CN112202596A - Abstract model construction device based on time sensitive network system - Google Patents
Abstract model construction device based on time sensitive network system Download PDFInfo
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Abstract
The invention discloses an abstract model construction device based on a time sensitive network system, which comprises: the building module is used for building a data model, a resource model and a component model of the time-sensitive network transmission node; the first integration module is used for integrating the data model, the resource model and the component model to obtain an abstract model of a single transmission node of the time sensitive network; and the second integration module is used for integrating the abstract model of the single transmission node to obtain the abstract model of the time-sensitive network system. By the abstract model of the time-sensitive network system, various performances of the time-sensitive network system can be conveniently analyzed, and the method has important significance.
Description
Technical Field
The invention relates to the technical field of Ethernet and network calculation, in particular to an abstract model construction device based on a time-sensitive network system.
Background
In today's embedded industrial systems, under the pressure of enormous time to market for products, every manufacturer has a strong desire to speed up the pace of the product, especially in the field of electronic products, such as mobile phones, where the product life cycle is weeks or months, rather than years, and where the product is ready, it may be completely out of date, or may be produced at a lower cost using other techniques, both of which are business cases that have a serious impact on the product, and thus the industry focus is shifting from the stage of improving system implementation to the stage of improving system design. The ability to quickly evaluate new designs, whether motivated by new technology seeking to change or influenced by changing market conditions, is essential. The evaluation process should be lightweight, fast and reliable so that new products meeting customer requirements can be produced more quickly at a minimum cost.
At present, the traditional simulation analysis-based mode cannot meet the requirement of rapid analysis, the cost is very high, and the requirement of performance analysis of the time-sensitive network cannot be met. Therefore, it is significant to provide an analysis scheme capable of analyzing network performance quickly and with low cost.
Disclosure of Invention
The embodiment of the disclosure provides an abstract model construction device based on a time-sensitive network system. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present disclosure provides a method for constructing an abstract model based on a time-sensitive network system, including:
constructing a data model, a resource model and a component model of a time-sensitive network transmission node;
integrating the data model, the resource model and the component model to obtain an abstract model of a single transmission node of the time sensitive network;
and integrating the abstract models of the single transmission nodes to obtain the abstract model of the time sensitive network system.
Further, constructing a data model of the time-sensitive network transmission node includes:
and constructing a data model according to the data traffic of the time-sensitive network transmission node.
Further, constructing a resource model of the time-sensitive network transmission node includes:
analyzing the gate control list data of the time-sensitive network transmission node to generate a resource model to be adjusted;
analyzing the influence of low-priority traffic, a prediction mechanism and high-priority traffic on a traffic transmission time slot;
and adjusting the resource model to be adjusted according to the influence to obtain the constructed resource model.
Further, adjusting the resource model to be adjusted according to the influence to obtain the constructed resource model, including:
adjusting a resource model to be adjusted according to the influence of low-priority traffic to obtain a first resource model;
adjusting the first resource model according to the influence of a prediction mechanism to obtain a second resource model;
and adjusting the second resource model according to the influence of the high-priority flow to obtain the constructed resource model.
Further, adjusting the resource model to be adjusted according to the influence of the low-priority traffic to obtain a first resource model, including:
and deleting the part covered by the low-priority flow and the target-priority flow existing in the gate control list data to obtain a first resource model.
Further, adjusting the first resource model according to the influence of the prediction mechanism to obtain a second resource model, including:
analyzing whether the residual time window of the transmission time slot can meet the transmission of the flow;
when the flow transmission can be satisfied, continuing the transmission;
when the flow transmission can not be met, stopping the flow transmission and waiting for the next time window;
and obtaining a second resource model.
Further, adjusting the second resource model according to the influence of the high-priority traffic to obtain the constructed resource model, including:
and deleting the part covered by the high-priority flow and the target-priority flow in the gate control list data to obtain the constructed resource model.
Further, constructing a component model of a time-sensitive network transmission node includes:
constructing a component function of a time-sensitive network transmission node;
the function associates a data model curve of the incoming component model with a resource model curve and a data model curve of the outgoing component model with a resource model curve.
