CN113420419B - Business process model analysis method under micro-service scene - Google Patents
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Abstract
The invention discloses a business process model analysis method under a micro-service scene, which comprises the following steps: a design analysis stage and a runtime analysis stage, wherein in the design analysis stage, accessibility analysis, model complexity analysis and implementation complexity analysis are carried out on the flow definition, a design analysis stage score is obtained according to the result of the accessibility analysis, the model complexity score and the implementation complexity, and the designed model is adjusted according to the design analysis stage score; in the runtime analysis stage, the process definition runs in an execution environment, and performs execution history analysis, QoS policy analysis and side branch waiting delay analysis, comprehensively analyzes the QoS value of the runtime process instance, and further adjusts the model or optimizes the execution policy by combining the execution condition. By utilizing the analysis method of the invention, a parameterized analysis result can be obtained by analyzing the business process model, thereby better optimizing the business process.
Description
Technical Field
The invention belongs to the technical field of micro services, and particularly relates to a business process model analysis method in a micro service scene.
Background
In recent years, with the development of microservices, microservices architecture has been commonly applied to production practice to develop flexible distributed programs to replace traditional monolithic applications, and the popularization of microservices architecture also drives the development of service orchestration technology.
For example, chinese patent publication No. CN111708647A discloses a service request processing method, which splits a service request into a first service request and a second service request by receiving the service request sent by a service requester, and sends the first service request to a corresponding first server and the second service request to a corresponding second server through a preset interactive component, and combines the result returned by the first server and the result returned by the second server, and processes the service request according to the combined result, so that each application system only focuses on the service logic in each field, thereby avoiding repeated construction, effectively reducing code development workload, realizing the checking, processing, and access exception handling of the service request, and decoupling the public security function and the service function in different fields.
The service orchestration technology aims to design and execute workflows in a complex microservice system in a more efficient manner, optimize the cooperative work among a plurality of microservices and reduce the burden of development and operation and maintenance personnel.
The orchestration engine is one of the specific applications of service orchestration technology. In a microservice orchestration engine, developers can predefine executable business process models and automatically execute these business process models using the engine. Due to the adoption of the micro-service arrangement engine, the coupling degree between services can be further reduced, and the service value of the services is higher; developers can consider the scheduling problem under the complex micro-service architecture less, and further concentrate on the development of the service; a planner can more flexibly implement a business process from the system macro, fully reuse the existing micro service and simultaneously more accurately put forward business requirements.
The business process model can be executed as the product of service orchestration and the input of the orchestration engine, the quality of which will affect the whole microservice architecture system from two aspects: the business development needs to refer to a business process model, reuse the existing business as much as possible, accurately design the business process and effectively reduce the pressure of developers. The reasonable business process is designed, the business execution path can be optimized, and the overall efficiency of the system is improved.
The increasingly expanded services lead to increasingly complex calling relationships among services under the micro-service architecture, and the service flow model analysis method can help designers to analyze the value of a service flow model under the scenes, so that the service flow model is optimized.
Disclosure of Invention
The invention provides a business process model analysis method in a micro-service scene, which can obtain a parameterized analysis result by analyzing a business process model so as to better optimize a business process.
For the sake of convenience in the following description, the invention is defined by the following basic terms:
process definition and Process example: a process definition is a normalized description of a business process model. A process instance is an execution entity of a process definition.
Services and activities: a business represents a node in the flow definition that represents the actual existing service. An activity is an executing entity of a service.
A gateway: the gateway is a state node in the flow definition, and the gateway determines the subsequent execution path.
The business process model analysis of the invention is divided into two stages: and the design analysis stage and the runtime analysis stage respectively correspond to the process definition and the process instance. The two stages respectively pay attention to different attributes of the model, and an analysis result is given by a parameterized index.
