CN115858316A - Networked software system reliability modeling simulation method based on multiple agents - Google Patents
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Abstract
The invention discloses a multi-Agent-based networking software system reliability modeling simulation method, which comprises the following steps: identifying services and connectors of the networked software system, abstracting the services into nodes, and abstracting the connectors into edges; respectively establishing individual agents for each service and each connecting piece; obtaining a network topological relation among the agents according to the static structure of each Agent, the executed software service and the interactive relation between the agents and other agents, and establishing a reliability model of a networked software system; and simulating and evaluating the fault propagation condition and the fault repair capability of the reliability model of the networked software system and the system reliability when different tasks are executed by using the simulation platform to obtain the quality and reliability level of the networked software system. The invention can dynamically simulate the structure and the behavior of the networked software system, discover software defects and weak links, and promote the improvement of the quality and the reliability level of the networked software system.
Description
Technical Field
The invention relates to the technical field of software reliability engineering, in particular to a multi-Agent-based networking software system reliability modeling simulation method.
Background
With the continuous development of the digitization, the informatization and the intellectualization of weapon equipment in China, the tactical internet is a battlefield information infrastructure for guaranteeing the maneuvering operation of digital troops in modern war and is a foundation and a condition for realizing the perception of battlefield situation information to a command and decision department and the command and control information to a fire fighting platform. The reliability of the tactical internet directly influences the result of battle, and a military command control system as a typical networked software system is an important component in the tactical internet, and the reliability of the military command control system is the most direct part of the reliability of an information-based war system.
The networked software system is a complex system which is hierarchical, open and nonlinear in function, behavior and structure. The single subsystem has autonomy and responsiveness, namely relative independence, initiative and self-adaptability, and the capability of sensing external operation and use environments and providing useful information for system evolution; the multiple subsystems have the cooperativity and the generalization, namely the interconnection, intercommunication, cooperation and alliance of the multiple subsystems, and the capability of dynamically adjusting functions, structures and behaviors according to requirements, and finally the networked system with ordered structures in space, time, behaviors and functions can be formed in a self-organizing way.
The reliability of the networked software system mainly comprises a research method based on state analysis, a path and test data. The state-based reliability model represents the system structure and component transfer of the system through a control flow graph, the future behavior condition of the system is generally independent of the past behavior due to component independence, so that the transfer between software components has Markov characteristics, and the reliability is analyzed and calculated by using the Markov theory; the reliability is evaluated according to all possible execution paths and relevant frequencies of the system based on the reliability model of the paths; the reliability model based on the test is based on failure data of components in the system, a software reliability growth model is applied, the system structure of a networked software system is not considered, and meanwhile, the test result is influenced by different test environments and methods.
Still foreign scholars propose a structural-based component composition reliability calculation method, and the complexity of reliability calculation is reduced by applying component combination, but analysis and acquisition of a networked software structure are not considered. And the students introduce monitoring and feedback to realize the reliability evaluation of networked software, but the calculation process is relatively complex and lacks effective formal analysis modeling and system reliability calculation. In consideration of the fact that more intensive researches such as component failure correlation, fault-tolerant configuration and the like are carried out, a software reliability model in a wider sense is explained, but the obvious characteristics of networked software reliability analysis are not shown. Domestic research is only limited to focus on reliability assessment and improvement based on a state model, and extensive and intensive research is not yet developed.
Through the research result analysis, the reliability is difficult to accurately calculate based on the test method, the method is influenced by system operation and test environment, and the result accuracy is not high; the reliability analysis and calculation method based on the structure analysis sets the system structure information and the transition probability to be fixed, which is not in line with the actual situation of networked software, lacks further research on the acquisition of the structure and the calculation of the transition probability, and does not consider the influence of the network environment from the calculation method. In summary, the existing architecture model construction method mainly aims at single-machine software, cannot describe the dynamic evolution characteristics of the architecture and behavior of a networked software system, and needs to research a new modeling simulation method and a new construction mechanism. The problems restrict the reliability analysis, test and evaluation work of a networked software system, and influence the performance improvement of the existing equipment and the development of new-generation digital martial instrument equipment.
