CN111431731A - Apparatus and method for acquiring system configuration of distributed system - Google Patents
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Abstract
The invention relates to a method for obtaining one or more optimized system configurations of a distributed system described by a technical model, wherein the technical model is defined by processing nodes (21) and communication connections (22) between the processing nodes (21), wherein the system configurations specify an assignment of functions (11) of a functional model (1) to the respective processing nodes (21) of the technical model (2) and an assignment of communication connections (22) between communication paths (12) and processing nodes (21), wherein the communication paths (12) for the assignment of functions are specified by the requirement for communication of a function with a function assigned in a further processing node (21).
Description
Technical Field
The invention relates to a method for distributing functions of a functional model in a distributed system having a plurality of networked processing nodes. The invention relates in particular to measures for allocating functions to the individual processing nodes in terms of optimal resource utilization.
Background
In designing distributed systems, the problem of assigning the functions of a functional model to the various processing nodes is known. This allocation is typically performed manually. In this case, configurations are produced which do not fully exploit the possibilities of the distributed system and which furthermore do not allow a degree of freedom in the design of the processing nodes of the distributed system and their communication connections.
For example, a plurality of controllers are provided in a motor vehicle, which are connected to one another by suitable communication links. The controllers include, for example, motor controllers, transmission controllers, driver assistance systems, and various control assemblies for distributed vehicle functions. The controller and control assembly represent a processing node in which the functionality of the functional model is implemented as software, so that the controller can be assigned and perform the functionality implemented in software.
In such distributed systems, functions are usually assigned to processing nodes which are located in the vicinity of or are assigned to the means to be controlled by the relevant function. In addition to this, functions can be intuitively assigned to the respective processing nodes by experts. This may lead to the following situation, namely: the available hardware capacity cannot be optimally (too much or too little) utilized and, if necessary, a higher bandwidth of the communication connection than is required in the allocation of other functions must be provided for data communication between the processing nodes. The more complex or extensive the distributed system, the more difficult it is to intuitively assign the functionality of the functional model to the various processing nodes of the distributed system.
In the solutions known from the prior art, the optimization is limited to local observations that do not take into account all possibilities for resource sharing. Solutions that take into account the resources of the communication connection in addition to the resources of the processing nodes focus on finding the shortest communication path, which in any case does not lead to a suboptimal system utilization when viewed globally.
Disclosure of Invention
According to the invention, a method for compiling an optimized system configuration of a distributed system described by a technical model according to claim 1 and a device according to the accompanying claims are provided.
Further embodiments are specified in the dependent claims.
According to a first aspect, a method is provided for obtaining one or more optimized system configurations of a distributed system described by a technical model, wherein the technical model is defined by processing nodes and communication connections between the processing nodes, wherein the system configurations describe an assignment of functions of the functional model to the respective processing nodes of the technical model and an assignment of communication paths to the communication connections between the processing nodes, wherein the communication paths for the assignment of functions are described by the communication requirement of a function to a function assigned in a further processing node, wherein the following steps are performed:
by passing
o assigning the functions of the functional model to the processing nodes of the technical model, respectively, in a function assignment;
determining a communication path according to the function assignment;
o assigning the communication paths to the communication connections of the technical model in a communication assignment, respectively, for obtaining a system configuration;
to obtain a plurality of system configurations for the system,
-obtaining, for each of the obtained system configurations, a processing cost of the allocation of functions of all processing nodes and a communication cost of the allocation of communications between all processing nodes; and is
-determining one or more optimized system configurations by selection from the obtained system configurations in dependence of the processing costs and the communication costs.
The above method is conceived in that the functions of the functional model are allocated to the processing nodes of the distributed system described by the technical model by means of a suitable optimization method for obtaining an optimized system configuration that utilizes the available resources of the processing nodes and of the communication connections between the processing nodes according to an optimization goal. The optimization is performed in two steps by allowing the functions of the functional model to be assigned to the processing nodes and by allowing communication paths between the functions of the functional model to be assigned to communication connections between the processing nodes, wherein a pareto-optimal (pareto-optimal) solution is obtained in an iterative manner.