The technical scheme of the invention is as follows:
a business process model analysis method under a micro-service scene comprises the following steps:
(1) design analysis phase
Performing accessibility analysis, model complexity analysis and implementation complexity analysis on the flow definition, obtaining a design analysis stage score according to the accessibility analysis result, the model complexity score and the implementation complexity, and adjusting the designed model according to the design analysis stage score;
(2) runtime analysis phase
The process definition is operated in an execution environment, execution history analysis, QoS strategy analysis and side-branch waiting time delay analysis are carried out, QoS value of a process instance in operation is comprehensively analyzed, and a model is further adjusted or an execution strategy is optimized by combining with execution conditions.
Further, when performing reachability analysis, using the NuSMV tool, the result of the reachability analysis is described as a boolean quantity B, while all unreachable nodes are noted.
Further, when the model complexity analysis is carried out, a circle complexity V is introducedGNumber of services n and parameter size VpiThree parameters to calculate a model complexity score MeThe concrete formula is as follows:
wherein k is1To take the value of [0,1]The self-defined parameters are used for adjusting the specific gravity of the parameter values under different conditions.
Further, when performing complexity analysis, examining the implementation Cost of the model from two aspects of multiplexing service proportion η and development Cost, wherein the value of multiplexing service proportion η is the proportion of multiplexing service to total traffic n, the development Cost is the ratio of total man-hour estimated by a designer to expected man-hour, and the specific formula is as follows:
I＝k2·η+(1-k2)·Cost
where I is the implementation complexity, k2To take the value of [0,1]Is used to determine the proportion of η and Cost in the evaluation score.
Further, the formula of the design analysis stage score is as follows:
wherein G represents a design analysis stage score; b represents a Boolean quantity, and is a reachability analysis result; meRepresenting a model complexity score; i represents implementation complexity; moptRepresenting either the ideal model complexity or the optimal model complexity, the value of G will fall in the interval (0, 2)]In (1).
Further, when performing the execution history analysis, the actual execution condition of the service instance is obtained by installing an additional monitoring component in the execution environment, and the evaluation of the runtime performance of a service flow model is completed by comprehensively considering the overall completion rate, the path load distribution, the average time delay and the fusing times of the service instance.
Furthermore, when QoS policy analysis is performed, the QoS policy includes a service degradation policy, a load balancing policy and an HPA policy, and the completion probability of the corresponding process instance is presumed by analyzing the QoS policy gateway related to the process definition.
Further, when analyzing the side branch waiting time delay, the side branch waiting time delay Twait-kRepresenting the waiting time delay of activity k at the branch convergence in the process instance; in a multi-branch flow, the execution time of a parallel branch depends on the execution duration of the slowest branch, and the process that the activity at the convergence is ready is also resource-consuming.
Compared with the prior art, the invention has the following beneficial effects:
1. in the design and analysis stage, the invention focuses on the model design and analyzes the quality of the flow definition. Calculating the complexity of the flow definition by a graph analysis method; analyzing the unreachable service in the flow definition by using reachability analysis, and marking the unreachable service and the path thereof; the implementation complexity of the whole service is given through the service implementation information filled in during the process definition
2. The analysis stage in the running process focuses on the QoS value of the flow instance in the running process. In the stage, the QoS value of the process instance in operation is comprehensively analyzed by three means of history analysis, QoS strategy analysis and side-branch waiting delay analysis, so that a user can be assisted to further optimize the process definition by combining the actual operation condition.
3. During analysis, the method not only carries out abnormal labeling, but also carries out standardized value calculation, and provides visual parameters for a user so as to conveniently compare quality differences among different models.
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FIG. 1 is a block diagram of an analytical method according to the present invention;
FIG. 2 is a flow chart of the application of the analysis method of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
As shown in fig. 1 and fig. 2, a method for analyzing a business process model in a micro-service scenario includes two stages: and the design analysis stage and the runtime analysis stage respectively correspond to the process definition and the process instance.
The first stage is a design analysis stage, and the method analyzes the rationality of the design defined by one process from three angles and helps designers to improve the model design.