Therefore, how to provide a multi-Agent-based networked software system reliability modeling simulation method capable of remarkably improving the quality and reliability level of a networked software system is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of this, the invention provides a multi-Agent-based reliability modeling and simulation method for a networked software system, which can dynamically simulate the structure and the behavior of the networked software system, and analyze and evaluate the quality and the reliability level of the networked software system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a reliability modeling simulation method for a networked software system based on multiple agents comprises the following steps:
identifying services and connectors of the networked software system, abstracting the services into nodes, and abstracting the connectors into edges;
respectively establishing individual agents for each service and the connecting piece;
obtaining a network topological relation among the agents according to the static structure of each Agent, the executed software service and the interactive relation between the agents and other agents, and establishing a reliability model of a networked software system;
and simulating and evaluating the fault propagation condition and the fault repair capability of the reliability model of the networked software system and the system reliability when different tasks are executed by using the simulation platform to obtain the quality and reliability level of the networked software system.
Further, the Agent comprises: a service Agent and a connecting piece Agent;
the service Agent receives the required information from the external network environment, integrates or judges the received information according to the internal self state, and generates the description for modifying the self current state; then, matching information in the knowledge base to make a plan, and generating a series of actions acting on the external network environment according to the target;
the connecting piece Agent receives needed information from the external network environment, integrates the information, performs action reaction according to an internal rule base, and feeds back the processed information to the external network environment.
Further, the service Agent is modeled by a cognitive Agent, and the formal description of the service Agent is represented by a six-tuple:
Ser_Agent::＝<Seri,Ser_S,Ser_P,Ser_Per,Ser_Trans,Ser_KB,Ser_PS,Ser_GS,Ser_AS>
wherein, seri represents the serial number of the service Agent; ser _ S represents the state set of the self; ser _ P represents the sensing set; ser _ Per represents a perception function of E → Ser _ P, and maps the environment state as a perception input, wherein E represents an environment state set; ser _ Trans represents a decision function of Ser _ P + Ser _ S → Ser _ S, and changes of services are realized according to the current state of sensing input; ser _ KB represents the knowledge base, is the knowledge set of the service Agent, and at least comprises the knowledge of the operating environment, the action knowledge of the Agent, and the target knowledge of the service Agent; ser _ PS represents the planned set of services; ser _ GS represents a service target set; ser _ AS represents the action set of the service.
Further, the connecting piece Agent is modeled by using the structure of the reactive Agent, and the formal description of the connecting piece Agent is represented by a quadruple:
Con_Agent::＝<Coni,Con_set,Con_KB,Con_AS>
wherein Coni represents the serial number of the connecting piece Agent; con _ set represents a state set of the connecting piece Agent and at least comprises reliability and state information of the connecting piece; con _ KB represents the knowledge base of the connector Agent; con _ AS represents the action set of the connector Agent.
Further, the establishing process of the reliability model of the networked software system comprises the following steps:
establishing an environment operation model of a networked software system: the environment information required to be established comprises a networked software system use profile, software target use probability, hardware operation information and user actions;
establishing a generation model of a software target: any one Agent participating in the networked software system plays a role, and different software targets are issued by the same Agent or are issued by a plurality of agents together;
establishing a software service calling model: according to the requirements of the software target, each Agent determines whether to participate in the establishment of the software target according to the function and the capability of the Agent; when a software target appears or reaches an Agent, if the Agent can independently complete the software target, the software target is completed, and the next software target is entered; when a software target appears or reaches one Agent, if the Agent cannot finish the software target independently, starting to analyze other agents related to the Agent, and judging whether the software target can be solved together; if the two agents can be completed together, the agents form a set, and the next step is started to carry out a dynamic coordination process; if the software targets can not be completed together, abandoning the software targets and feeding back the incomplete software targets;
establishing a dynamic coordination relationship model: acquiring relevant information between the Agent set of the established software target and other agents, coordinating and interacting to jointly complete the software target;
establishing a reliability measurement model: measuring the specific number of software objects that the networked software system can accomplish for the user over a period of time;
and integrating the five models as the reliability model of the networked software system.
Further, the simulation platform simulates the number of software objects used by a user of the networked software system by using a random number generation method, specifically: in the running time of the simulation platform, dividing the simulation time into six time periods which respectively correspond to the actual running time of the networked software system; the software target quantity provided by each stage of the networked software system is divided into a small quantity, a medium quantity and a large quantity, and the software target quantity finished in unit time is unequal.
Further, the simulation platform simulates the software service quantity of the software service by using a Monte Carlo method; the method specifically comprises the following steps: software services are divided into three types, namely simple services, general services and complex services, the number of the software services contained in each type of services is different, the number of the software services contained in the complex services is the largest, and the number of the software services contained in the simple services is the smallest.