In this way, a large number of possible system configurations can be evaluated with regard to an optimal system utilization for obtaining an advantageous solution for the system configuration. Furthermore, the total cost based on the optimization method can also be taken into account for the cost of the communication connection. All possible schemes for transmitting data or information between two processing nodes are evaluated.
With the above-described solution, it is also possible in particular to find an optimized system configuration which takes into account longer communication paths which meet the requirements and reduce the costs of the overall system design.
Furthermore, the functions of the functional model can each be assigned a processing requirement parameter, wherein the functions of the functional model are each assigned to a processing node of the technical model in the function assignment in such a way that the processing requirement parameters assigned to the functions of the respective processing node accordingly do not violate boundary conditions predefined by the respective processing efficiency parameter.
In particular, the processing requirement parameters for each function can be predefined for one or more resource classes, wherein a resource class, in particular for a processing node, may comprise one or more of the following classes: computing power, storage capacity, direct access to sensor data, classification according to a level of safety in function, interface to actuators and energy requirements.
Furthermore, the communication connections of the functional model can each be assigned a communication requirement parameter which is assigned in the communication assignment to a communication path of a communication connection comprising the technical model, such that the communication requirement parameters of all communication connections assigned to the processing node accordingly do not violate boundary conditions predefined by the respective communication efficiency parameter.
Communication requirement parameters can be specified for each communication path between two functions in different processing nodes, in particular for one or more resource categories, wherein the resource categories, in particular for the communication paths, comprise one or more of the following categories: bandwidth, minimum delay, media type (optical/electrical/wireless), topology, protocol (synchronous/asynchronous), and transmission direction.
Provision can be made for the determination of one or more system configurations to be carried out as a function of processing and communication costs by: a total cost is determined from the processing cost and the communication cost and one or more system configurations are determined from the total cost.
Alternatively, the determination of one or more optimized system configurations can be implemented in terms of processing and communication costs by: a pareto set of system configurations is selected from a plurality of acquired system configurations.
In particular, the processing cost in one of the processing nodes can be obtained from the processing cost function and from the degree of realization or utilization of one or more of the resource categories for the processing nodes, and/or the communication cost for one of the communication connections can be obtained from the communication cost function and from the degree of realization or utilization of one or more of the resource categories for the communication connections. If the resource class is based on capacity, the resource class has a utilization, whereas for discrete resource classes, such as for example a classification according to functional safety, the resource class has a degree of implementation.
According to one specific embodiment, the utilization of one or more of the resource classes for the processing nodes can be determined accordingly by the difference between the sum of the processing requirement parameters of the functions assigned to the relevant processing nodes for the relevant resource class and the predefined processing efficiency parameter of the relevant processing nodes, and/or the utilization of one or more of the resource classes for the communication connections can be determined accordingly by the difference between the sum of the communication requirement parameters of the communication paths assigned to the relevant communication connections for the relevant resource class and the predefined communication efficiency parameter of the relevant communication connections.
Furthermore, at least one of the processing cost functions and/or at least one of the communication cost functions can correspond to a bathtub-shaped cost function, which represents costs by utilization, so that high costs are assigned to low and high utilizations and low costs are assigned to medium utilizations. The use of a bathtub-shaped cost function advantageously enables optimized allocation scenarios in which the use of processing nodes avoids disadvantageous hardware configurations in which the processing nodes experience too low utilization and are therefore not effectively utilized or experience too high utilization and thus lose scalability and flexibility.
Furthermore, one or more additional processing nodes and one or more communication connections can be augmented to the technical model used to compile one of the system configurations. After the end of the iterative method steps, unused processing nodes and/or communication nodes can be removed from the technical model.
According to one embodiment, the allocation of functions of the function model to function allocations and/or the allocation of communication connections to communication allocations can be optimized, in particular, by the use of genetic algorithms.
According to another aspect, a method for creating a distributed system is provided, wherein the distributed system is created with a system configuration, which is determined as an optimized system configuration according to the above method.
