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WO2018141435A1 - Procédé et dispositif d'attribution de ressources en appareils - Google Patents

Procédé et dispositif d'attribution de ressources en appareils Download PDF

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Publication number
WO2018141435A1
WO2018141435A1 PCT/EP2017/080724 EP2017080724W WO2018141435A1 WO 2018141435 A1 WO2018141435 A1 WO 2018141435A1 EP 2017080724 W EP2017080724 W EP 2017080724W WO 2018141435 A1 WO2018141435 A1 WO 2018141435A1
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WIPO (PCT)
Prior art keywords
tasks
distributed system
resources
task
devices
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Application number
PCT/EP2017/080724
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German (de)
English (en)
Inventor
Josep Soler Garrido
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Publication of WO2018141435A1 publication Critical patent/WO2018141435A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system

Definitions

  • the invention relates to a method and apparatus for allocating resources from distributed system devices to tasks executed by distributed system devices.
  • Distributed systems can include a variety of devices that have different resources and communicate with one another.
  • the resources of the distributed system include, for example, processor resources, memory resources or data transfer resources.
  • the various devices of the distributed system are thus limited in their local resources and are exposed to a dynamic workload.
  • the various devices or nodes of the system can locally execute tasks of a work process or workflow to use their local resources.
  • local sensor data can be processed which receive the various devices of the distributed system from associated data sources or sensors from local data streams.
  • the distributed system devices can perform various tasks, such as diagnostics, maintenance, monitoring, or other data processing tasks.
  • Workflows or working processes of the distributed Sys tems ⁇ comprise a plurality of tasks that are performed on different devices using multiple data streams.
  • the results of the various individual tasks can be combined or chained to extract results to identify different patterns or certain situations within the distributed system know ⁇ to it.
  • a control unit of the distributed system can initiate or carry out measures.
  • executing a work process or workflow advertising the assigned various tasks of the workflow data processing ⁇ resources of the distributed system.
  • the allocation of resources takes place dynamically in many applications. It is therefore an object of the present invention to provide a method and apparatus for allocating resources of wireless devices within a distributed system to tasks wel ⁇ che of devices of the distributed system are performed to establish, in which the allocation of resources is optimized.
  • the inventive method allows the allocation of the system resources of the distributed system in a robust and simple manner.
  • the other tasks that are related to the tasks that can be executed by the respective device are contained in at least one common workflow, which comprises a set of z associated tasks.
  • each unit of the distributed system performs the Loka ⁇ le optimization, when a resource allocation to each device is changed, or change the available locally on the device resources, or when the device an update at least one embodiment quality metric from a receives another device of the distributed system.
  • the predetermined optimization function has a global function of the entire distributed system.
  • the resources of a distributed system device include processor resources, memory resources and / or data transmission resources of the device.
  • the execution quality metrics indicate a quality with which a task is executed by the device.
  • the execution quality metrics have a sensitivity, an accuracy and / or an error rate.
  • a task that can be executed by a device can be executed in different operating modes of the device, which each claim a different set of resources of the device.
  • a set of permissible operating modes of a device is provided for each task.
  • the selection of resources of a device for executing a task by switching to another permissible operating mode of the respective device takes place.
  • the invention further provides, according to another aspect, a resource allocation device having the features specified in claim 13. Possible embodiments of the resource allocation according to the invention are specified in the subclaims.
  • the resource allocation device has a storage unit for storing the local execution quality metrics and the external resource allocation
  • the resource assigning apparatus further includes a storage unit for storage of all of brass by the device within a workflow from ⁇ executable tasks, ie which set of tasks T can perform the device locally.
  • the device stores which sets of tasks within the system are related to any task of the set of tasks and which other devices are capable of executing them.
  • the invention further provides, according to a further aspect, a distributed system having the features specified in claim 16.
  • the invention provides a distributed system with a plurality of devices, each of which has resources for processing tasks and each containing a Monashami ⁇ sungsvortechnische according to one aspect of the invention.
  • the resource allocation devices of the distributed system devices exchange
  • the devices of the distributed system field devices have each including a processor as ⁇ calculation unit, the data derived in particular from sensors, sensor data, in a workflow, the one or more Tasks includes, processes and transmits the processed data to a control unit of the distributed system.