Further, the simulation model adopted by the simulation platform is as follows: a fault propagation simulation model and a reliability simulation model;
the fault propagation simulation model is modeled based on a virus propagation model, and single or cyclic simulation is carried out on the fault propagation process in the networked software system by setting the rule and the strength of fault propagation in the networked software system; in the simulation process, the parameters to be set at least comprise fault propagation probability, node fault detection probability and fault node recovery probability; after the simulation is finished, reliability related data of the networked software system are obtained, and the capability of the networked software system for bearing software faults and repairing is simulated and evaluated;
the reliability simulation model carries out single or circular simulation on the fault condition of the internal nodes of the software when the networked software system executes various different tasks according to the reliability definition, calculates the structural reliability and the task reliability of the software, and carries out simulation and evaluation on the reliability of the networked software system when executing different tasks in the using process.
Further, the simulation platform further includes: the system comprises a parameter setting module, a service simulation and state management module, a visual display module and a simulation process display module;
setting the type of the simulation model and partial index parameters in the simulation process through the parameter setting module;
simulating a development environment through the service simulation and state management module, and starting and controlling a corresponding simulation function of the selected simulation model according to set parameters;
checking the numerical value and the state of the key indexes of the networked software system reliability model of each simulation time point through the visual display module;
and displaying various parameters set for the selected simulation model, state change chart information of the reliability model of the networked software system in the simulation process, simulation result information when the simulation is finished and the state of the reliability model of the networked software system through the simulation process display module.
Further, after the single simulation of the fault propagation simulation model is finished, displaying the proportion of the nodes generating faults, the proportion of the nodes having fault resistance, the proportion of the nodes still possibly having faults and the value and the change condition of each simulation time point in the networked software system through the visual display module;
after the cyclic simulation of the reliability simulation model is finished, the structural reliability and the task reliability of the networked software system are displayed through the visual display module along with the development and change of simulation time after the simulation is started, and a task sequence when the simulation is executed, a node sequence of each task in the execution sequence and the fault condition of each task are output.
According to the technical scheme, compared with the prior art, the reliability modeling simulation method of the networked software system based on the multiple agents can model the structure and the reliability of the networked software system, perform simulation analysis, effectively perform defect propagation and failure mechanism analysis and reliability modeling simulation analysis of the networked software system, complete reliability analysis and evaluation work of the networked software system, complete discovery and exposure of software defects, and remarkably improve the quality and the reliability level of the networked software system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a reliability modeling simulation method of a multi-Agent-based networked software system provided by the invention;
FIG. 2 is a single simulation run interface for a reliability model of a networked software system using a fault propagation model in an embodiment of the present invention;
FIG. 3 is a configuration area interface in a single simulation process using a fault propagation model according to the present invention;
FIG. 4 is a data output area interface in a single simulation process using a fault propagation model according to the present invention;
FIG. 5 is a variation interface of various statistical indexes and partial statistical indexes in a single simulation process using a fault propagation model according to the present invention;
FIG. 6 is a state display interface of a reliability model of a networked software system in a single simulation process using a fault propagation model according to the present invention;
FIG. 7 is a single simulation result display interface provided by the present invention, employing a fault propagation model;
FIG. 8 is a schematic diagram of a cyclic simulation interface using a fault propagation model according to the present invention;
FIG. 9 is a display interface of a cycle simulation result using a fault propagation model according to the present invention;
FIG. 10 is a diagram illustrating a reliability model state display interface of a networked software system simulated by a reliability simulation model according to the present invention;
FIG. 11 is an operational interface simulated using a reliability simulation model according to the present invention;
FIG. 12 is a data output area interface simulated using a reliability simulation model according to the present invention;
FIG. 13 is a graph illustrating reliability variation using a reliability simulation model according to the present invention;
FIG. 14 is a simulation result display interface for simulation using a reliability simulation model according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention discloses a reliability modeling simulation method for a multi-Agent-based networked software system, including:
s1, identifying services and connecting pieces of a networked software system, abstracting the services into nodes, and abstracting the connecting pieces into edges;
respectively establishing individual agents for each service and each connecting piece;
s2, obtaining a network topological relation among the agents according to the static structure of each Agent, the executed software service and the interactive relation among other agents, and establishing a reliability model of the networked software system;
and S3, simulating and evaluating the fault propagation condition and the fault repair capability of the reliability model of the networked software system and the system reliability when different tasks are executed by using the simulation platform to obtain the quality and the reliability level of the reliability model of the networked software system.
The networked software system is composed of software services for realizing specific targets, service-oriented is the basis of reliability analysis, and from the perspective of users, the reliability of the networked software system is mainly measured from the continuity, reliability and the like of services provided by the system to the users.