According to a further aspect, an apparatus is provided for obtaining one or more optimized system configurations of a distributed system described by a technical model, wherein the technical model is defined by processing nodes and communication connections between the processing nodes, wherein the system configurations describe an assignment of functions of a functional model to the respective processing nodes of the technical model and an assignment of communication paths to the communication connections between the processing nodes, wherein the communication paths for the assignment of functions are described by the communication requirement of a function to a function assigned in a further processing node, wherein the apparatus is designed to:
by passing
o assigning the functions of the functional model to the processing nodes of the technical model, respectively, in a function assignment;
determining a communication path according to the function assignment;
o assigning the communication paths to the communication connections of the technical model in a communication assignment, respectively, for obtaining a corresponding system configuration;
to obtain a plurality of system configurations for the system,
-for each of the obtained system configurations obtaining a processing cost of the allocation of functions of all processing nodes and a communication cost of the allocation of communications between all processing nodes; and is
-determining the one or more optimized system configurations by selection from the obtained system configurations in dependence of the processing costs and the communication costs.
Drawings
The embodiments are explained in detail below with the aid of the figures. Wherein:
FIG. 1 shows a schematic diagram of the assignment of functions to processing nodes of a distributed system;
FIG. 2 shows a flow chart for illustrating a method for finding an optimized system configuration for a distributed system with a given set of functions; and is
Figure 3 shows a schematic of a bathtub type cost function.
Detailed Description
A method for finding a system configuration is described below, in which the functions of a functional model are assigned to the individual processing nodes of a distributed system described by a technical model, and communication paths are assigned by means of communication connections between the processing nodes.
To this end, fig. 1 schematically illustrates the underlying problem. It comprises a functional model 1 with a set of interrelated functions 11. These functions 11 are designed at least partially such that they exchange data with one another via a communication relationship 12.
Furthermore, a technical model 2 is provided, which describes a distributed system with individual processing nodes 21, which can be connected to one another via a communication connection 22. The processing node 21 can be a programmable controller or assembly, to which it is possible to not assign any function 11 of the functional model 1 or to which one, more or all of its functions are assigned or which is able to perform these functions, respectively. A communication connection 22 can be provided between at least a part of said processing nodes 21.
The processing nodes 21 and the communication connections 22 between two respective processing nodes 21 are each assigned a processing efficiency parameter V L P1, V L P2, V L P3 … or a communication efficiency parameter K L P, K L P2, K L P3 …, the processing efficiency parameters V L P1, V L P2, V L P3 … for the processing nodes 21 can relate to at least one of the following resource classes of computing power, storage capacity, direct access to sensor data, interface to actuators, classification according to a functional security class, further discrete parameters and energy requirements, the communication efficiency parameter K L P42P 3 for the communication connections 221、KLP2、KLP3… can relate to at least one of the following resource categories: bandwidth, minimum delay, media type (optical/electrical/wireless), topology, protocol (synchronous/asynchronous), and transmission direction.
The system configuration 3, for which one of the processing nodes 21 is assigned to each of the functions 11 of the functional model 1, is generated by the optimization method described below. When implemented, the functions can then be implemented and executed in the respective processing nodes 21. Furthermore, communication paths between functions 11 implemented in different processing nodes 21 are defined.
The functions 11 of the functional model 1 use the processing requirement parameters VAP for the processing nodes 21 for the respective resource classes (indices), respectively1、VAP2、VAP3…, the processing requirement parameters specifying required computing power (demand for computing power), required storage capacity (storage demand), possible use of sensor dataThe processing requirement parameter VAP specifies for the relevant function and for each observed resource class the corresponding requirement for the operational capability (L eistongsf ä highkeit) of the processing node 21 to be allocated.
Furthermore, the communication requirement parameter KAP1、KAP2、KAP3… define requirements for communication between two (distributed) functions 11 implemented in different processing nodes 21, which requirements relate to data reception and data transmission from and to other processing nodes 21 for respective resource categories, like e.g. bandwidth, maximum allowed time delay, necessary transmission direction and, if necessary, other communication requirement parameters. Thus, the communication requirement parameter KAP for related functions and related resource classes1、KAP2、KAP3… each illustrate the corresponding communication requirements for the communication connection 22.