  • FIG. 1 is a block diagram illustrating a possible exemplary embodiment of a device having a resource allocation device according to the invention
  • FIG. 2 is a flow chart illustrating an embodiment of a method of allocating device resources of a distributed system according to the invention
  • Fig. 3 is another block diagram illustrating a
  • FIG. 5 is a table of an exemplary list of event detection task operation modes that may be performed by a device
  • FIG. 6 is a graph for logically illustrating an exemplary implementation of various workflows on different distributed system devices; a functional diagram for representing an average workflow overhead as a function of a resource variance according to a simulation; a further functional diagram for representing an average workflow overhead as a function of a resource variance according to another simulation; a distribution of values of an expense or
  • a distributed system 1 comprises a plurality of devices 2-0, 2-1, 2-2 capable of communicating with each other.
  • the various devices 2-i may communicate with each other via a wireless or wired interface.
  • the devices 2-i are connected to a common bus of the system 1 in order to be able to exchange data or messages with one another.
  • the bus may be a serial or parallel data bus.
  • the devices are 2-i field devices that have data via a field device bus, in particular
  • Execution quality metrics can exchange with each other.
  • the various devices 2-i can also transmit processed data to an optionally existing common unit 3 of the distributed system 1.
  • the unit 3 may include one or more workflows and working process abar w ⁇ BEITEN.
  • no unit 3 is present and the system consists of devices 2, in particular of field devices.
  • the number of devices 2 and the number of layers may vary.
  • a first layer of devices 2 may be connected to sensors.
  • a second layer of devices 2 may serve as a data concentrator and unprocessed data from field devices. Rates 2 received.
  • Further units for example in a cloud, can collect and process data in a third layer.
  • each device 2-i can be connected to one or more data sources 4-i which supply data for data processing.
  • the device 2-0 of the distributed system 1 is connected to N different data sources 4-1, 4-2 ... 4-m in the illustrated example.
  • the data sources 4-i may include sensors that provide sensor data in different data streams DS to the device 2-0. Other data sources, such as memory units or the like, are also possible.
  • the data sources can also be formed by other devices 2.
  • a device 2 may send raw data or processed data from its sensors to other devices 2 for further data processing. These data streams can be transmitted via a network interface.
  • Each device 2-i has a ⁇ be restricted amount of locally available resources for data processing.
  • the device 2-0 has N different device resources 5-i.
  • This device resources are 5-i, for example, processor resources, memory resources and data transmission resources of the equipment 2.
  • the various devices 2-i of the, allocated system 1 each have a resource allocation ⁇ device 6 for allocating resources 5-i of the device to task t on, which are executed by devices of the distributed system 1 ⁇ .
  • a predetermined optimization function is provided, the external local execution quality metrics q d, the device of the self-executable tasks 2- i t, and
  • Execution quality metrics q d 'depend on other tasks t' which may be related to the tasks t executable by the respective device 2-i and executable by other devices of the distributed system 1.
  • the other tasks, which together ⁇ men conference with the locally executable by the respective unit 2-i tasks are preferably contained in a common workflow w, comprising a set of associated tasks.
  • each Distributed system device 1 also has relation data for each task executable by the device, which is a relationship between a local execution quality metric q d with which a task can be executed by the respective device and resources 5-i of the device. which are assigned to the respective task.
  • Execution quality metrics may depend on the resources of the device itself and, where appropriate, resources of the sensors or other device extensions that the device controls.
  • each resource allocating device 6 of a device 2-i within the distributed system 1 preferably comprises a receiving unit 6A, a calculating unit 6B and a transmitting unit 6C.
  • the Emp ⁇ capturing unit 6A and 6C, the transmitting unit can be integrated in a transmitter 6 de-receiving unit of the resource allocation device.
  • the receiving unit 6A of the resource allocating device 6 is adapted to receive execution quality metrics q d of tasks t from other devices 2 of the distributed system 1 executed by the other devices 2.
  • the device 2-0 communicates with two further devices 2-1, 2-2 of the distributed system 1 for exchanging execution quality metrics.
  • the receiving unit 6A of the device 2-0 receives a first execution quality metric q d i 'from the device 2-1 with respect to tasks executed by the device 2-1. Furthermore, the receiving unit 6A of the device 2-0 receives execution quality metrics q d 2 'from tasks of the further device 2-2 of the distributed system 1, which are executed by this other device 2-2.
  • the calculation unit 6B of the resource allocation device 6 is adapted to a local function from an upstream LF give ⁇ nen to be optimized optimization function (F) derive.
