Omg issue Number


Disposition: Resolved OMG Issue No: 11553



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Disposition: Resolved

OMG Issue No: 11553


Title: Issue 11530: first sentence of sub-section 6.4.2

Source:

Thales (Sebastien Demathieu, sebastien.demathieu@thalesgroup.com)



Summary:

It is stated that MARTE uses "MOF 2.0 Queries, Views, and Transformation framework to define any model transformation rules...". Additionally, Figure 6.1 shows a <> dependency between MARTE and QVT. However, no explicit dependency to QVT concepts has been used/found in this document. Resolution:

The comment is correct. I suggest then to modify the text of the first paragraph of the section 6.1 and modify the figure 6.1 in order to suppress this reference to MOF QVT.



Revised Text:

In section 6.1, first paragraph, delete the fourth sentence, i.e., “In addition, it uses the MOF 2.0 Queries, Views, and Transformation framework to define any model transformation rules (e.g., rules for transforming a MARTE stereotype into a corresponding analysis model element). "

In section 6.1, modify figure 6.1 in order to suppress the package named MOF 2.0 QVT and delete the link between this latter package and the MARTE package.

Disposition: Resolved

Disposition: Resolved

OMG Issue No: 11554


Title: Class descriptions are missing (in Annex D)

Source:

THALES (Mr. Sebastien Demathieu, sebastien.demathieu@thalesgroup.com)



Summary:

Class descriptions are missing in the MARTE model library for extended datatypes (Annex D2).


Resolution:

We describe a subset that requires unambiguous definition. The remaining extended data types are self-explanatory.



Revised Text:

-In Section D.2, after the second paragraph, add:

“Extended data types that have not been described in the chapters that used them are described below.”


-In Section D.2, after Fig. D.4, add:

D.2.1 TransmModeKind

This enumeration defines the kind of transmission mode of messages over a network.



Literals

  • simplex it allows for one-way communication of data through the network.

  • half-duplex it allows communication in both directions, but only one direction at a time (not simultaneously). Typically, once a party begins receiving a signal, it must wait for the transmitter to stop transmitting, before replying.

  • full-duplex it allows communication in both directions, and unlike half-duplex, allows this to happen simultaneously.


-In Section D.2, after Fig. D.5, add:
D.2.2 ArrivalPattern

This is a ChoiceType that contains the different kinds of parameters that are necessary to specify the most common arrival patterns of events.


Attributes


  • periodic: PeriodicPattern it describes periodic interarrival patterns, with an optional maximal deviation (jitter).

  • aperiodic: AperiodicPattern it describes an unbounded pattern that is defined by a distribution function

  • sporadic: AperiodicPattern it describes a bounded pattern that is defined by a corner case interarrival times and a maximum deviation (jitter)

  • burst: BurstPattern it describes a bursty interarrival pattern with a number of events that can occur in a bounded period.

  • irregular: IrregularPattern it describes an aperiodic pattern that is described by a table of successive interarrivals durations measured from a starting phase.

  • closed: ClosedPattern it describes a workload characterized by a fixed number of active or potential users or jobs that cycle between executing the scenario.

  • open: OpenPattern it describes a workload that is modeled as a stream of requests that arrive at a given rate in some predetermined pattern (such as Poisson arrivals).


D.2.3 PeriodicPattern

This is a TupleType that contains the parameters that are necessary to specify a periodic pattern.


Attributes


  • period: NFP_Duration the period as a duration.

  • jitter: NFP_Duration the maximum deviation of the occurrences

  • phase: NFP_Duration a delay for the first occurrence of the event.


D.2.3 AperiodicPattern

This is a TupleType that contains the parameters that are necessary to specify an aperiodic pattern (unbounded pattern).


Attributes


  • distribution: NFP_CommonType a distribution of the arrival pattern that could use one of the patterns described in Section 8.3.3.



D.2.3 SporadicPattern

This is a TupleType that contains the parameters that are necessary to specify a sporadic pattern (bounded pattern).


Generalizations

  • AperiodicPattern


Attributes


  • minInterarrival: NFP_Duration the minimum interarrival duration between two successive occurrences of an event.

  • maxInterarrival: NFP_Duration the maximum interarrival duration between two successive occurrences of an event.

  • jitter: NFP_Duration the maximum deviation of the occurrences regarding to the minimum interarrival time.

D.2.3 BurstPattern

This is a TupleType that contains the parameters that are necessary to specify a bursty pattern.


Generalizations

  • AperiodicPattern


Attributes


  • minInterarrival: NFP_Duration the minimum interarrival duration between two successive occurrences of an event.

  • maxInterarrival: NFP_Duration the maximum interarrival duration between two successive occurrences of an event.

  • minEventInterval: NFP_Duration the minimum interval between two occurrences within a burst.

  • maxEventInterval: NFP_Duration the maximum interval between two occurrences within a burst.

  • burstSize: NFP_Integer the number of event occurrences within a burst.




  • minEventInterval: NFP_Duration the minimum interval between two occurrences within a burst.

D.2.3 IrregularPattern

This is a TupleType that contains the parameters that are necessary to specify an irregular pattern (list of duration separations between successive event occurrences). This is a fully deterministic arrival pattern.


Generalizations

  • AperiodicPattern


Attributes


  • phase: NFP_Duration a delay for the first occurrence of the event.

  • interarrivals: NFP_Duration [*] the set of duration separations between successive event occurrences.



D.2.3 ClosedPattern

This is a TupleType that contains the parameters that are necessary to specify a closed pattern. It is characterized by a fixed number of active or potential users or jobs that cycle between executing the scenario, and spending an external delay period (sometimes called “think time”) outside the system, between the end of one response and the next request.


Attributes


  • population: NFP_Integer The size of the workload (number of system users).

  • extDelay: NFP_Duration The delay between the end of one response and the start of the next for each member of the population of system users


D.2.3 OpenPattern

A workload that is modeled as a stream of requests that arrive at a given rate in some predetermined pattern (such as Poisson arrivals).


Attributes


  • interArrivalTime: NFP_Duration the time between successive arrivals. For a Poisson process this is exponentially distributed with mean = 1/rate.

  • arrivalRate: NFP_Frequency the average rate of arrivals.

  • arrivalProcess: String the name of an arrival process, understood by the analysis tool. Examples (not exhaustive) are Poisson, General, Phase-type, Markov-Modulated Poisson, Correlated, Pareto. If arrivalProcess is defined, normally arrivalRate is also defined, and interArrivalTime is not.


Disposition: Resolved


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