CN107645412B - Web service combination multi-target verification method in open environment - Google Patents
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Abstract
The invention discloses a Web service combination multi-target verification method in an open environment, which comprises the following steps: abstracting a Web service combination process and QoS attributes, and modeling to form a multi-target Markov decision process; abstracting environmental conditions influencing the Web service combination process, modeling into a traditional Markov decision process, enabling the traditional Markov decision process to interact with a Web service combination model, and truly simulating the process that the Web service combination is influenced by a dynamic environment in a complex open environment; modeling the QoS attribute into a multi-target sequential logic formula according to the preference of the user; and taking the Web service combination model, the environment model and the multi-target sequential logic formula as the input of the method, and using a probability model detection tool to finally obtain a quantitative result meeting the user requirements and derive a corresponding strategy. The method solves the problem that the traditional Web service combination verification method is difficult to directly use due to environment uncertainty and user requirement multi-objective in a complex open environment.
Description
Technical Field
The invention belongs to the technical field of computer software engineering development, and particularly relates to a Web service combination multi-target verification method in an open environment.
Background
The probability model detecting technology is a formalization method for verifying whether a finite state system meets the attribute, and aims to describe a given probability system and the attribute to be verified by a finite state model and a sequential logic formula respectively, and then judge whether the system model meets the system attribute by adopting a model detector. The general process of the probabilistic model detection technology is to model the probabilistic system to be tested, then use a formal language such as sequential logic formula to describe the system attributes, and finally use the corresponding model detection analysis technology to judge whether the system model meets the system attributes. From this we can see that the probabilistic model detection technique is generally divided into three parts: the system comprises a modeling language for describing a probability system, a time sequence logic formula for describing system attributes and an analysis technology for verifying whether the system meets the attributes. The probabilistic model detection tool can be automatically executed under the support of a detection algorithm, and a counterexample path is provided when the system does not meet the property to be detected.
PRISM is a tool used to analyze probabilistic systems and can support three types of models, discrete time Markov chains, continuous time Markov chains, and Markov decision processes. The tool can verify whether a dynamic probability system meets the attributes represented by Probability Computation Tree Logic (PCTL) and continuous random logic (CSL) through automatic analysis of the established probability system.
Web services composition is a technique that combines different independent services to accomplish a more powerful composite service. By combining existing single services to build complex and value-added applications, deployment time and cost can be greatly reduced. With the rapid development of Web services, the number of services with similar functional attributes and different Quality of Service (QoS) is rapidly increasing. The service selected for different preferences of a user according to the quality of service is called a QoS-aware Web service composition.
In the field of Service Oriented Architecture (SOA) and Service Oriented Computing (SOC), QoS aware Web services are combined as a current research hotspot. How to select a service to maximize the quality of service of the entire service composition becomes a key issue in the research of service compositions. The criteria for assessing the quality of service of a Web service portfolio are typically user requirements such as service price, provider reputation, reliability, etc.
People have previously focused on improving the accuracy of obtaining a Web service combination by improving a Web service selection algorithm, and have paid less attention to a verification method based on a probabilistic model detection technology. Meanwhile, the previous work does not consider the influence of the random change of the environment on the Web service combination method.