  • the local function LF preferably depends on the local Execution quality metrics q d, 2-i executable tasks of the equipment used.
  • the optimization function F is a global function of the entire distributed system 1.
  • the optimization function F can have a cost function to be minimized.
  • the computing unit 6B of the resource allocation device 6 is further adapted to perform a local optimization based on the derived local function LF and the relationship data present in the respective device 2-i.
  • the local optimization is carried out on the basis of submitte ⁇ th local function LF and stored relationship data of the respective device 2-i in consideration of the available resources 5 of the respective unit 2-i for selection of resources of the particular device which the affected task to the execution be assigned to.
  • the resource allocating device 6 further includes the sending unit 6C that is suitable for updating
  • the transmitting unit 6C of the device 2-0 transmits updated execution quality metrics q d o of each task t performed by the device 2- 0 by the device 2-0 using the resources 5 assigned to this task t 2-0, to the other two devices 2-1, 2-2 of the distributed system 1, wherein each of the two remaining devices 2-1, 2-2 are provided for the execution of another task, which may be related to one of the tasks executed by the device 2-0 or may belong to the same workflow w.
  • the resource allocating device 6 preferably has a storage unit for storing the local
  • Resource allocation device 6 preferably also has a further storage unit for storing all tasks t that can be executed by device 2-i within a workflow or workflow w. Further, the apparatus 2 stores are provided which sets or groups of tasks within the system with ir ⁇ vicinity of a task of the set of tasks in relationship, and what other devices of the system 1 are in a position they exit out the various resource allocation devices 6 of the various devices 2- i of the distributed system 1 exchange execution quality metrics among each other to increase the efficiency of resource usage within the distributed system 1.
  • the various devices 2-i of the distributed system 1 have field devices, each of which contains a processor as the calculation unit 6B, which processes data in a workflow w, which includes one or more tasks t, and then processes the processed data to the control unit 3 of the distributed system 1 transmits.
  • the processor and the calculation unit 6B of the unit 2-i for example, processed Sensorda ⁇ th derived from sensors 4-i, which are connected to the device in question 2-i, or derived from other system devices.
  • FIG. 2 shows a simple flow chart for illustrating an embodiment of the method according to the invention for allocating resources from devices 2-i of a distributed system.
  • tems 1 to tasks t which are executed by devices 2 of the distributed system 1, according to another aspect of the invention.
  • Each device 2-i of the distributed system 1 can execute the main steps shown schematically in FIG.
  • a first step Sl the relevant device 2-i receives from other devices of the system 1
  • Execution quality metrics q d 'of tasks executed by the other devices 2 of the system 1. It can be sufficient if a single execution quality metric of another device has changed.
  • a local function which depends on the local execution quality metrics q d of the tasks t that can be executed by the respective device 2-i, is first determined by a predetermined optimization function F to be optimized on the basis of the external execution quality metrics q d ' derived the respective device 2-i from the other devices of the system 1 derived.
  • the to be optimized is first determined by a predetermined optimization function F to be optimized on the basis of the external execution quality metrics q d ' derived the respective device 2-i from the other devices of the system 1 derived.
  • Function F is in a possible embodiment a global function of the entire distributed system 1, in particular a global cost function of the distributed system 1.
  • a further step S3 it is a local optimization ⁇ step on the basis of the derived at step S2 local function and on the basis of relationship data of the respective device 2-i, taking into account the available resources of the respective device 2-i for the selection of resources of the respective device 2-i, which are assigned to the relevant task for their execution.
  • the device has two for each of the unit 2 executable task from relationship ⁇ data.
  • the stored relationship data gives a relationship between a local execution quality metric q d , with which a task can be executed by the relevant device 2, and the resources 5 of the device 2, which are assigned to the respective task for this purpose.
  • the relationship data can be stored in a memory unit of the device 2.
  • Execution quality metrics q d each task performed by the respective device 2-i executed by the respective device 2-i using the resources 5 of the device 2 assigned to this task to all other devices 2 of the system 1 which are used to execute a other task are provided.
  • the other task may be related to one of the tasks executed by the respective device 2-i or belong to a same workflow w.
  • Each device 2 of the distributed system 1 may, in one possible embodiment, perform the local optimization in step S3 as soon as a resource allocation at the respective device 2-i is changed and / or a resource availability in the device 2-i changes.