The service-oriented architecture model is composed of a plurality of services and connectors. During modeling, services are abstracted into nodes, and connecting pieces are abstracted into edges, so that a topological network is formed. The subsystems are connected with each other by connecting pieces to form a complex network. Meanwhile, services and connecting pieces are abstracted into agents with different structural types for representation, so that nodes and edges of a complex network have intelligence, and effective analysis of a multi-Agent macroscopic mode is performed.
The invention takes the agents as the basic abstract unit of the target networked software system, adopts the related abstract technology, firstly establishes the Agent model of each individual body forming the networked software system, then assembles the individual agents by adopting the proper topological relation, and finally establishes the system model of the whole networked software system.
The behavior of each Agent can affect the reliability of the networked software system, and the states of other agents can be changed. By researching the static structure and the dynamic relation among different types of Agent entities, the operation mechanism and the reliability change of the networked software system can be analyzed.
Therefore, a networked software system can be used as a target system and decomposed into a system consisting of a plurality of individual services and connecting pieces, and meanwhile, the name of each individual Agent, the executed software service and the interaction relation with other agents can be determined.
The above steps will be further described below.
In S1, the Agent comprises: a service Agent and a connecting piece Agent;
(1) Modeling process of the service Agent:
the service Agent receives the required information from the external network environment, integrates or judges the received information according to the internal self state, and generates the description for modifying the self current state; and then, matching information in the knowledge base to make a plan, and generating a series of actions acting on the external network environment according to the target. The service Agent is modeled by a cognitive Agent, and the formal description of the service Agent is represented by a six-tuple:
Ser_Agent::＝<Seri,Ser_S,Ser_P,Ser_Per,Ser_Trans,Ser_KB,Ser_PS,Ser_GS,Ser_AS>
wherein, seri represents the serial number of the service Agent; ser _ S represents the state set of the self; ser _ P represents the sensing set; ser _ Per represents a perception function of E → Ser _ P, and maps the environment state as a perception input, wherein E represents an environment state set; ser _ Trans represents a decision function of Ser _ P + Ser _ S → Ser _ S, and changes of services are realized according to the current state of sensing input; ser _ KB represents the knowledge base, is the knowledge set of the service Agent, and at least comprises the knowledge of the operating environment, the action knowledge of the Agent, and the target knowledge of the service Agent; ser _ PS represents the planned set of services; ser _ GS represents a set of service objectives; ser _ AS represents the action set of the service.
(2) Modeling process of the connecting piece Agent:
the connecting piece Agent is modeled by using the structure of the reactive Agent, and the formal description of the connecting piece Agent is represented by a quadruple:
Con_Agent::＝<Coni,Con_set,Con_KB,Con_AS>
wherein Coni represents the serial number of the connecting piece Agent; con _ set represents a state set of the connecting piece Agent and at least comprises reliability and state information of the connecting piece; con _ KB represents the knowledge base of the connector Agent; con _ AS represents the action set of the connector Agent.
In one embodiment, in S2, reliability changes of the networked software system are analyzed by studying interaction between agents. Each Agent behavior can affect the reliability of the networked software system, and simultaneously change the states of other agents. In the embodiment of the invention, a modeling process of networked system software is mainly established from the aspects of operating environment, software service generation and service measurement, and the specific modeling process is as follows:
s21, establishing an environment operation model of the networked software system: the environment information required to be established comprises a networked software system use profile, software target use probability, hardware operation information and actions of a user.
S22, establishing a generation model of a software target: the software objects are distributed by the same Agent or by a plurality of agents together.
S23, establishing a software service calling model: according to the requirements of the software target, each Agent determines whether to participate in the establishment of the software target according to the function and the capability of the Agent; when a software target appears or reaches an Agent, if the Agent can independently finish the software target, finishing the software target and entering the next software target; when a software target appears or reaches an Agent, if the Agent cannot finish the software target independently, starting to analyze other agents related to the Agent, and judging whether the software target can be solved together; if the two agents can be completed together, the agents form a set, and the next step is started to carry out a dynamic coordination process; if co-completion is not possible, the software object is discarded and feedback is given that the software object is not completed.
S24, establishing a dynamic coordination relation model: and acquiring relevant information between the Agent set of the established software target and other agents, and coordinating and interacting to jointly complete the software target.
S25, establishing a reliability measurement model: the specific number of software objects that the networked software system can accomplish for a user in a period of time is measured.
And integrating the five models to serve as a reliability model of the networked software system.