The method is explained in detail below with the aid of the flow chart of fig. 2. The method is provided with information about the configuration of a technical model 2, which depicts the output structure of the processing nodes 21 in the distributed system, and about the functional model 1 with a set of functions 11 to be implemented, in a suitable manner.
In step S1, the technical model 2 can be expanded first by: a processing node 21 is added that is the same as an already existing processing node or that differs from an existing processing node in one or more of the processing efficiency parameters. These additional processing nodes 21 can be connected with different types of additional communication connections 22 with one or more of the processing nodes 21 of the original technical model 2. The extent of the expansion depends on the problem and can also be completely eliminated. For example, the technical model 2 can be expanded by a number of processing nodes 21 which corresponds to approximately 30% to 100%, in particular 50%, of the number of processing nodes 21 of the original technical model 2. This results in an extended technical model which enables variable function allocation.
In the step ofIn S2, an assignment method is carried out, in which the individual functions 11 of the functional model 1 are assigned to the processing nodes 21. The allocation is carried out in such a way that the processing requirement parameter VAP for the processing in the processing node 21 being observed, which processing requirement parameter VAP passes through the function 11 to be allocated to the relevant processing node 211、VAP2、VAP3… are correspondingly lower than the respective processing efficiency parameters V L P of the assigned processing node 211、VLP2、VLP3… (constraints). That is, the functions to be allocated to the respective processing nodes 21 can be performed with the resources available in the processing nodes 21.
The method for utilizing search space reduction (Suchramureduezierung) in function allocation comprises the following steps: only such processing nodes that meet the requirements of the functions to be allocated are considered. Whether this is the case by processing the requirement parameter VAP1、VAP2、VAP3… and a process efficiency parameter V L P1、VLP2、VLP3… one example here would be the case where the allocation of functions is declared invalid as soon as the utilization of the processing node with respect to the relevant resource parameter exceeds 100%, but other arbitrarily chosen limits (90%, 120% …) can also be considered another example in this respect could be the case where functions for functional security with the ASI L C classification are allocated only to processing nodes with ASI L C or ASI L D, and not to ASI L a and ASI L B classifications.
Processing efficiency parameter V L P capable of being at processing node 21 for each function 111、VLP2、VLP3… or processing requirement parameter VAP1、VAP2、VAP3… process cost functions VK, VK for each of the process efficiency parameters or the process requirement parameters1、VK2、VK3…, the allocation of functions to the processing nodes 21 of the expanded technical model 2 is determined. The processing cost GVK is obtained here.
For the allocated number z of processing nodes, the number m of functions to be allocated, the processing cost GVK applies:
for each discrete resource parameter, for example, indicating a need for the performance of a processing node 21, only the following conditions apply:
for example, one possible processing cost function VKi for one or more of the resource classes is shown in FIG. 3. In this case, if the function 11 to be allocated to the processing node 21 with respect to the relevant resource class reaches a medium utilization N for the relevant resource, a minimum cost for implementing a plurality of functions in the processing node 21 with respect to the resource class results. The utilization N can be defined as the ratio of the sum of the resource requirements defined by the respective processing requirement parameter VAP of the function to be allocated with respect to the relevant resource class to the maximum resource availability indicated by the respective processing efficiency parameter for the relevant resource class. If the resource class is based on capacity, the resource class holds the utilization, while for discrete resource classes, like for example a classification according to functional security, the resource class holds the implementation degree. For example, the functions can each define a storage requirement as a processing requirement parameter, wherein the total storage requirement of the function determined by the accumulation of the individual storage requirements is evaluated with respect to the maximum available memory specified by the processing efficiency parameter of the relevant processing node.