  • the exchange of execution quality metrics may be continuous in one possible embodiment. Alternatively, the replacement of the
  • steps S1, S4 are triggered when a particular event occurs.
  • Execution quality metrics indicate a quality with which a task by the particular device 2-i of the system is guided out ⁇ . 1
  • the exchanged execution quality metrics have a sensitivity, an accuracy and / or an error rate with which a task is executed by the device 2.
  • An executable by a device 2 of the system 1 task which is executable in different operating modes m of the device 2, each claiming a different set of resources 5 of the device 2 and / or resources of device extensions, in particular of sensors or data sources.
  • a set of permissible operating modes of the relevant device 2-i is provided for each task. The selection of resources 5 of a device 2-i to perform a task in one possible embodiment by switching to another permissible operating mode of the device in question 2. In the event that the local resources of a device 2 are changed, the
  • Fig. 3 shows another schematic diagram for Erläute ⁇ tion of the operation of a distributed system 1, which comprises multiple devices 2-i, the tasks of a workflow w can perform ⁇ .
  • an iterative distributed allocation of resources takes place by means of a distributed resource management.
  • the individual distributed resources ⁇ management by the different devices 2 of the distributed system 1 has the advantage that a local optimization can be done by the different devices 2 of the distributed system. 1 This local optimization based on a derived local function LF can be performed particularly quickly as soon as the resource allocation situation within the distributed system 1 changes.
  • Each device 2-i of the distributed system 1 has information indicating the topology of the workflows or workflows w. Depending ⁇ the unit 2-i has information or data which is ⁇ ben which tasks are executable by the device concerned. 2
  • the device 2 has for each workflow w on Informa ⁇ tion data which group or set of devices which perform tasks 2, which belongs to the relevant workflow w.
  • each device 2 has information data of a function F associated with these workflows w.
  • the function F is a function of the local
  • Each node or device 2 of the distributed system 1 stores data of execution quality metrics which the device 2 has received from other peer devices 2 of the system 1.
  • the unit 2 in question performs a local optimization on the basis of the derived Loka ⁇ len function by LF, wherein values of the
  • Execution quality metrics are fixed by the other devices 2 of the distributed system 1 or kept constant.
  • an operating mode m can be selected for each task of the device 2. Further, by using the vorlie ⁇ constricting mapping a set or group of data source can configurations of data sources 4-i and values of
  • Execution quality metrics are read from a lookup table.
  • the output of the local optimization step is the optimal resource assigned to each task that can be executed locally as well as related qualities or quality metrics.
  • the relationship or mapping between the two is known and used by the device. However, the relationship can be expressed in different ways. For example, a list of operating modes (quality resources) may be provided. Alternatively, the quality of a task or its execution quality metric may be defined as a function of the resources used for it.
  • the device 2-i can configure associated data sources 4-i (dashed line in FIG. 1) to obtain data corresponding to the individual tasks of the operating mode.
  • the device 2 transmits its updated
  • the resource allocation method according to the invention provides a faster iterative update of data source configurations in response to changes in device resources, enabling or disabling workflows, changing workflow priorities, device errors, or data source failures.
  • Execution quality metrics used for its own local optimization may for example be effected by by adding time stamps or sequence numbers to the updated execution quality metrics, which are exchanged between different nodes or devices 2 of the distributed system 1 and preferably additionally defines a predefi ⁇ ned priority between the various devices 2 which all devices 2 of the distributed system 1 is known. For example, if a first node 2A broadcasts an execution quality metric update with the N + 1 sequence, but receives another update from another higher priority peer device 2B that specifies the sequence metadata, that the quality metric sequence N of device 2A has been used, then the node or device 2A can, based on its known priorities, immediately broadcast a new update N + 2 based on the last execution quality metrics of the device 2B. The node or device 2B of the distributed system 1 ignores the update N + 1 of device 2A because it is not based on the last execution quality metrics of the device 2B.
  • the resource allocating device 6 of the device 2 may perform various optimization procedures.
  • the device 2 leads to Timing by the installation of the system 1 or dauerhaf ⁇ th changes an initial or initial optimization.
  • an individual resource management or an individual resource allocation can take place without feedback from the other devices 2 of the distributed system 1.
  • the iterative distributed resource allocation or resource allocation can then be carried out with the aid of the method according to the invention.
  • Fig. 4 shows a schematic representation for a game stick at ⁇ workflow w, which can be carried out by the inventive distributed system 1.