In a specific embodiment, after the reliability model of the networked software system is constructed, the reliability model of the networked software system is simulated by using a simulation platform according to the following two aspects:
(1) The simulation platform simulates the number of software targets used by a user of the networked software system by using a random number generation method, and specifically comprises the following steps: in the running time of the simulation platform, dividing the simulation time into six time periods which respectively correspond to the actual running time of the networked software system; the software target quantity provided by each stage of the networked software system is divided into a small quantity, a medium quantity and a large quantity, and the software target quantity finished in unit time is unequal. Namely: in the time of executing a large amount of software targets, the number of the software targets concurrently executed by the system is large; the number of software objects concurrently executed by the system is small for a small amount of time to execute the software objects.
(2) The simulation platform also simulates the software service quantity of the software service by using a Monte Carlo method; the method specifically comprises the following steps: software services are divided into three types, namely simple services, general services and complex services, the number of the software services contained in each type of services is different, the number of the software services contained in the complex services is the largest, and the number of the software services contained in the simple services is the smallest. A system modeling and simulation which is lack of credibility is meaningless, and the credibility guarantee is more important for a complex simulation system.
Specifically, the reliability modeling of the networked software system is realized through a platform based on a NetLoco development environment, the multi-agent modeling can be performed on a networked software structure, multiple simulation models and strategies are designed to perform multi-angle reliability simulation analysis on the networked software system, and the integrated software platform is an integrated software platform for fault propagation analysis, reliability modeling simulation and evaluation of the networked software system. The method can perform fault propagation modeling simulation and reliability modeling simulation on the networked software system based on a NetLogio platform multi-Agent technology to obtain the fault propagation condition and the repair capability of the networked software system, the structural reliability of the system and the change condition of the task reliability, and evaluate and predict the quality and reliability level of the target networked software system. The platform mainly uses two types of simulation models, namely a fault propagation simulation model and a reliability simulation model, and comprises the following specific steps:
(1) The fault propagation simulation model is modeled based on a virus propagation model, and single or cyclic simulation is carried out on the fault propagation process in the networked software system by setting the rule and the strength of fault propagation in the networked software system; in the simulation process, the parameters to be set at least comprise fault propagation probability, node fault detection probability and fault node recovery probability; and after the simulation is finished, reliability related data of the networked software system is obtained, and the capability of the networked software system for bearing software faults and repairing the software faults is simulated and evaluated.
(2) When the reliability simulation model executes various different tasks on the networked software system according to the reliability definition, the fault condition of the internal nodes of the software is subjected to single or cycle simulation, the structural reliability and the task reliability of the software are calculated, and the reliability degree of the networked software system when executing different tasks in the using process is simulated and evaluated.
In other embodiments, the simulation platform further comprises: the system comprises a parameter setting module, a service simulation and state management module, a visual display module and a simulation process display module;
and setting the type of the simulation model and partial index parameters in the simulation process through a parameter setting module.
And simulating the development environment through the service simulation and state management module, and starting and controlling the corresponding simulation function of the selected simulation model according to the set parameters.
And checking the numerical value and the state of the key indexes of the networked software system reliability model of each simulation time point through a visual display module. After the single simulation of the fault propagation simulation model is finished, the node proportion of a fault generated in the networked software system, the node proportion with fault resistance, the node proportion which is still possible to have the fault, and the value and the change condition of each simulation time point are displayed through the visual display module. After the cyclic simulation of the reliability simulation model is finished, the development change conditions of the structure reliability and the task reliability of the networked software system along with the simulation time after the simulation is started are displayed through the visual display module, and the task sequence when the simulation is executed, the node sequence of each task in the execution sequence and the fault condition of each task are output.
And displaying various parameters set for the selected simulation model, state change chart information of the reliability model of the networked software system in the simulation process, simulation result information when the simulation is finished and the state of the reliability model of the networked software system by using a simulation process display module.
The modeling simulation method of the present invention is further described below with specific examples.
The method provided by the invention is used for commanding the networked software system to complete case application and implementation aiming at a certain type of weapon system, analyzing the connection relation between nodes according to the structural topological graph of the networked software system, constructing the structural model and the reliability simulation model of the networked software system, performing simulation analysis by using the fault propagation model and the software reliability model, completing software reliability analysis and evaluation, and verifying the rationality and feasibility of the proposed method.
Example software profiles
The weapon system comprises a camp command, a mechanical step connection, an assault connection, a mortar connection, an air defense connection and a guarantee squad, is provided with complete equipment such as information reconnaissance, command control, technical guarantee and the like, and can realize full-process automatic command from target acquisition, information processing and transmission, command decision and weapon equipment control. The command networked software system can be deployed on corresponding seat computers of a camp command vehicle, an auxiliary camp command vehicle, a mortar connection command vehicle, an air defense connection command vehicle, a motor step connection command vehicle, an assault connection command vehicle, a reconnaissance vehicle and a target indication radar, different business functions are started according to a uniform configuration strategy and different software of user roles, and the fighting requirements of command equipment and reconnaissance equipment at all levels are met.