Further, the abstract model of a single transmission node is integrated to obtain the abstract model of the time-sensitive network system, which includes:
and integrating the abstract models of the single nodes according to the topological structure of the time sensitive network system to obtain the abstract model of the time sensitive network system.
In a second aspect, an embodiment of the present disclosure provides an abstraction model building apparatus based on a time-sensitive network system, including:
the building module is used for building a data model, a resource model and a component model of the time-sensitive network transmission node;
the first integration module is used for integrating the data model, the resource model and the component model to obtain an abstract model of a single transmission node of the time sensitive network;
and the second integration module is used for integrating the abstract models of the single transmission node to obtain the abstract model of the time-sensitive network system.
Further, a building block comprising:
and the data model building unit is used for building a data model according to the data traffic of the time sensitive network transmission node.
Further, a building block comprising:
the resource model building unit is used for analyzing the gate control list data of the time-sensitive network transmission node and generating a resource model to be adjusted;
the method is used for analyzing the influence of low-priority traffic, a prediction mechanism and high-priority traffic on a traffic transmission time slot;
and the resource model to be adjusted is adjusted according to the influence to obtain the constructed resource model.
Further, the method for adjusting the resource model to be adjusted according to the influence to obtain the constructed resource model includes:
the resource model adjusting method comprises the steps of adjusting a resource model to be adjusted according to the influence of low-priority flow to obtain a first resource model;
the resource prediction method comprises the steps of adjusting a first resource model according to the influence of a prediction mechanism to obtain a second resource model;
and the resource model is used for adjusting the second resource model according to the influence of the high-priority flow to obtain the constructed resource model.
Further, the adjusting the resource model to be adjusted according to the influence of the low-priority traffic to obtain the first resource model includes:
and deleting the part covered by the low-priority traffic and the target-priority traffic existing in the gate control list data to obtain the first resource model.
Further, the adjusting the first resource model according to the influence of the prediction mechanism to obtain a second resource model includes:
the method comprises the steps of analyzing whether a residual time window of a transmission time slot can meet the transmission of traffic;
when the flow transmission can be satisfied, continuing the transmission;
when the flow transmission can not be met, stopping the flow transmission and waiting for the next time window;
and obtaining a second resource model.
Further, the method for adjusting the second resource model according to the influence of the high-priority traffic to obtain the constructed resource model includes:
and deleting the part covered by the high-priority flow and the target-priority flow existing in the gate control list data to obtain the constructed resource model.
Further, a building block comprising:
the component model building unit is used for building a component function of the time-sensitive network transmission node;
the function associates a data model curve of the incoming component model with a resource model curve and a data model curve of the outgoing component model with a resource model curve.
Further, a second integration module comprising:
and the method is used for integrating the abstract models of the single nodes according to the topological structure of the time sensitive network system to obtain the abstract model of the time sensitive network system.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the invention provides an abstract model constructing device based on a time-sensitive network system, which is used for constructing a data model, a resource model and a component model for a single node in the time-sensitive network on the basis of a real-time calculation technology, then integrating the three models to obtain an abstract model of the single node, and then integrating the abstract model to obtain an abstract model of the whole time-sensitive network.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flowchart illustrating a method for constructing an abstract model based on a time-sensitive network system, according to an example embodiment;
FIG. 2 is a flowchart illustrating a resource model building method according to an exemplary embodiment;
FIG. 3 is a flowchart illustrating a method for abstract model construction based on a time-sensitive network system, according to an example embodiment;
FIG. 4 is a diagram illustrating a component model based on a time sensitive network in accordance with an exemplary embodiment;
FIG. 5 is a diagram illustrating an abstract model of a time-sensitive network-based system in accordance with an exemplary embodiment;
FIG. 6 is a schematic diagram illustrating a resource model based on a time sensitive network in accordance with an exemplary embodiment;
FIG. 7 is a block diagram illustrating an apparatus for constructing an abstract model based on a time-sensitive network system, according to an example embodiment;
FIG. 8 is a block diagram illustrating a building block according to an exemplary embodiment.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The first embodiment is as follows:
FIG. 1 is a flowchart illustrating a method for constructing an abstract model based on a time-sensitive network system, according to an example embodiment;
FIG. 3 is a flowchart illustrating a method for abstract model construction based on a time-sensitive network system, according to an example embodiment;
as shown in fig. 1, a method for constructing an abstract model based on a time-sensitive network system includes:
s101, constructing a data model, a resource model and a component model of a time-sensitive network transmission node;
the data model is a model for describing the data quantity of a single transmission node of the time-sensitive network, and the data model is described by using an arrival curve, wherein the arrival curve is a data model curve.
