same number of events occur.
  • the union of the subgraphs can lead to further additional paths. These additional paths can lead to different costs.
  • the event sequences of the merged subgraph must cover the minimum and maximum costs, respectively, of the subgraphs and the additional paths resulting from the union.
  • the event sequences of the resulting sub-graph C are determined as follows.
  • the start event sequence of C results from a combination of the start event sequence of A with a concatenation of the total event sequence of A with the start event sequence of B.
  • the final event sequence of C is determined by merging the final event sequence of B with the concatenation of the final event sequence of A and the total event sequence of B.
  • the inner event sequence results from the stepwise union of three event sequences. These are, on the one hand, the inner event sequences of the subgraphs A and B and, on the other hand, an event sequence which results from the concatenation of the end event sequence of A with the start event sequence of B.
  • the total event sequence of C is just a concatenation of the total event sequences of A and B.
  • the hierarchical event sequences at least largely satisfy the separation condition.
  • Unification operation incoming event sequences.
  • the execution of the operation for the best possible event sequences follows analogously.
  • the event sequence resulting from this embodiment of the merge operation also satisfies the event stream property.
  • the merge operation thus receives this property.
  • FIG. 3 illustrates the process for efficiently determining the doping sequence.
  • the initial value of the straight line is the initial distance and the slope is given by the period.
  • a comparison of two different event sequence elements of different event sequences is thereby reduced to stepwise minimum formation of two lines. There are two possible cases. Either there is an intersection between the lines or not.
  • the resulting event sequence is then formed from at least two elements. These result from the elements dominating in the subsections, which are adapted to the individual dominating sections by adapting the initial distance and the maximum number of events that can be generated in a period.
  • the first element is provided with a limit on the maximum number of events it can generate, so that only events up to the point of intersection are generated.
  • the second element is provided with an initial distance that allows only event generation at or beyond the intersection. The limit of the second element is adjusted so that the total number of events that can be generated is not exceeded. As a result, the separation condition is retained in the resulting event sequence as well.
  • An intersection occurs when one element has a smaller initial distance but a larger period than the other element.
  • event sequences consist of more than one element, corresponding straight lines result separately for the separation areas resulting from the individual elements. This can cause the domination to change several times.
  • a separate event sequence element is inserted into the resulting event sequence for each dominating section.
  • these can then be combined in part with exact or approximate compression methods. For example, if neighboring elements have the same period, they can be grouped into one element with that period. All such event sequence elements in the individual sections, which can lead to the same number of events, are always compared with one another. If event sequence elements are repeated periodically, this can lead to a repetitive change of domination.
  • Element of ⁇ 3 and an element of ⁇ 4 In order to be able to find clear dominions, such elements now become interconnected which result in the same number of events. For this, the number of affected elements of the involved event sequences are adapted to each other. This can be done, for example, by bringing the number of elements in all event sequences to the least common multiple of the element numbers of the individual event sequences. The number of elements of an event sequence is increased by increasing the period (for example, doubling) and, for compensation, the missing events are generated by additional elements. For example, at ⁇ 4 , the
  • the events for this event sequence are then no longer generated by an element, but alternately by the two elements. If the numbers of the affected elements of the different event sequences are brought to a common denominator, then the respective corresponding elements, which lead to the same number of events, can be directly compared with each other.
  • the periods of the individual event sequence elements are irrelevant. For example, in the above example, the first elements each determine the odd number of event slots 1,3,5,7, ..., and the second elements the intervals of the even number of events 2,4,6, .... This is true at least after the adaptation of the element number and possible sorting of the elements according to increasing initial distance.
  • the domination intervals can now be determined separately and the corresponding event sequence elements inserted into the resulting event sequence.
  • the event sequences ⁇ 3 and ⁇ 4 listed above have an example following expansion:
  • ⁇ r ⁇ ( ⁇ , 0.50, ⁇ (24, 0.1, e) ⁇ ), (co, 0, 11, ⁇ (25, 0, 1, e) ⁇ ), (oo, 264, 39 , ⁇ (24,12, l, e) ⁇ ), ( ⁇ , 1188, 50, ⁇ 3 ) ⁇ .
  • ⁇ r ⁇ ( ⁇ , 0, 22, ⁇ (24, 0, 1, e), (25, 1, 1, e ⁇ ), (oo, 264.78 , ⁇ (12, 0.1, e) ⁇ ), ( ⁇ , 1188, 100, ⁇ 3 ) ⁇
  • a line segment breaks off due to the limitation of the total number of events. In this case, one of the remaining straight lines dominates. In the example, this is the case for all event numbers from 100 events.
  • the first element of ⁇ 3 dominates in the odd number of events and the second element of ⁇ 4 in the case of the even number of events. There, after 11 events, the domination of the second element of ⁇ 3 changes . After 100 events, only the elements of ⁇ 3 dominate .
  • an exemplary embodiment for the concatenation operation is presented. It is mainly needed to determine the inside event sequence.
  • the elements of the involved event sequences were compared with each other separately.
  • the elements of an event sequence are complemented by elements of the other event sequence. This leads to more combination possibilities for the individual event numbers.
  • Many event numbers can be involved by each element of the th event sequences are generated at a corresponding supplement with a corresponding element of the other event sequence.
  • the resulting event sequence contains only intervals that by definition always contain the interface between the event sequences in possible result intervals.
  • the different combinations of elements lead to different minimum event numbers. For each combination, there is an initial distance that results from the sum of the initial distances of the two elements involved.
  • the combinations can be continued at maximum in two directions periodically. Corresponding to the merging operation, only the combinations which lead to the same number of occurrences even if they are continued are compared with one another. This again results in possible domination sections.
  • the combinations are compared in two dimensions, namely their initial distance and their period. If a combination can be continued periodically in both directions, it will be included twice in this comparison. If several combinations are identical in both values, any one can be selected.
  • a combination with a shorter initial interval is better than one with a longer, one combination with a shorter period better than one with a longer one.
  • Each combination is continued with the event sequence element that has the lower period since the initial distances within a combination are always the same.
  • the domino sections and intersections are again determined via the straight line equations, and from this the resulting event sequence elements are determined determines their initial distances and their limits.
  • a special feature of the concatenation operation is that when a limitation of the number of events for an event sequence element has been reached, the combination can be continued by the corresponding event sequence element until its limitation has also been reached.
  • Event sequence two and an event sequence generates three events (intermediate combination) at equal or greater intervals and are therefore not relevant to the formation of the case of the maximum performance pattern.
  • ⁇ i represents an end event sequence and ⁇ 2 a start event sequence.
  • ⁇ i, i be the first event sequence element of ⁇ i, COi 12 the second event sequence element of ⁇ i, ⁇ 2 / i the first event sequence element of ⁇ 2 and ⁇ 2 , 2 the second event sequence element of ⁇ 2 .
  • At the concatenation of the two Event sequences results in an initial distance for two events, which is determined by the sum of the initial distances of both event sequences. For each of the event sequences involved, this is the smallest initial distance of all their event sequence elements. In the present example, this sieving amounts to and results from the event sequence elements ⁇ i, i with initial distance zero and co 2 / i with initial distance sieve. If the event sequence elements are sorted, the respective first elements always result in this interval of two events.
  • this is the combination of the dealtsequenzelemen- th C0i / 2 and ⁇ 2, i, which is periodically continued by the element ⁇ 2 / i. Only if the limit is reached, the combination is continued by the other element,
  • the number of events 4, 6, 8, 10, ... are assigned either by a combination of the event sequence elements ⁇ i / 2 and ⁇ 2/2 or by a co A combination of the elements ⁇ x , i and ⁇ 2 / i achieved.
  • one period is set to the initial distance of the Combination included. By including a period, the corresponding limit must be reduced by one in the further analysis.
  • the following possible combination elements thus result (without limitation): (25,20, l, e), (24, 20,1, e), (25,25, l, e) and (24,24, l, e ). It dominates first the second combination. This results in the same period 24 for the elements which initially dominate, which allows a combination of these elements into a hierarchical event sequence element. In principle, such a summary is also possible with different periods up to the points at which one element "overhauls" the other, ie changes the order in which the elements generate events. Thus, without taking into account the limitation for the resulting event sequence, the event sequence elements (24, 8, 1, e) and (24, 20, 1, e) result.
  • loop analysis can also be carried out with less effort by using the methods presented by exploiting the regularity of the structure and the occurrence patterns of events.