(II) fault propagation simulation analysis
1. Single-pass simulation using fault propagation model
(1) The simulation platform is opened, and after the menu bar selects the required simulation model, the lower main interface is switched to the corresponding parameter setting interface (left side) and the system state interface (right side), as shown in fig. 2. Firstly, a fault propagation model is selected, and various built-in model parameters of the model are set on a parameter setting interface. And the right system state interface is initially displayed as an initial state before the reliability model of the networked software system is simulated, and is used for displaying the reliability model structure of the networked software system.
After the model parameters are set, clicking a button of a simulation-performing class can open the NetLolo model and start simulation, executing single simulation of the fault propagation model, clicking the simulation-performing class, and after 10-20s, opening a NetLolo model interface and automatically starting simulation.
(2) The upper left is a model arrangement area, and as shown in fig. 3, the model parameters set by the model parameter setting section and the simulation function buttons included in the model can be seen. In general, the model can be automatically simulated after being opened, and if the model needs to be simulated again, the 'setup' button can be clicked to initialize the model, and then the corresponding simulation model button is clicked to simulate the model.
(3) The lower left is the data output area, as shown in fig. 4. The part outputs various information of the simulation process, and in the single simulation process of the fault propagation model, the area outputs the node name of the injection fault at the beginning of the simulation and the node name in the fault state in each simulation time step system.
(4) The middle of the interface is a second partial model data output region, as shown in fig. 5. The main output of the part is various statistical indexes in the model simulation process and the change condition of part of the statistical indexes. In the single simulation process of the fault propagation model, the output of the region is the number of fault nodes, the proportion of the fault nodes, the number of resistant nodes, the proportion of the resistant nodes, the number of the nodes which can generate faults and the proportion of the nodes which can generate faults. In the lower statistical chart, the node occupation ratios of the three types of analogy change along with the progress of the simulation process, and the simulation is seen to be finished after the proportion of the failed node is reduced to 0.
(5) The portion to the right of the interface is a system state presentation area, as shown in FIG. 6. The current state of the simulation system can be displayed in the area, for example, single simulation of the fault propagation model is taken as an example, after the simulation is finished, green nodes in the reliability model of the networked software system are nodes with fault resistance, blue nodes can still have faults, and if red nodes appear, the nodes are fault nodes.
(6) After the simulation is completed, clicking a button of outputting the simulation result on the main interface, and popping up a simulation result display interface on the right side of the main interface, as shown in fig. 7. The statistical chart in the simulation process is generally reproduced above the simulation result display interface, but the data display is clearer, and the statistical chart can also perform functions such as amplification and reduction, information display and the like. In the single simulation process of the fault propagation model, the data in the statistical graph is the change condition of the proportion of the three state nodes in the system. The lower part is the specific information display of each node in the upper statistical graph, and the state change condition of each simulation time step system can be checked in the table by adjusting the scroll bar. The above is the usage flow of the networked software system in the simulation platform, and the usage flow in different models is similar, but the input, output and functions of each part are different.
2. Loop simulation using fault propagation model
Similar to single simulation, a fault propagation model is still selected on a parameter setting interface (left side) and a system state interface (right side), and various built-in model parameters of the model are set. After the model parameters are set, clicking a button of a simulation-performing class can open the NetLolo model and start simulation, and when the cyclic simulation is performed, clicking a simulation-performing class can open a NetLolo model interface and automatically start the simulation after 10-20s, so that the finished interface is shown in FIG. 8.
In the cyclic simulation process of the fault propagation model, the output of the data output area is the node name of the injection fault when the simulation is started, and the time step from the injection of the fault to the completion of the maintenance and the real time of each round of simulation are calculated, so that the real time required by each time step is calculated on average. The statistical chart shows the change of the average repair time of the system along with the increase of the cycle number, and the simulation is finished after the simulation of the specified cycle number is finished. Clicking a button of "output simulation result" of the corresponding function on the main interface can pop up a simulation result display interface on the right side of the main interface, as shown in fig. 9.
In the cyclic simulation process of the fault propagation model, the data in the statistical graph is the change condition of the average repair time of the system, and the state change condition of each simulation time step system can be checked in the table by adjusting the scroll bar.