Disclosure of Invention
The invention aims to provide a Web service combination multi-target verification method in an open environment, which defines a novel multi-target Markov decision process, and verifies the process by using a probability model detection technology and a multi-target verification technology to obtain a Web service combination and a quantitative analysis result. The explicit environment model is introduced to interact with the Web service combination model, so that the Web service combination process in an open environment is simulated really, and the problem that the traditional Web service combination verification method is difficult to use directly due to environment uncertainty and user requirement multi-objective in a complex open environment is solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a Web service combination multi-target verification method under an open environment comprises the following steps:
(1) abstracting a Web service combination process and a QoS attribute to be verified according to the characteristics of an object to be researched;
(2) modeling the Web service combination process into a multi-target Markov decision process according to the Web service combination process and the QoS attribute in the step (1);
(3) determining environmental conditions capable of influencing the Web service combination process and the QoS attribute according to the Web service combination process and the QoS attribute in the step (1); abstracting the process of the random change of the environmental conditions, and modeling into a traditional Markov decision process; all states of the traditional Markov decision process correspond to different states of the environmental conditions, and the transition between the states of the Markov decision process corresponds to the random variation process of the environmental conditions; in the modeling process, the traditional Markov decision process model in the step is interacted with the multi-target Markov decision process model in the step (2);
(4) analyzing the user preference and the QoS attribute in the step (1), and expressing by using a multi-target sequential logic formula;
(5) the multi-target Markov decision process in the step (2) and the traditional Markov decision process in the step (3) form a finite state model for describing a probability system; and (4) the multi-target sequential logic formula in the step (4) represents the system attribute to be verified, whether the finite state model meets the system attribute to be verified is verified, and a quantitative verification result and a corresponding path of the finite state model are obtained, wherein the corresponding path is a Web service combination mode.
Preferably, the step (1) specifically comprises:
(11) analyzing tasks to be completed by an object to be researched, and defining a group of abstract service description system behaviors;
(12) analyzing the abstract services in the step (11), wherein the same abstract service is provided by different concrete services, and a set of the concrete services is defined as a group of concrete services of each abstract service;
(13) abstracting the object to be researched into a Web service combination process according to the analysis results in the steps (11) and (12);
(14) and abstracting the QoS attribute to be verified according to the user requirement.
Preferably, the step (2) specifically comprises:
(21) modeling into a set of actions in the multi-target Markov decision process according to the specific service obtained by analyzing in the step (12);
(22) establishing different reward structures according to the QoS attributes in step (14).
Preferably, the multi-target markov decision process in step (2) is created by a probabilistic model detection tool.
Preferably, the conventional markov decision process in step (3) is created by a probabilistic model detection tool.
Preferably, the step (4) specifically comprises:
(41) determining the number of user targets, wherein one QoS attribute is a target;
(42) according to the preference of users to different targets, the method is divided into two categories: the key target is the most interesting target of the user; the constraint target is a target which is secondary or not noticed by the user;
(43) representing the constraint target in the step (42) by using a time-series logic formula, and verifying by using a probability model detection technology to obtain a constraint range;
(44) and (5) integrating the constraint range obtained in the step (43) and the key target and the constraint target obtained in the step (42) for modeling to obtain a multi-target sequential logic formula.
Preferably, the step (5) specifically comprises: and verifying whether the finite state model meets the system attribute to be verified by adopting a probability model detection technology.
The invention has the beneficial effects that:
the invention introduces the environment model, makes the environment model interact with the Web service combination model, simulates the random change process of the real environment condition, and solves the problem that the traditional Web service combination verification method is difficult to be directly used due to the uncertainty of the environment; then, a multi-target sequential logic formula is used for describing user requirements with multiple targets and uncertainty, and finally, quantitative analysis results and corresponding paths are obtained through a multi-target verification technology, so that a novel Web service combination method based on a probability model detection technology is provided.
Drawings
FIG. 1 is a framework diagram of the present invention;
FIG. 2 is a Web services composition process;
fig. 3 is a process of random variation of environmental conditions.
Detailed Description
The invention provides a Web service combination multi-target verification method in an open environment, and the technical scheme of the invention is explained in detail below by taking an online shopping service system as an example in cooperation with a scheme shown in FIG. 1.