Specifically, as shown in fig. 3, when a data model is constructed, a data model of a time-sensitive network is analyzed, basic information of the data model is provided according to input path and traffic configuration information, a triplet (p, j, d) is used to represent the basic information of the data model construction process, where p represents a period of arriving traffic, j represents jitter of the traffic, and d represents a shortest time for transmitting the traffic from a previous node to a current node, and then a minimum and maximum arrival curve of the network traffic is calculated to obtain the data model of the time-sensitive network transmission node.
The resource model is constructed according to the gate control list data of a single transmission node of the time sensitive network, and the resource model is described by using a service curve, wherein the service curve is the resource model curve.
Specifically, as can be seen from fig. 3, when constructing the resource model, the gating control list data and the measurement method of a single transmission node of the time-sensitive network are analyzed first, and then the resource model is constructed.
FIG. 2 is a flowchart illustrating a resource model building method according to an example embodiment. As shown in fig. 2, the resource model building method includes:
step S201, analyzing the gate control list data of the time-sensitive network transmission node to generate a resource model to be adjusted;
step S202, analyzing the influence of low-priority flow, a prediction mechanism and high-priority flow on a flow transmission time slot;
step S203 adjusts the resource model to be adjusted according to the influence, and the constructed resource model is obtained.
Wherein the component model is used to describe how to handle arriving traffic according to transmission resources for a single transmission node of a time sensitive network, the component is internally specified by a set of functions that relate incoming arrival curves and service curves to outgoing arrival curves and service curves.
Specifically, as shown in FIG. 3, within a component model, such components are specified by a set of functions that associate incoming arrival curves and service curves with outgoing arrival curves and service curves, the functional relationships are related to the processing semantics of the actual components for a given component model, and different semantics will exist under different circumstances, where the components are set as fixed priority components. Fig. 4 is a diagram illustrating a component model based on a time sensitive network, according to an example embodiment, where the arrival curve and the service curve are processed by a transmission node to generate an arrival curve and a remaining service curve, as shown in fig. 4.
Step S102, integrating a data model, a resource model and a component model to obtain an abstract model of a single transmission node of a time sensitive network;
for a single transmission node of a time sensitive network, an abstract model of the transmission node is that a data model, a resource model and a component model of the transmission node are integrated to obtain the abstract model of the single transmission node.
And S103, integrating the abstract models of the single transmission nodes to obtain the abstract model of the time sensitive network system.
For the whole time-sensitive network system, the abstract model of the time-sensitive network system is obtained by integrating the abstract models of single nodes according to the topological structure of the system. As shown in fig. 3, a system architecture is completed according to the UML and SysML modeling languages, an abstract model of a single node is generated according to the constructed resource model, data model, and component model, and an abstract model of the time sensitive network system is generated according to the system architecture and the abstract model of the single node. And performing performance analysis on the time sensitive network system through an abstract model and a real-time calculation technology of the time sensitive network system, and outputting an analysis result.
FIG. 5 is a diagram illustrating an abstract model of a time-sensitive network-based system in accordance with an exemplary embodiment;
as shown in fig. 5, a bandwidth resource (resource) transmits resources through transmission nodes, and the abstract models of the transmission nodes are connected through a topology structure of the system, so as to obtain an abstract model based on a time-sensitive network system.