3. Software model reliability simulation analysis
The reliability modeling simulation platform of the networked software system is opened, the reliability simulation model is selected in the menu bar, and the main interface becomes a model parameter setting interface and a system state display interface of the reliability simulation model, as shown in fig. 10. In the parameter setting interface, various model parameters built in the model can be set, such as the reliability of various nodes of various organizations, the judgment standard of task failure, and the like. And the right system state interface is initially displayed as the initial state before the simulation of the reliability model of the networked software system and is used for displaying the reliability model structure of the networked software system.
(2) After the model parameters are set, the NetLogo model can be opened and simulation can be started by clicking a button of a simulation class, and the finished interface is shown in fig. 11.
(3) In the cyclic simulation process of the reliability simulation model, the data output area outputs the number of nodes participating in task execution, the number of nodes which have faults when executing the tasks, the total number of executed tasks and the number of fault tasks in the whole simulation process, as shown in fig. 12, and the structural reliability and the task reliability of the reliability model of the networked software system are calculated accordingly.
(4) The middle part is a simulation result output area of the second part, a task sequence which is executed in the simulation process, a node name of the executed task and the node fault condition are output, after the execution of each task is finished, an execution summary of the task is output, wherein the execution summary comprises the number of nodes, the number of fault nodes, the name of the fault node, the execution time of the task and the simulation step number included in the execution of the task, and the time required by single-step simulation is calculated according to the execution summary. The statistical chart on the lower side shows how the system structure reliability and the task reliability change as the simulation progresses, and as shown in fig. 13, after the simulation for the specified number of tasks is executed, the simulation is finished.
(5) After the simulation is completed, clicking a button of 'output simulation result' on the main interface, and popping up a simulation result display interface on the right side of the main interface, as shown in fig. 14. In the circulation simulation process of the reliability simulation model, the data in the statistical chart is the change condition of the reliability model structure reliability and the task reliability of the networked software system. The lower part is the specific information display of each node in the upper statistical graph, and the reliability change condition of each simulation time step system can be checked in the table by adjusting the scroll bar. Shown at the bottom is the networked software system reliability model state at the end of the simulation and the simulation process description above.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A reliability modeling simulation method of a networked software system based on multiple agents is characterized by comprising the following steps:
identifying services and connectors of the networked software system, abstracting the services into nodes, and abstracting the connectors into edges;
respectively establishing individual agents for each service and the connecting piece;
obtaining a network topological relation among the agents according to the static structure of each Agent, the executed software service and the interactive relation between the agents and other agents, and establishing a reliability model of a networked software system;
and simulating and evaluating the fault propagation condition and the fault repair capability of the reliability model of the networked software system and the system reliability when different tasks are executed by using the simulation platform to obtain the quality and reliability level of the networked software system.
2. The method for modeling and simulating reliability of the multi-Agent-based networked software system according to claim 1, wherein the Agent comprises: a service Agent and a connecting piece Agent;
the service Agent receives the required information from the external network environment, integrates or judges the received information according to the internal self state, and generates the description for modifying the self current state; then, matching information in the knowledge base to make a plan, and generating a series of actions acting on the external network environment according to the target;
the connecting piece Agent receives needed information from the external network environment, integrates the information, performs action reaction according to an internal rule base, and feeds back the processed information to the external network environment.
3. The multi-Agent-based networked software system reliability modeling simulation method according to claim 2, wherein the service Agent is modeled by a cognitive Agent, and the formal description of the service Agent is represented by a six-tuple:
Ser_Agent::＝<Seri,Ser_S,Ser_P,Ser_Per,Ser_Trans,Ser_KB,Ser_PS,Ser_GS,Ser_AS>
wherein, seri represents the serial number of the service Agent; ser _ S represents the state set of the self; ser _ P represents the perception set; ser _ Per represents a perception function of E → Ser _ P, and maps the environment state as a perception input, wherein E represents an environment state set; ser _ Trans represents a decision function of Ser _ P + Ser _ S → Ser _ S, and changes of services are realized according to the current state of sensing input; ser _ KB represents the knowledge base, is the knowledge set of the service Agent, and at least comprises the knowledge of the operating environment, the action knowledge of the Agent, and the target knowledge of the service Agent; ser _ PS represents the planned set of services; ser _ GS represents a service target set; ser _ AS represents the action set of the service.
4. The multi-Agent-based networked software system reliability modeling simulation method according to claim 2, wherein the connecting piece Agent is modeled by using a structure of a reactive Agent, and a formal description of the connecting piece Agent is represented by a quadruple:
Con_Agent::＝<Coni,Con_set,Con_KB,Con_AS>
wherein Coni represents the serial number of the connecting piece Agent; con _ set represents a state set of the connecting piece Agent and at least comprises reliability and state information of the connecting piece; con _ KB represents the knowledge base of the connector Agent; con _ AS represents the action set of the connector Agent.