A preferred processing cost function VK for one or more resource classes can be determined from a bathtub-shaped function that assigns a cost K to a utilization N. Here, the high costs for low and high utilization N and the low costs for resource utilization in medium utilization N are explained. For these medium utilizations N, particularly low costs of between 30% and 80%, preferably between 40% and 60%, can be assumed, for example.
By using a bathtub-shaped cost function, the utilization N of the processing nodes 21 for the resource class can be adjusted by the function 11 assigned to the relevant processing node 21 to a range which enables good resource utilization and a reservation of the capacity of the processing node 21.
The assignment method can be performed by genetic algorithms or other heuristic methods with the aim of minimizing the processing cost GVK.
Next, for the found function assignment of the function 11 to the processing node 21, a communication path between the two functions is determined which is composed of one or more communication connections 22. In order to simplify the subsequent optimization, a search space reduction can be performed in step S3 for finding possible communication paths 12 according to the allocation of functions and processing nodes, by means of which communication can be utilized according to the communication requirements between two distributed functions.
This significantly reduces the degree of freedom for assigning the required communication paths to the existing communication connections 22 of the technical model 2, so that the optimization speed can be significantly increased and the convergence can be improved.
In the following step S4, the requirements for the individual communication paths between the processing nodes 21 are each assigned to a communication connection 22. The communication paths between processing nodes 21 are created by the communication requirements of each of the functions with another function implemented in other processing nodes 21. The allocation is verified under given boundary conditions.
The allocation is carried out in such a way that the communication via the communication connection 22 passes through the phasesCommunication requirement parameter KAP of a communication path extending from a communication connection 221、KAP2、KAP3… meet the given boundary conditions accordingly, for example by all requirements below the observed communication connection 22 being passed through the corresponding communication efficiency parameter K L P1、KLP2、KLP3…, communication between two distributed functions 11 can be carried out only via a communication path consisting of one or more communication connections 22 if its communication efficiency parameter K L P satisfies the following condition:
The assignment of the communication paths 12 to the communication connections 22 can be carried out for the communication connections 22 or the communication requirement parameter KAP1、KAP2、KAP3.., efficiency parameter K L P1、KLP2、KLP3… communication cost function KK for each communication efficiency parameter1、KK2、KK3… on the basis of the measured signal. Where a communication cost GKK is determined.
The method is applicable to the following steps:
。
the communication cost function KK for one or more resource classes can be determined from a bathtub-shaped function which assigns the cost K to the utilization N of the communication connection 22. The high costs for resource utilization with small and high utilization N and the low costs for resource utilization in medium utilization N are explained here. For these medium utilizations, particularly low costs of between 30% and 80%, preferably between 40% and 60%, can be assumed, for example.
By using a bathtub-shaped cost function, the utilization of the resource-type communication link 22 can be adjusted to a range that enables efficient resource utilization and a reservation of the capacity of the communication link 22 for future expansion.
The assignment method for the communication connection 22 can be performed by an evolutionary algorithm or other heuristic method.
The respective cost shares relating to the used processing and communication resources are now obtained in an iterative manner for the different system configurations.
The total cost GK is acquired in step S5. The total cost GK is derived as a weighted sum of the processing cost GVK and the communication cost GKK: GK GVK + k GKK, where k is a predetermined weighting factor.
The system configuration thus obtained is saved if its assigned total cost is lower than the total cost of the previously stored system configuration, otherwise the previously stored system configuration is retained as the current system configuration.
Alternatively, the system configurations can be collected in a set of pareto-optimal solutions in the sense of multi-objective optimization.
The optimization steps S2 to S5 can be repeatedly performed. In this case, the system configuration, i.e. the assignment of functions 11 to processing nodes 21, is changed and communication paths via communication connections 22 are defined accordingly.
For this purpose, in step S6, an interruption condition is checked. Possible interrupt conditions for system optimization can include, for example, at least one of the following interrupt conditions:
-the calculation duration is reached;
the reduced variation of the total cost GK of the one or more optimization objectives is below a predefined threshold, in particular for a predefined number of successive iterations;
-a certain number of iterations is reached; and is
-a certain number of iterations during which the total cost is not further reduced;
a certain number of iterations during which no further pareto-optimal solution is found.
