CN110515595B - Resource modeling and management method of avionics distributed management system - Google Patents
Resource modeling and management method of avionics distributed management system Download PDFInfo
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- G06F9/46—Multiprogramming arrangements
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- G06F9/5083—Techniques for rebalancing the load in a distributed system
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Abstract
The invention discloses a resource modeling and management method of an avionic distributed management system, which comprises the steps of carrying out static system modeling design and dynamic system design under an operating system conforming to ARINC653 avionic cloud operating system standard; the static system modeling design specifically comprises the following steps: setting abstract properties of a static system, including partition description information, partition scheduling information and communication information among partitions of an operating system, configuring XML format files by using the abstract properties, compiling and programming processes to create a configured static operating system; the dynamic system design is specifically as follows: setting abstract attributes of the LightVM, including partition identification, partition memory limitation, partition mirror image and the like, configuring XML format files by using the abstract attributes, compiling and configuring parameters to create a configured LightVM system. By the method, the tasks in the distributed cloud platform system are executed on time and efficiently, and meanwhile, the high utilization rate of the whole system to all resources is ensured.
Description
Technical Field
The invention belongs to the technical field of avionics, and relates to a resource modeling and management method of an avionics distributed management system.
Background
The avionics system is shown in figure 1 and is mainly composed of three parts. Ground base station: and manually sending a command such as cooperative striking and the like to the man-machine. The method comprises the following steps: the system is internally provided with 2 general calculation processing modules and 2 general storage modules, and the internal buses are used for communication, so that the system can be communicated with two unmanned aerial vehicles through a general high-performance network module to realize high-performance network connection to distribute scheduling tasks. Unmanned aerial vehicle: and 1 general image processing module is arranged in the ground base station, and is matched with a man-machine through high-performance network connection communication to finish the task instruction of the ground base station.
The execution flow of the task instruction of the avionics end system is as follows: firstly, a ground base station manually sends a task instruction to a man-machine for execution (taking a cooperative striking instruction as an example below) through a wireless communication circuit, and after the man-machine receives the task instruction, generating a series of subtasks to be distributed and executed comprises the following steps: obtain the target position, perform the striking, etc. Then the unmanned aerial vehicle distributes subtasks (obtaining target positions) related to the unmanned aerial vehicle general image processing module to the unmanned aerial vehicle through high-performance network connection to monitor unmanned aerial vehicle execution, the unmanned aerial vehicle returns a result to the unmanned aerial vehicle after finishing image processing, and the unmanned aerial vehicle uses the general calculation processing module and the general storage module to calculate and store the result of subtask execution. The general purpose computing processing module and the general purpose memory module communicate using a bus. After all the subtasks (obtaining the target position and implementing the striking) are distributed and executed, the state of the man-machine and the unmanned plane and the instruction execution result are sent back to the ground base station by the man-machine, the ground base station knows that the instruction execution is completed after receiving the message, the instruction execution result is stored, and the next instruction is calculated to run in a reciprocating mode.
The existing avionic system cannot automatically optimize a ground base station to send an execution instruction to an organic machine through a wireless communication circuit, the organic machine distributes execution tasks to an unmanned aerial vehicle to execute the resource allocation of the completion tasks, the scheduling mode is simple, the on-time efficient execution of the tasks cannot be guaranteed, the high utilization rate of the whole system to all resources is guaranteed, and load balancing among all system partitions cannot be achieved.
Disclosure of Invention
Aiming at the problems, the invention provides a resource modeling and management method of an avionics distributed management system, which can ensure that tasks in a distributed cloud platform system are executed on time and efficiently, and simultaneously ensure the high utilization rate of the whole system to all resources.
The invention provides a resource modeling and management method of an avionic distributed management system, which is used for carrying out static system modeling design and dynamic system design under an operating system conforming to ARINC653 avionic cloud operating system standard; the static system modeling design specifically comprises the following steps: setting abstract properties of a static system, including partition description information, partition scheduling information and communication information among partitions of an operating system, configuring XML format files by using the abstract properties, compiling and programming processes to create a configured static operating system; the dynamic system design is specifically as follows: setting abstract attributes of the LightVM, including partition identification, partition memory limitation, partition image, partition type, partition starting type, partition name, partition CPU limitation and partition CPU weight, configuring XML format file by the abstract attributes, compiling and configuring parameters to create a configured LightVM system.