  • the workflow w is used to detect or detect a system event S based on the combination of six independent events or events S 1 to S 6 via logic operations. Each event can be signaled by an associated task t1 to t6, which are executed on a device 2.
  • a singletechnischsproofsmet- rik is selected for the particular workflow W or selected.
  • the execution quality metric in the illustrated example is the sensitivity s c for detecting a complex event c.
  • This embodiment quality metric s c (up S6 Sl) for the various individual events by the individual sensitivities (El to E6) expressed ⁇ the.
  • Devices 2 in particular field devices, are limited in terms of their CPU resources. The sensitivity with which event detection can be achieved depends on the analytical model and sensor configuration. Each configuration requires a corresponding set of device ⁇ resources of the unit 2.
  • the table shown in FIG. 5 shows an example of how different five operating modes m for each task defined who can ⁇ . The table belongs to a task t that runs on a particular device 2. The same task t, if executable on different devices of system 1, has a different table for each of these devices. For example ⁇ the task requires more resources of the device in question to achieve the given quality.
  • Each operation mode ⁇ m may for example be defined for executing the task by a differing ⁇ Chen algorithm.
  • FIG. 6 shows a deployment in which three workflows w, as shown in FIG. 4, can be deployed or deployed.
  • the three workflows w as shown in FIG. 4, can be deployed or deployed.
  • the example shown the
  • a resource allocation mechanism serves to allocate resources 5 to tasks on each device 2.
  • a set or a group of different workflows w may be provided, which each comprise a set or a group of associated tasks, as well as a topology which indicates how the tasks are linked to one another.
  • a set of operating modes zuläs ⁇ sigen m can be provided which contain the necessary configuration for the data source 4 and the associated necessary device resources.
  • An operating mode m represents a particular way in which a task t is to be solved, with an associated set of resources and qualities achieved.
  • An operating mode m may be defined by a particular configuration at data sources 4 or by other parameters, such as a particular algorithm for solving the task.
  • an optionally multidimensional execution quality metric may be indicated as a function of the resources used.
  • a cost function may be specified as a function of the execution quality metrics.
  • For each device 2 of the system 1 comprises a set of verheg ⁇ cash resources for executing the task t. This set of available resources may change over time, depending on the current workload of the device 2. The workload of the device 2 is measurable in one possible embodiment.
  • the method according to the invention can be based on a distributed optimization algorithm.
  • Each device 2 of the distributed system 1 can individually decide which proportion or percentage of its available resources 5 it assigns to the various individual tasks.
  • Each device 2 is capable of exchanging information with other devices of the system 1 to iteratively achieve a global optimum for the entire distributed system 1.
  • the devices 2 inform their peer devices 2 of the quality with which they perform their own tasks t. This information is used by each device 2 in the next local optimization step to allocate resources to locally executed tasks. In this way a high probability is achieved friendliness to achieve a global optimum, while at the same time keeps the complexity and quantity of the information exchanged ⁇ or data minimal.
  • Each device 2 for example each field device, lists a set of tasks based on its locally available sensor data and other required data based on its limited resources, for example, the device 2 through a network of other distributed system devices 1 receives.
  • Each device 2-i of the system 1 can, for example, execute T d tasks.
  • Each unit 2-i may play control a set of S d local data sources or sensors at ⁇ , to generate data for data processing.
  • a set of R device resource classes (for example, CPU, memory, or network bandwidth ...) is the second device of all devices distributed system 1, which perform tasks t, together.
  • the amount of each of these different resources for the data processing tasks may vary for each device 2.
  • the amount of the resource type r available on a device d may be indicated as R d , r .
  • r di ir is the amount of resource r assigned to a task j on a device d.
  • resource r must meet the following condition for each type of resource r follows that each device of the system 1 d at each time point: r d, j, r ⁇ Rd.r Vd, r: d E ⁇ 1 ... D], E ⁇ r 1 ... R ⁇
  • the results of all tasks that are performed on all devices 2 of the Sys tems ⁇ 1 can be combined in w workflows that are able to extract results in terms of the distributed system.
  • Different tasks t can be combined to a workflow w on a higher layer or layer. This combination does not necessarily have to be done at the device level.
  • the data sources or sensors 4 can support different configurations with different attributes, for example with a certain sampling rate or with a certain accuracy or precision.