If the interrupt condition is met (alternative: yes), the method continues with step S7, otherwise it jumps back to step S2.
The obtained system configuration with the lowest total cost GK or the obtained system configuration of the pareto aggregate is adopted as the optimized system configuration.
Due to the extension of the technical model, it is possible that the processing node 21 remains unused, i.e. has no functions assigned to it. Furthermore, the communication connections can remain unused, i.e. no communication paths are allocated for these communication connections. In step S7, therefore, the unused processing nodes 21 and the unused communication connections 22 are now searched for and, if appropriate, removed from the obtained system configuration.
The above method enables an overall optimization of the system configuration taking into account the processing resources in the processing nodes and the communication resources of the communication connections between the processing nodes. Furthermore, an optimal solution can be found within the complex problem with a large number of functions of the functional model and a large number of also heterogeneous processing nodes, within which optimal solution human complexity can be detected.
Claims (17)
1. Method for obtaining one or more optimized system configurations of a distributed system described by a technical model, wherein the technical model is defined by processing nodes (21) and communication connections (22) between the processing nodes (21), wherein the system configurations specify an assignment of functions (11) of the functional model (1) to the individual processing nodes (21) of the technical model (2) and an assignment of communication connections (22) between communication paths (12) and processing nodes (21), wherein the communication paths (12) for function assignments are specified by communication requirements of one function (11) and one function assigned in a further processing node (21), wherein the following steps are carried out in an iterative manner:
by passing
o assigning (S3) the functions (11) of the functional model (1) to the processing nodes (21) of the technical model (2) in a function assignment, respectively;
o determining a communication path (12) from the function allocation;
o assigning (S4) the communication paths (12) to the communication connections (22) of the technical model (2) in a communication assignment, respectively, for obtaining a corresponding system configuration;
to obtain a plurality of system configurations for the system,
-obtaining, for each of the obtained system configurations, a processing cost (GVK) of the function allocation of all processing nodes (21) and a communication cost (GKK) of the communication allocation (GKK) between all processing nodes (21); and is
-determining (S5) one or more optimized system configurations by selection from the acquired system configurations according to the processing cost (GVK) and the communication cost (GKK).
2. Method according to claim 1, wherein the functions of the functional model (1) are each assigned a processing requirement parameter (VAP), wherein the functions (11) of the functional model (1) are each assigned to a processing node (21) of the technical model (2) in the function assignment in such a way that the processing requirement parameters (VAP) assigned to the functions (11) of the respective processing node (21) do not respectively violate boundary conditions predefined by the respective processing efficiency parameter (V L P).
3. Method according to claim 2, wherein the processing requirement parameter (VAP) of each function (11) is pre-given for one or more resource categories, wherein the resource categories, in particular for the processing nodes (21), comprise one or more of the following categories: computing power, storage capacity, direct access to sensor data, classification according to a level of safety in function, interface to actuators and energy requirements.
4. Method according to one of claims 1 to 3, wherein the communication connections (22) of the functional model (1) are each assigned a communication requirement parameter (KAP) which is assigned in the communication assignment to a communication path (12) formed by the communication connections (22) of the technical model (2) in such a way that the communication requirement parameters (KAP) of all the communication connections (22) assigned to a processing node (21) do not respectively violate a boundary condition predefined by the respective communication efficiency parameter (KAP).
5. Method according to claim 4, wherein the communication requirement parameters are pre-given for each communication path (12) between two functions (11) in different processing nodes (21) for one or more resource categories, wherein in particular the resource categories for the communication path (12) comprise one or more of the following categories: bandwidth, minimum delay, media type (optical/electrical/wireless), topology, protocol (synchronous/asynchronous), and transmission direction.
6. The method of any of claims 1 to 5, wherein the determining of the one or more system configurations is performed in dependence on a processing cost (GVK) and a communication cost (GKK) by: the total cost (GK) is determined from the processing cost (GVK) and the communication cost (GKK).