By the method, the constructed abstract model based on the time-sensitive network system is obtained. By the model, various performances of the network system, including network delay, cache, network transmission utilization rate and the like, can be conveniently analyzed, and the model has important significance.
Further, constructing a data model of the time-sensitive network transmission node includes:
and constructing a data model according to the data traffic of the time-sensitive network transmission node.
The data model is a model for describing the data quantity of a single transmission node of the time-sensitive network, and the data model is described by using an arrival curve, wherein the arrival curve is a data model curve.
Specifically, as shown in fig. 3, when a data model is constructed, a data model of a time-sensitive network is analyzed, basic information of the data model is provided according to input path and traffic configuration information, a triplet (p, j, d) is used to represent the basic information of the data model construction process, where p represents a period of arriving traffic, j represents jitter of the traffic, and d represents a shortest time for transmitting the traffic from a previous node to a current node, and then a minimum and maximum arrival curve of the network traffic is calculated to obtain the data model of the time-sensitive network transmission node.
Further, constructing a resource model of the time-sensitive network transmission node includes:
analyzing the gate control list data of the time-sensitive network transmission node to generate a resource model to be adjusted;
specifically, the method for analyzing the gate control list data of the transmission node of the time sensitive network firstly includes: a traffic transmission time slot period, a traffic transmission link, a traffic priority, and a traffic time slot. By analyzing the data of the gate control list, a resource model to be adjusted without influence of other factors can be constructed, and the constructed method uses a network calculation method, is similar to a time division multiple access analysis method, and obtains a minimum service curve and a maximum service curve of a time-sensitive network, and the minimum service curve and the maximum service curve respectively represent the minimum transmission capacity and the maximum transmission capacity of the network under an ideal condition.
Analyzing the influence of low-priority traffic, a prediction mechanism and high-priority traffic on a traffic transmission time slot;
FIG. 6 is a schematic diagram illustrating a resource model based on a time sensitive network in accordance with an exemplary embodiment;
as shown in figure 6 of the drawings,
indicating that the traffic is of low priority and,
a target priority traffic is indicated and,
the traffic flow with high priority is shown, the shaded part shows the reduction of a transmission window caused by the influence of other priority traffic or a prediction mechanism, and the broken line is an obtained service curve and shows the transmission capability of the time-sensitive network under the influence of other priority and the prediction mechanism.
In general, during traffic transmission, traffic is transmitted strictly according to the time window in which the gating control list is distributed. But not within the time window, it is still subject to other factors, including the impact of low priority traffic, the impact of predictive mechanisms, which are present in each time window, and the impact of high priority traffic.
Specifically, the influence of low-priority traffic on the traffic transmission time slot is analyzed, and for the currently analyzed target priority buffer queue, the influence of low-priority traffic on the traffic transmission time slot compared with the currently analyzed target priority buffer queue is considered, as can be seen from fig. 6, when the target traffic starts to be transmitted, the low-priority traffic has not been transmitted yet, the low-priority traffic in the time-sensitive network continues to be transmitted, and the transmission of the target traffic can be performed. Low priority traffic therefore has an impact on the transmission of the target traffic. In the actual analysis, the maximum possible size influenced by the low-priority traffic needs to be analyzed; analyzing the impact of the prediction mechanism on the traffic transmission time slot, it can be seen from fig. 6 that, at the end stage of each time window, there is an affected length, which is the impact of the prediction mechanism. In time sensitive networks, each priority queue has a separate transmission time slot, and the transmission of traffic cannot exceed the respective transmission range, especially at the end of the transmission window, and in case a very long traffic frame starts to be transmitted and cannot complete the transmission process in the next time slot, it will collide with the time slots of other priorities. The solution of the prediction mechanism, which proposes the concept of guard band and effectively alleviates these losses by the guard band, checks before transmission whether the remaining transmission time can satisfy the transmission of traffic, if the length of time available for transmission satisfies the requirement, then normally transmits, otherwise stops, and waits for the next time slot of the priority to arrive; analyzing the influence of the high-priority traffic on the traffic transmission time slot, it can be seen from fig. 6 that the preemption of the target traffic by the high-priority traffic is absolute. In the process of traffic transmission, when high-priority traffic starts to wait for transmission, once the preemption condition for the current transmission traffic is met, preemption is performed, which greatly increases the delay of target traffic.