  • the various devices 2-i of the system 1 are capable of configuring the data sources 4 and controlling their attributes. Typically, an execution quality metric is assigned to an analytic task.
  • the execution quality metric includes, for example, sensitivity, specificity, or accuracy.
  • the quality of execution of a task t depends on the data model or algorithm used and on the configuration of the data sources 4, for example on a sampling rate of the data sources 4.
  • Tasks that process data with a higher level of execution ⁇ quality generally require more device resources 5. Therefore, it is possible to define the quality of a task or the execution quality as a function of applied comparable to them resources r.
  • the quality q as a function of resources r can of course be continuous. In most cases the quality is discretized.
  • a finite set of operating modes m di as follows: m d, j e i m d, j, l- m d , j, Mdj ⁇ ⁇
  • An execution quality metric for a workflow w as a function of the qualities of the associated associated tasks. This can be formulated as a cost function, for example, with higher values indicating a ge ⁇ ringere quality.
  • a workflow Wi with a set of related tasks ti the effort and depend on the Kos ⁇ ten belonging to the workflow w, from the
  • FIGS. 7, 8 show simulation results in a functional diagram. In this case, a simulation or an Implemen ⁇ tion is shown, as shown in FIG. 6. There, three different workflows or workflows wi, W2, W3 are shown, each workflow w based on the logical combination of six tasks t, as shown in Fig. 4. These tasks are distributed over nine different devices 2, each performing two tasks t. The various tasks have five different operating modes
  • the total amount of CPU resources of each device 2 can be modeled as an independent random variable with a Gaussian distribution ⁇ ( ⁇ , ⁇ 2 ).
  • the variance ⁇ 2 ranges from [0 to 2] depending on the respective simulation point.
  • Fig. 7 shows the average cost (average
  • False negative rate FNR for different methods.
  • the false negative rate FNR approaches zero, as expected, because the devices 2 have more device resources than are required to perform all tasks in the higher mode of operation.
  • the performance or performance of the individual Resource management decreases faster because there kei ⁇ nen way or no way to adjust the allocation of resources and resource allocation to the global tasks to the lack of resources in some devices with 2 resources to other equipment 2 of the system 1 are available to compensate.
  • FIG. 8 shows a diagram for illustrating a further simulation.
  • FIG. 8 shows an average workload as a function of a resource variance.
  • the variance ⁇ 2 ranges from [0 to 2] depending on the simulation point.
  • FIG. 7 shows an average value of the cost function.
  • the distribution of the cost values for covering different methods ⁇ NEN is also relevant. This is illustrated in the diagram of FIG. 9 for the second simulation.
  • Fig. 9 shows the distribution of the values of the cost function for different methods used in the second simulation.
  • FIG. 9 shows the results of the resource allocation method according to the invention.
  • FIG. 9 III shows the results of an optimal resource allocation.
  • FIG. 9 shows the results for an individual resource management.
  • the inventive method has a low complexity. Therefore, it is able to respond very quickly to variations or changes in resource conditions and system conditions.
  • the method according to the invention does not require a central coordination unit.
  • the method according to the invention scales well with the size of the distributed system 1.
  • the inventiveness proper procedure provides a nearly optimal allocation of resources and resource allocation within the distributed system 1, that is an almost optimal utilization of standing for Availability checked ⁇ supply resources of the distributed system 1 for the execution of workflows w.
  • the inventive method is suited in particular for a distributed system 1 with several ⁇ ren distributed devices 2, in particular field devices.

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Abstract

L'invention concerne un procédé d'attribution de ressources en appareils (2) d'un système réparti (1), chaque appareil (2) du système réparti (1) attribuant ses tâches aux ressources (5) disponibles localement en fonction des métriques de qualité d'exécution externes, qui ont reçu l'appareil (2) parmi les autres appareils (2) du système réparti (1).
PCT/EP2017/080724 2017-01-31 2017-11-28 Procédé et dispositif d'attribution de ressources en appareils WO2018141435A1 (fr)

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DE102019129969A1 (de) * 2019-11-06 2021-05-06 Endress+Hauser SE+Co. KG System zur Ressourcenverwaltung in einer Anlage der Automatisierungstechnik
US12164283B2 (en) 2019-08-30 2024-12-10 Phoenix Contact Gmbh & Co. Kg Method and industrial controller for the synchronized calling of a function block in a control program having OPC UA

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