7. The method of any of claims 1 to 5, wherein the determination of the one or more optimized system configurations is performed in dependence on a processing cost (GVK) and a communication cost (GKK) by: a pareto set of system configurations is selected from a plurality of acquired system configurations.
8. The method according to any of claims 1 to 7, wherein the processing cost (GVKK) is obtained in one of the processing nodes (21) according to a processing cost function (VK) and according to a utilization of one or more resource classes for the processing node (21), and/or wherein the communication cost (GKK) for one of the communication connections is obtained according to a communication cost function (KK) and according to a utilization of one or more resource classes for the communication connection (22).
9. Method according to claim 8, wherein the utilization of one or more of the resource classes for the processing nodes (21) is respectively generated by the difference between the sum of the processing requirement parameters of the functions (11) assigned to the relevant processing node (21) for the relevant resource class and a predefined processing efficiency parameter (V L P) of the relevant processing node (21), and/or
Wherein the utilization of one or more of the resource classes for the communication connection (22) is generated by the difference between the sum of the communication requirement parameters (KAP) of the communication paths (12) assigned to the relevant communication connection (22) for the relevant resource class and the predefined communication efficiency parameter (KAP) of the relevant communication connection (22), respectively.
10. Method according to claim 8 or 9, wherein at least one of the processing cost functions (VK) and/or at least one of the communication cost functions (KK) corresponds to a bathtub-shaped cost function, which reflects the cost by utilizing the efficiency, so that a high cost is allocated to a low utilization and a high utilization and a low cost is allocated to a medium utilization.
11. Method according to any of claims 1 to 10, wherein the technical model (2) is augmented with one or more additional processing nodes (21) and one or more communication connections with respect to the additional processing nodes (21) for programming one of the system configurations, and wherein unused processing nodes (21) and/or communication connections (22) are removed from the technical model (2) after the end of the iterative method steps.
12. Method according to one of claims 1 to 11, wherein the assignment of functions (11) of the functional model (1) is optimized in function assignment and/or the assignment of communication connections (22) is optimized in communication assignment, in particular by the use of genetic algorithms.
13. Method for producing a distributed system, wherein the distributed system is compiled in accordance with a system configuration, which is determined as an optimized system configuration according to the method according to any one of claims 1 to 12.
14. The method of claim 12, wherein the distributed system is used.
15. Apparatus for obtaining one or more optimized system configurations of a distributed system described by a technical model (2), wherein the technical model (2) is defined by processing nodes (21) and communication connections between the processing nodes (21), wherein the system configurations describe an assignment of functions (11) of a functional model (1) to the respective processing nodes (21) of the technical model (2) and an assignment of communication connections (22) between communication paths (12) and the processing nodes (21), wherein a communication path (12) for function assignment is described by a communication requirement of a function (11) and a function (11) assigned in a further processing node (21), wherein the apparatus is configured to:
by passing
o assigning the functions (11) of the functional model (1) to the processing nodes (21) of the technical model (2) in a function assignment, respectively;
o determining a communication path (12) from the function allocation;
o assigning the communication paths (12) to the communication connections (22) of the technical model (2) in a communication assignment in each case for obtaining a corresponding system configuration;
to obtain a plurality of system configurations for the system,
-obtaining, for each of the obtained system configurations, a processing cost (GVK) assigned to the functions of all processing nodes (21) and a communication cost (GKK) assigned to the communication between all processing nodes (21); and is
-determining the one or more optimized system configurations by selection from the obtained system configurations in dependence of the processing cost (GVK) and the communication cost (GKK).
16. Computer program which is set up for carrying out all the steps of the method according to any one of claims 1 to 12.
17. Electronic storage medium having stored thereon a computer program according to claim 16.
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| CN101398871A (en) * | 1995-02-13 | 2009-04-01 | 英特特拉斯特技术公司 | Systems and methods for secure transaction management and electronic rights protection |
| US20160110474A1 (en) * | 2014-10-20 | 2016-04-21 | Korea Institute Of Science And Technology Information | Method and apparatus for distributing graph data in distributed computing environment |
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