And adjusting the resource model to be adjusted according to the influence to obtain the constructed resource model.
Specifically, the resource model to be adjusted is adjusted according to the influence of the low-priority traffic to obtain a first resource model, the influence of the low-priority traffic on the traffic transmission time slot is analyzed, and for the currently analyzed target priority buffer queue, the influence of the low-priority traffic on the traffic transmission time slot compared with the currently analyzed target priority buffer queue is considered, as can be seen from fig. 6, when the target traffic starts to be transmitted, the low-priority traffic does not complete transmission yet, the low-priority traffic in the time-sensitive network continues to complete transmission, and the transmission of the target traffic can be performed. According to the influence, the condition that the low-priority traffic and the target-priority traffic existing in the gate control list are covered is analyzed, and the part covered by the low-priority traffic and the target-priority traffic existing in the data of the gate control list is deleted to obtain the first resource model.
And adjusting the first resource model according to the influence of the prediction mechanism to obtain a second resource model, and analyzing the influence of the prediction mechanism on the traffic transmission time slot. In time sensitive networks, each priority queue has a separate transmission time slot, and the transmission of traffic cannot exceed the respective transmission range, especially at the end of the transmission window, and in case a very long traffic frame starts to be transmitted and cannot complete the transmission process in the next time slot, it will collide with the time slots of other priorities. The solution of the prediction mechanism is to check whether the remaining transmission time can satisfy the transmission of the traffic before transmission, and if the time length available for transmission satisfies the requirement, the transmission is normal, otherwise, the transmission is stopped, and a next time slot of the priority comes, so as to obtain a second resource model.
And adjusting the second resource model according to the influence of the high-priority flow to obtain the constructed resource model. As can be seen from fig. 6, preemption of the target traffic by high priority traffic is absolute. In the process of flow transmission, when high-priority flow starts to wait for transmission, once the preemption condition of the current transmission flow is met, preemption is carried out, so that the delay of target flow is greatly increased, according to the influence, the condition that the high-priority flow and the target-priority flow in a gate control list are covered is analyzed, and the part covered by the high-priority flow and the target-priority flow in the gate control list data is deleted, so that the constructed resource model is obtained.
In some optional embodiments, the influence of the three situations is comprehensively analyzed, the delay of the start phase caused by low-priority traffic and the delay of the start phase caused by high-priority traffic take the maximum value, the influence of the prediction mechanism in the close phase and the influence of the high-priority traffic in the close phase take the minimum value, and the actual opening and closing time of the transmission window can be obtained.
FIG. 6 is a schematic diagram illustrating resource model generation based on a time sensitive network in accordance with an exemplary embodiment.
As shown in figure 6 of the drawings,
indicating that the traffic is of low priority and,
a target priority traffic is indicated and,
the traffic flow with high priority is shown, the shaded part shows the reduction of a transmission window caused by the influence of other priority traffic or a prediction mechanism, and the broken line is an obtained service curve and shows the transmission capability of the time-sensitive network under the influence of other priority and the prediction mechanism.
By the method, influence factors can be combined to generate a more accurate resource model, and the method can play an active role in performance analysis of the time-sensitive network.
Further, constructing a component model of a time-sensitive network transmission node includes:
constructing a component function of a time-sensitive network transmission node;
the function associates a data model curve of the incoming component model with a resource model curve and a data model curve of the outgoing component model with a resource model curve.
Wherein the component model is used to describe how to handle arriving traffic according to transmission resources for a single transmission node of a time sensitive network, the component is internally specified by a set of functions that relate incoming arrival curves and service curves to outgoing arrival curves and service curves.