Further, the partition description information includes the number, name, importance level, function entry, message queue, code segment and memory allocation of the data segment of the partition. The zone scheduling information includes the frame time of the entire 653 operating system and the time window occupied by each zone function. The partition communication information includes communication channels and communication address conventions between partitions.
Further, the static operating system is created by automatically executing a series of compilation and programming flows of the static system starting with the python createcstry.
Furthermore, the partition information analysis detection and the monitoring function in the running process of the static system are provided, and the partition information of the static system, the scheduling information of each partition and the scheduling information of the whole partition operating system are presented to a user through analyzing the xml configuration file of the static system.
Further, when the static system is running, monitoring information of the static system in real time is displayed, wherein the monitoring information comprises a system state, a partition state and a state of a transaction called.
Further, a configured LightVM system is created by executing the python script of lightvm_create.
Further, the system also provides functions of real-time monitoring and automatic monitoring of the dynamic LightVM system, and the real-time resource state and configuration information of the LightVM are automatically output every 10 seconds after 'g' and a custom time period are input.
The invention provides a resource modeling and management method of an avionics distributed management system, which designs three methods, namely an abstract modeling method for static system resource allocation in the avionics system, an abstract modeling method for dynamic system resource allocation in the avionics system, and an online state monitoring and combined optimization allocation method for the avionics distributed system resource based on the resource abstract modeling method, wherein the combined actions of the three methods produce the following beneficial effects: the system XML format file which is correspondingly statically or dynamically configured is automatically generated by the avionic system according to the type of the requirement of the user task. After generating the system XML format file with corresponding configuration, the avionics system distributed management software analyzes the XML format file and creates the system partition with corresponding configuration in the background of the working node. When the working nodes of the system partitions are connected to the network of the host, the working nodes can be added into the distributed cluster created by the host. The host may then execute instructions to create a service to enable load balancing of one or more tasks (services) to be distributed to certain or designated nodes for execution. And finally, running monitoring software on the host or the working node to realize real-time monitoring and automatic monitoring of the task resource state of the system.
Drawings
FIG. 1 is a diagram of an avionics end system architecture in accordance with the background of the invention;
FIG. 2 is a diagram of static system abstraction properties according to the present invention;
FIG. 3 is a diagram of a static system xml file format of the present invention;
FIG. 4 is a static system xml parsing code of the present invention;
FIG. 5 is a diagram illustrating a static system partition information resolution according to the present invention;
FIG. 6 is a diagram illustrating the analysis of static system partition scheduling information and system scheduling information according to the present invention;
FIG. 7 is a diagram of static system runtime monitoring information of the present invention;
FIG. 8 is a diagram of the abstract properties of a dynamic LightVM of the present invention;
FIG. 9 is a diagram of a dynamic LightVM system XML file format of the present invention;
FIG. 10 is a LightVM_create.py code of the present invention;
FIG. 11 is a diagram of an avionics system management software real-time monitoring and automated monitoring interface in accordance with an embodiment of the present invention;
FIG. 12 is a diagram illustrating static system partition information in accordance with an embodiment of the present invention;
FIG. 13 is a diagram of static system partition scheduling information and system scheduling information according to an embodiment of the present invention;
FIG. 14 is a diagram illustrating dynamic system partition information in accordance with an embodiment of the present invention;
FIG. 15 is a diagram of a distributed management task distribution map for a Swarm cluster in accordance with an embodiment of the present invention;
fig. 16 is a view of a swart cluster distributed management task monitoring diagram in an embodiment of the present invention.
Detailed Description
One specific embodiment of the invention is:
the invention designs an innovative resource modeling and management method. Based on the method, an avionics system administrator can generate a correspondingly configured system partition by configuring open653.Xml or LightVM. Xml, can build a cluster with a host connected with a plurality of arm architecture development boards and then distribute scheduling tasks in a distributed load balancing manner, and monitors task resource states and node resource states in real time or automatically through avionics system management software.
The improvement is as follows: static system modeling design
In the static system modeling design, an operating system conforming to the ARINC653 avionics cloud operating system standard is adopted to realize the static system part of the avionics system. The corresponding abstract attributes include: partition description information, partition scheduling information and inter-partition communication information of an operating system. The partition description information includes the number, name, importance level, function entry, message queue, code segment, memory allocation of data segment, etc. The partition scheduling information includes the frame time of the entire 653 operating system and the time window occupied by each partition function. The partition communication information includes communication channels and communication address conventions among the partitions. The abstract properties are shown in fig. 2.