5. The method for modeling and simulating the reliability of the networked software system based on the multiple agents according to claim 1, wherein the process for establishing the reliability model of the networked software system comprises the following steps:
establishing an environment operation model of a networked software system: the environment information required to be established comprises a networked software system use profile, software target use probability, hardware operation information and user actions;
establishing a generation model of a software target: any Agent participating in the networked software system plays a role, and different software targets are issued by the same Agent or a plurality of agents together;
establishing a software service calling model: according to the requirements of the software target, each Agent determines whether to participate in the establishment of the software target according to the function and the capability of the Agent; when a software target appears or reaches an Agent, if the Agent can independently complete the software target, the software target is completed, and the next software target is entered; when a software target appears or reaches one Agent, if the Agent cannot finish the software target independently, starting to analyze other agents related to the Agent, and judging whether the software target can be solved together; if the two agents can be completed together, the agents form a set, and the next step is started to carry out a dynamic coordination process; if the software targets can not be completed together, abandoning the software targets and feeding back the incomplete software targets;
establishing a dynamic coordination relationship model: acquiring relevant information between the Agent set of the established software target and other agents, coordinating and interacting to jointly complete the software target;
establishing a reliability measurement model: measuring the specific number of software objects that the networked software system can accomplish for the user over a period of time;
and integrating the five models as the reliability model of the networked software system.
6. The method for modeling and simulating the reliability of the networked software system based on the multiple agents according to claim 1, wherein the simulation platform simulates the number of software objects used by a user of the networked software system by using a random number generation method, and specifically comprises the following steps: in the running time of the simulation platform, dividing the simulation time into six time periods which respectively correspond to the actual running time of the networked software system; the software target number provided by each stage of the networked software system is divided into a small quantity, a medium quantity and a large quantity, and the software target number completed in unit time is unequal.
7. The multi-Agent-based networked software system reliability modeling simulation method according to claim 1, wherein the simulation platform further simulates the number of software services constituting the software service by using a Monte Carlo method; the method specifically comprises the following steps: software services are divided into three types, namely simple services, general services and complex services, the number of the software services contained in each type of services is different, the number of the software services contained in the complex services is the largest, and the number of the software services contained in the simple services is the smallest.
8. The multi-Agent-based networked software system reliability modeling and simulation method according to claim 1, wherein the simulation model adopted by the simulation platform is as follows: a fault propagation simulation model and a reliability simulation model;
the fault propagation simulation model is modeled based on a virus propagation model, and single or cyclic simulation is carried out on the fault propagation process in the networked software system by setting the rule and the strength of fault propagation in the networked software system; in the simulation process, the parameters to be set at least comprise fault propagation probability, node fault detection probability and fault node recovery probability; after the simulation is finished, reliability related data of the networked software system are obtained, and the capability of the networked software system for bearing software faults and repairing is simulated and evaluated;
the reliability simulation model carries out single or circular simulation on the fault condition of the internal nodes of the software when the networked software system executes various different tasks according to the reliability definition, calculates the structural reliability and the task reliability of the software, and carries out simulation and evaluation on the reliability of the networked software system when executing different tasks in the using process.
9. The multi-Agent-based networked software system reliability modeling simulation method according to claim 1, wherein the simulation platform further comprises: the system comprises a parameter setting module, a service simulation and state management module, a visual display module and a simulation process display module;
setting the type of the simulation model and partial index parameters in the simulation process through the parameter setting module;
simulating a development environment through the service simulation and state management module, and starting and controlling a corresponding simulation function of the selected simulation model according to set parameters;
checking the numerical value and the state of the key indexes of the networked software system reliability model of each simulation time point through the visual display module;
and displaying various parameters set for the selected simulation model, state change chart information of the reliability model of the networked software system in the simulation process, simulation result information when the simulation is finished and the state of the reliability model of the networked software system through the simulation process display module.
10. The multi-Agent-based networked software system reliability modeling and simulation method according to claim 9, wherein after the single simulation of the fault propagation simulation model is finished, the node proportion of a fault generated in the networked software system, the node proportion with fault resistance, the node proportion still possibly having the fault, and the value and change condition of each simulation time point are displayed through the visual display module;
after the cyclic simulation of the reliability simulation model is finished, the structural reliability and the task reliability of the networked software system are displayed through the visual display module along with the development and change of simulation time after the simulation is started, and a task sequence when the simulation is executed, a node sequence of each task in the execution sequence and the fault condition of each task are output.
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