Specifically, as shown in FIG. 3, within a component model, such components are specified by a set of functions that associate incoming arrival curves and service curves with outgoing arrival curves and service curves, the functional relationships are related to the processing semantics of the actual components for a given component model, and different semantics will exist under different circumstances, where the components are set as fixed priority components. Fig. 4 is a diagram illustrating a component model based on a time sensitive network, according to an example embodiment, where the arrival curve and the service curve are processed by a transmission node to generate an arrival curve and a remaining service curve, as shown in fig. 4.
Further, the abstract model of a single transmission node is integrated to obtain the abstract model of the time-sensitive network system, which includes:
and integrating the abstract models of the single nodes according to the topological structure of the time sensitive network system to obtain the abstract model of the time sensitive network system.
For the whole time-sensitive network system, the abstract model of the time-sensitive network system is obtained by integrating the abstract models of single nodes according to the topological structure of the system. As shown in fig. 3, a system architecture is completed according to the UML and SysML modeling languages, an abstract model of a single node is generated according to the constructed resource model, data model, and component model, and an abstract model of the time sensitive network system is generated according to the system architecture and the abstract model of the single node. And performing performance analysis on the time sensitive network system through an abstract model and a real-time calculation technology of the time sensitive network system, and outputting an analysis result.
FIG. 5 is a diagram illustrating an abstract model of a time-sensitive network-based system in accordance with an exemplary embodiment;
as shown in fig. 5, a bandwidth resource (resource) transmits resources through transmission nodes, and the abstract models of the transmission nodes are connected through a topology structure of the system, so as to obtain an abstract model based on a time-sensitive network system.
By the method, the constructed abstract model based on the time-sensitive network system is obtained. By the model, various performances of the network system, including network delay, cache, network transmission utilization rate and the like, can be conveniently analyzed, and the model has important significance.
Example two:
the embodiment of the disclosure provides an abstract model construction device based on a time-sensitive network system,
FIG. 7 is a block diagram illustrating an apparatus for constructing an abstract model based on a time-sensitive network system, according to an example embodiment;
as shown in fig. 7, an abstraction model constructing apparatus based on a time-sensitive network system includes:
the S701 construction module is used for constructing a data model, a resource model and a component model of the time-sensitive network transmission node;
s702, a first integration module for integrating the data model, the resource model and the component model to obtain an abstract model of a single transmission node of the time-sensitive network;
s703 a second integration module, configured to integrate the abstract models of the single transmission node, so as to obtain an abstract model of the time-sensitive network system.
FIG. 8 is a block diagram illustrating a building block according to an exemplary embodiment.
As shown in fig. 8, a building block includes:
and the S801 data model building unit is used for building a data model according to the data traffic of the time sensitive network transmission node.
Further, a building block comprising:
the S802 resource model building unit is used for analyzing the gate control list data of the time-sensitive network transmission node and generating a resource model to be adjusted;
the method is used for analyzing the influence of low-priority traffic, a prediction mechanism and high-priority traffic on a traffic transmission time slot;
and the resource model to be adjusted is adjusted according to the influence to obtain the constructed resource model.
Further, the method for adjusting the resource model to be adjusted according to the influence to obtain the constructed resource model includes:
the resource model adjusting method comprises the steps of adjusting a resource model to be adjusted according to the influence of low-priority flow to obtain a first resource model;
the resource prediction method comprises the steps of adjusting a first resource model according to the influence of a prediction mechanism to obtain a second resource model;
and the resource model is used for adjusting the second resource model according to the influence of the high-priority flow to obtain the constructed resource model.
Further, the adjusting the resource model to be adjusted according to the influence of the low-priority traffic to obtain the first resource model includes:
and deleting the part covered by the low-priority traffic and the target-priority traffic existing in the gate control list data to obtain the first resource model.
Further, the adjusting the first resource model according to the influence of the prediction mechanism to obtain a second resource model includes:
the method comprises the steps of analyzing whether a residual time window of a transmission time slot can meet the transmission of traffic;
when the flow transmission can be satisfied, continuing the transmission;
when the flow transmission can not be met, stopping the flow transmission and waiting for the next time window;
and obtaining a second resource model.