A detailed description of the abstract properties of a static system is shown in the following table:
configuration XML format file of static system in avionics system as shown in fig. 3, avionics system administrators can create a configured static operating system by modifying configuration parameters in the static system XML configuration file and then automatically performing a series of compilation and programming processes of the static system starting with python createscripts.
Avionics system management software also provides partition information resolution detection and runtime monitoring functions for static systems whose xml configuration file resolution-related code is shown in fig. 4. By analyzing the configuration file, the avionics management system can clearly present the partition information of the static system, the scheduling information of each partition, and the scheduling information of the entire partition operating system to the user, as shown in fig. 5 and 6.
In addition, when the static system is running, the system state, the partition state, the state of the transaction called and the like can be known in real time by providing real-time monitoring information of the static system running, as shown in fig. 7.
And (2) improvement: dynamic system design
Since the LightVM is established after the system profile configuration completes startup, abstracting the LightVM includes the following attributes: partition identification, partition memory limitations, partition mirroring, partition type, partition startup type, partition name, partition CPU limitations, and partition CPU weights, with abstract properties as shown in FIG. 8. A detailed description of the abstract properties of the LightVM is shown in the following table:
avionics system dynamic LightVM configuration XML format file as shown in fig. 9, an avionics system administrator can create a configured LightVM system by modifying configuration parameters in the LightVM system XML file and then executing the python script of lightvm_create.
The XML configuration file parsing process of the LightVM system is shown in fig. 10. In addition to dynamic LightVM system profile creation, avionics system management software also provides the functionality to monitor the dynamic LightVM system in real-time as well as automatically. The avionics system administrator can input 'g' and a custom time period, and the avionics system monitoring software can automatically output real-time resource status and configuration information of the LightVM every 10 seconds. The specific monitoring content is shown in fig. 11 and the following table:
the invention is further elucidated below with reference to the accompanying drawings.
(1) Configuration files for configuring static and dynamic systems and execution
The user opens and modifies the system configuration parameters in the XML standard format file of the open653 and LightVM, and then executes the python 653_create. Py and python lightvm_create. Py in the command line, and the final execution results are shown in fig. 12, 13, and 14.
(2) Avionics system Swarm cluster publication tasks and monitoring
A Swarm cluster is first created on the host using command lines and then added on multiple development boards using command lines. Then, the system administrator can issue tasks to the plurality of development boards and monitor the task execution state through the command line on the host, and finally issue task results and task execution state monitoring results are shown in fig. 15 and 16.
Claims (1)
1. A resource modeling and management method of an avionic distributed management system is characterized in that static system modeling design, dynamic system design, on-line state monitoring and combined optimization configuration are carried out under an operating system conforming to ARINC653 avionic cloud operating system standard;
the static system modeling design specifically comprises the following steps: the abstract modeling method for static system resource allocation in an avionic system comprises the steps of configuring XML format files by abstract attributes, compiling and programming flows to create a configured static operating system, wherein the abstract modeling method comprises the steps of partition description information, partition scheduling information and communication information among partitions of the operating system;
the dynamic system design is specifically as follows: the abstract modeling method for dynamic system resource allocation in an avionic system sets abstract attributes of a LightVM (virtual machine), including partition identification, partition memory restriction, partition mirroring, partition type, partition starting type, partition name, partition CPU restriction and partition CPU weight:
Executing the python script of LightVM_create.py to create a configured LightVM system;
the online state monitoring and the combined optimization configuration are specifically as follows: besides the creation of the configuration file of the dynamic LightVM system, the avionics system management software also provides the functions of real-time monitoring and automatic monitoring of the dynamic LightVM system, and after the avionics system manager inputs 'g' and a custom time period, the avionics system manager automatically outputs the real-time resource state and configuration information of the LightVM every 10 seconds, wherein the monitoring content is as follows:
the partition description information comprises the number, the name, the importance level, the function entry, the message queue, the code section and the memory allocation of the data section of the partition;
the zone scheduling information includes the frame time of the whole 653 operating system and the time window occupied by each zone function;
the partition communication information comprises communication channels and communication address conventions among partitions;
the static operating system is created by automatically executing a series of compiling and programming flows of the static system starting from the python createcstructs. Py src/command;
the partition information analysis detection and the monitoring function in the running process of the static system are also provided, and the partition information of the static system, the scheduling information of each partition and the scheduling information of the whole partition operating system are presented to a user through analyzing the xml configuration file of the static system;
and when the static system operates, displaying monitoring information of the static system in real time, wherein the monitoring information comprises a system state, a partition state and a state of a transaction called.
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