And (3) accessibility analysis: the model accessibility analysis can be done with some model detection tools. The present invention proposes the use of a NuSMV tool that integrates SAT-based bounded model detection techniques, supports all the specifications described by the computational tree logic CTL and the linear sequential logic LTL, and can cover the vast majority of scenarios. A healthy flow definition should not have unreachable traffic, so the result of the reachability analysis is described as a boolean quantity B. In addition, reachability analysis should also note all unreachable nodes to provide an improved reference.
And (3) analyzing the complexity of the model: the invention introduces the circle complexity VGNumber of services n and parameter size VpiThree parameters are used to calculate the model complexity. The computation of the circle complexity can refer to other related data, which is not described in the present invention. Services are basic elements constituting the flow definition, so the number of services also directly affects the complexity of the model, and on the other hand, the circulation of messages also consumes time and other hardware resources. Therefore, for a flow definition, the number of services in the flow definition is always smaller and better under the premise that the task goal can be achieved. The parameter scale is described as a binary parameter of the number of parameters of the inter-service flow and the step size thereof, and the inner product of the parameter scale can be used for visually describing the data scale defined by one flow.
Model complexity score MeThe following formula can be used for calculation:
wherein k is1To take the value of [0,1]The self-defined parameters are used for adjusting the specific gravity of the parameter values under different conditions. The lower the score the better under the same task goal.
Performing complexity analysis: the implementation complexity mainly considers the implementation Cost of the model from two aspects of multiplexing service proportion eta and development Cost. The value of η is the proportion of the multiplexing service to the total service amount n, and the development Cost is the ratio of the total man-hour estimated by the designer to the expected man-hour. The implementation complexity I calculation formula is as follows:
I＝k2·η+(1-k2)·Cost
wherein k is2To take the value of [0,1]Is used to determine the proportion of η and Cost in the evaluation score. Most of the time, the weight of Cost is greater than the model complexity (Cost first).
For a process definition, its design analysis stage score, G, can be calculated using the following formula:
wherein M isoptRepresenting either the ideal model complexity or the optimal model complexity, the value of G will fall in the interval (0, 2)]In (1).
The second phase is a runtime analysis phase, which monitors a specific execution environment and analyzes some problems existing in the process of executing the process instance to help optimize the model.
Performing a history analysis: the execution history analysis acquires the actual execution condition of the service instance by installing an additional monitoring component in the system, and comprehensively considers the overall completion rate (stability), the path load distribution, the average time delay and the fusing frequency of the service instance to complete the evaluation of the runtime performance of a service flow model.
QoS policy analysis: in actual production, in order to guarantee the quality of important services, the gateway will often implement some extra QoS policies. A typical QoS policy is service degradation, which allocates resources unfairly by prioritizing traffic in order to ensure that core traffic has enough resources to respond to requests in time. Other QoS policies also include load balancing policies, HPA policies, distance. In a similar QoS policy, dominant and disadvantaged activities may result when resources are tight. By analyzing the QoS policy gateways involved in the flow definition, we can infer the completion probability of the corresponding flow instance.
Analyzing the waiting time delay of the side branch: in a multi-branch flow, the execution time of a parallel branch depends on the execution duration of the slowest branch, and the process that the activity at the convergence is ready is also resource-consuming. Side branch latency Twait-kRepresenting the latency of activity k at the branch sink in the flow instance. The analysis can provide data to help designers to optimize the model on one hand, and can also provide support for runtime monitoring and optimization of the system on the other hand.
The invention provides three quantitative analysis strategies in the first stage, and helps the business process model to judge the quality of the designed model in the process definition stage. Meanwhile, three monitoring strategies are provided in the second stage and used for evaluating the execution condition of the business process model in a specific environment, and a designer can adjust the model or optimize the execution strategy according to the specific condition so as to improve the overall execution efficiency of the model.
The embodiments described above are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the present invention.
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