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Abstract

Procédé d'analyse, en particulier d'analyse en temps réel, d'un système, en particulier d'un système informatique, selon lequel un certain nombre de tâches (t) différentes sont prévues, lesdites tâches (t<SUB>n</SUB>) étant au moins en partie demandées et traitées de manière répétée par le système ou produisant de manière répétée des demandes à des composants du système (événements). Le modèle d'apparition des événements demandant les tâches ou produits par les tâches est représenté au cours de l'analyse au moins en partie par une description constituée d'une certaine quantité d'éléments qui décrivent chacun le modèle d'apparition des événements. Ledit procédé est caractérisé en ce que pour au moins deux éléments de description du modèle d'apparition représentés par eux, un certain nombre d'éléments n'étant pas nécessairement uniformes est de surcroît utilisé et ces nombres d'éléments ainsi que le modèle d'apparition décrit par eux se distinguent les uns des autres.
EP07785867A 2006-07-03 2007-06-28 Procédé de contôle de la capacité d'un système à fonctionner en temps réel Withdrawn EP2044541A2 (fr)

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DE102006031013 2006-07-03
PCT/EP2007/005732 WO2008003427A2 (fr) 2006-07-03 2007-06-28 Procédé de contôle de la capacité d'un système à fonctionner en temps réel

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JP (1) JP2009541876A (fr)
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WO (1) WO2008003427A2 (fr)

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US8306784B2 (en) 2012-11-06
WO2008003427A3 (fr) 2008-06-19
US20100017168A1 (en) 2010-01-21
CA2656673C (fr) 2016-02-23
CA2656673A1 (fr) 2008-01-10
IL196155A0 (en) 2009-09-22
JP2009541876A (ja) 2009-11-26
EP2306349A1 (fr) 2011-04-06
KR20090040312A (ko) 2009-04-23
WO2008003427A2 (fr) 2008-01-10

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