Further, the method for adjusting the second resource model according to the influence of the high-priority traffic to obtain the constructed resource model includes:
and deleting the part covered by the high-priority flow and the target-priority flow existing in the gate control list data to obtain the constructed resource model.
Further, a building block comprising:
s803, the component model building unit is used for building a component function of the time-sensitive network transmission node;
the function associates a data model curve of the incoming component model with a resource model curve and a data model curve of the outgoing component model with a resource model curve.
Further, a second integration module comprising:
and the method is used for integrating the abstract models of the single nodes according to the topological structure of the time sensitive network system to obtain the abstract model of the time sensitive network system.
The time-sensitive-network-system-based abstract model construction device provided by the embodiment of the present disclosure executes the time-sensitive-system-based abstract model construction method provided by the above embodiment, and will not be elaborated herein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known systems, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed system should not be interpreted to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the steps in the devices of the embodiments may be adaptively changed and disposed in one or more devices other than the embodiments. Steps or components in the embodiments may be combined into one step or component, and further, may be divided into a plurality of steps or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or steps of any system or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or steps are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software steps running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the system described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the step claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. An abstract model construction device based on a time-sensitive network system is characterized by comprising:
the building module is used for building a data model, a resource model and a component model of the time-sensitive network transmission node;
the first integration module is used for integrating the data model, the resource model and the component model to obtain an abstract model of a single transmission node of the time sensitive network;
and the second integration module is used for integrating the abstract model of the single transmission node to obtain the abstract model of the time-sensitive network system.
2. The apparatus of claim 1, wherein the building block comprises:
and the data model building unit is used for building the data model according to the data traffic of the time sensitive network transmission node.
3. The apparatus of claim 1, wherein the building block comprises:
the resource model building unit is used for analyzing the gate control list data of the time-sensitive network transmission node and generating a resource model to be adjusted;
the method is used for analyzing the influence of low-priority traffic, a prediction mechanism and high-priority traffic on a traffic transmission time slot;
and the resource model to be adjusted is adjusted according to the influence to obtain the constructed resource model.
4. The apparatus according to claim 3, wherein the means for adjusting the resource model to be adjusted according to the influence to obtain a constructed resource model includes:
the resource model to be adjusted is adjusted according to the influence of low-priority flow to obtain a first resource model;
the resource model is used for adjusting the first resource model according to the influence of a prediction mechanism to obtain a second resource model;
and the resource model is used for adjusting the second resource model according to the influence of the high-priority flow to obtain the constructed resource model.
5. The apparatus according to claim 4, wherein the means for adjusting the resource model to be adjusted according to the impact of low priority traffic to obtain a first resource model comprises:
and deleting the part covered by the low-priority traffic and the target-priority traffic existing in the gate control list data to obtain the first resource model.
6. The apparatus of claim 4, wherein the means for adjusting the first resource model according to the impact of the prediction mechanism to obtain a second resource model comprises:
the method comprises the steps of analyzing whether a residual time window of a transmission time slot can meet the transmission of traffic;
when the flow transmission can be satisfied, continuing the transmission;
when the flow transmission can not be met, stopping the flow transmission and waiting for the next time window;
and obtaining a second resource model.
7. The apparatus of claim 4, wherein the means for adjusting the second resource model according to the impact of the high-priority traffic to obtain a constructed resource model comprises:
and deleting the part covered by the high-priority flow and the target-priority flow existing in the gate control list data to obtain the constructed resource model.
8. The apparatus of claim 1, wherein the building block comprises:
the component model building unit is used for building a component function of the time-sensitive network transmission node;
the function associates data model curves and resource model curves that are passed into the component model and data model curves and resource model curves that are passed out of the component model.
9. The apparatus of claim 1, wherein the second integration module comprises:
and the abstract model of the single node is integrated according to the topological structure of the time-sensitive network system to obtain the abstract model of the time-sensitive network system.
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