(1) And abstracting a concrete Web service combination process and a QoS attribute to be verified according to the characteristics of the object to be researched. The method specifically comprises the following steps:
(11) and analyzing the tasks to be completed of the object to be researched and defining a group of abstract service description system behaviors. In this example, the online service system needs to accomplish several tasks: selecting a suitable shopping platform, such as the kyoto, naobao; selecting a proper store as different stores, wherein the prices of the same commodities are different and the discounts of the commodities are different; selecting a payment platform, wherein the most used payment platforms are a Paibao platform and a WeChat payment platform; and selecting express delivery, wherein the arrival time and the price of different express deliveries are different. Therefore, in this example, a total of 4 abstract services are defined AS AS1，AS2，AS3，AS4。
(12) Analyzing the abstract services in step (11), the same abstract service being provided by different concrete services, the set of concrete services defining a set of concrete services for each abstract service. From step (11), the definitions in this example are altogetherFour abstract services, respectively: sales platform, store, payment platform, express, defined AS AS1，AS2，AS3，AS4. Abstract service AS1There are two specific services, CS1_1, CS1_2, defined AS1CS1_1, CS1_ 2. These two specific services represent services that provide a sales platform, CS1_1 represents paibao, and CS1_2 represents kyoto. At abstract service AS2There are 3 specific services, namely AS2CS2_1, CS2_2, CS2_ 3. CS2_1 represents a transportation bank, CS2_2 represents a China bank, and CS2_3 represents a construction bank. At abstract service AS3Of which there are 2 specific services, AS3CS3_1, CS3_ 2. CS3_1 represents a paypal platform and CS3_2 represents a WeChat payment platform. At abstract service AS4Of which there are 4 specific services, AS4CS4_1, CS4_2, CS4_3, CS4_ 4). CS4_1 represents Shunfeng express, CS4_2 represents Yunjian express, CS4_3 represents Yuntong express and CS4_4 represents Zhongtong express.
(13) And (5) abstracting the object to be researched into a Web service combination process according to the analysis result in the steps (11) and (12). In this example, abstract services and concrete services of the online shopping service system are obtained, and the Web service combination process is as shown in fig. 2.
(14) And abstracting the QoS attribute to be verified according to the user requirement. In this example, three QoS attributes are proposed, respectively: consumption of the invocation service, total cost of the user spent in the shopping scenario, total time the user spent in the shopping scenario.
(2) And (3) modeling the service combination process into a multi-target Markov decision process according to the Web service combination process and the QoS attribute in the step (1). The method specifically comprises the following steps:
(21) and (4) modeling according to the specific service obtained by analyzing in the step (12) to form a set of actions in the multi-target Markov decision process. In this example, each specific service is an action in the multi-target Markov decision process, when the action occurs, the current state is transferred to the next state, and the whole process is created by a probability model detection tool PRISM language.
(22) And (4) establishing different reward structures of the multi-target Markov decision process according to the QoS attributes in the step (14). In this example, there are three attributes to be verified, and corresponding reward structures are respectively established, as shown in table 1, as follows:
TABLE 1
| QoS attributes | Reward structure name |
| Express arrival time | express_arrive_time |
| Total price | price |
| Invoking service consumption | consumption |
(3) Determining environmental conditions capable of influencing the QoS attribute of the Web service combination process according to the Web service combination process and the QoS attribute in the step (1); abstracting the process of the random change of the environmental conditions, and modeling into a traditional Markov decision process; all states of the traditional Markov decision process correspond to different states of the environmental conditions, and the transition between the states of the Markov decision process corresponds to the random variation process of the environmental conditions; in the modeling process, the traditional Markov decision process model in the step is interacted with the multi-target Markov decision process model in the step (2) through a motion synchronization mechanism provided by a probability model detection tool PRISM. In this example, using weather quality as an environmental condition, weather changes are random, defining two states: good and bad; under this condition, the process of the environmental condition changing randomly is the process of random transition between two states. The process is modeled as a traditional Markov decision process using PRISM language, and the environmental model interacts with the Web service composition model using a synchronization method in PRISM language. The state transition process of the environment model is as shown in FIG. 3.
The interaction between the traditional Markov decision process and the multi-target Markov decision process in the step (3) can accurately simulate the influence of dynamic changes of environmental conditions on the selection of services by the Web service combination method in a real environment (different concrete services can be selected by a certain abstract service according to the real-time environmental condition state under different environmental conditions); the traditional Web service combination method does not have the capability of modeling the dynamic change process of the environmental conditions, and cannot simulate the influence of the dynamic change of the environmental conditions on the service selection of the Web service combination method; therefore, compared with the traditional Web service combination method, the Web service combination method disclosed by the invention can obtain a more accurate Web service combination mode.
(4) Analyzing the user preference and the QoS attribute in the step (1), and expressing by using a multi-target sequential logic formula. The method specifically comprises the following steps:
(41) determining the number and meaning of user targets, wherein one QoS attribute corresponds to one target, and dividing the targets into two types according to the preference of users to different targets: the key target is the most interesting target of the user; the constraint objective is the objective of secondary intention or unnoticed intention of the user. In this example, the number of user targets is three in total, and the non-functional requirements of the user have uncertainty. Three targets, P1, P2, and P3, are defined according to three different QoS attributes in the example, as in table 2. Now exemplifying two users, it may be desirable for user 1 to have as little service consumption as possible, regardless of total cost and express arrival time. It may be desirable for user 2 to have a minimum total cost, regardless of service consumption and express arrival time. Therefore, the key goal of USER-1 is P3, while the constraint goals P1 and P2; the key goal for user 2 is P2, while the constraint goals are P1 and P3. Table 2 is as follows:
TABLE 2
| Target | Means of |
| P1 | What is the total consumption of the invoked service |
| P2 | What the total cost the user spends in the shopping scenario is |
| P3 | Time of express arrival of user in shopping scene |
(42) And (4) representing the constraint target in the step (41) by using a time-series logic formula, and verifying by using a probability model detection technology to obtain a constraint range. In this example, the maximum value of the constraint targets of user 1 and user 2 is expressed by a time-series logic formula and verified by using a probabilistic model detection technique, and the results are expressed by P1_ max, P2_ max and P3_ max, as shown in Table 3, as follows:
TABLE 3
(43) And (4) integrating the constraint range obtained in the step (42) and the key target and the constraint target in the step (41) for modeling to obtain a multi-target sequential logic formula. In this example, the key objective of USER-1 is attribute P3, and after obtaining the values of P1_ max and P2_ max, the multi-objective sequential logic formula can be obtained. In the same way, the multi-target sequential logic formula of the user 2 can be obtained, as shown in table 4, as follows:
TABLE 4
(5) The multi-target Markov decision process in the step (2) and the traditional Markov decision process in the step (3) form a finite state model for describing a probability system; and (4) expressing the system attribute to be verified by the multi-target sequential logic formula in the step (4), verifying whether the finite state model meets the system attribute to be verified by using a probability model detection technology, and obtaining a quantitative verification result and a corresponding path of the finite state model. The corresponding path is the Web service combination mode.
After all steps are completed, the correctness and feasibility of the invention are verified by two experiments. The operation environment of the experiment is Ubuntu14.04LTS system, Intel core I7 processor, 32GB memory; the experimental tool was a probabilistic model detector PRISM with a version number of 4.3.1-linux 64. In the first experiment, the state number and the transition number are used as the measurement, the abstract service number and the concrete service number are used as variables, and the method can support the largest-scale service number. The maximum scale that the method can support is verified. The verification result of the sequential logic formula of the constraint target is shown in table 5, the verified multi-target sequential logic formula is shown in table 6, and the experimental result obtained by verification is shown in table 7. On the basis, in order to verify the multi-target expansibility of the Web service combination by the method, namely under the condition of the same abstract service quantity and specific service quantity, performing a second experiment by using key targets and all target numbers as variables, wherein the results are shown in tables 8, 9 and 10, and tables 5 to 10 are as follows:
TABLE 5
| Abstracting the number of services | P1_ max verification result |
| 3 | 9.445 |
| 4 | 13.445 |
| 5 | 19.667 |
| 6 | 19.667 |
TABLE 6
TABLE 7
| Abstracting the number of services | Multiple target verification results | Number of states | Number of transfers |
| 3 | 15.67 | 50897 | 126804 |
| 4 | 23.333 | 50897 | 126804 |
| 5 | 39.167 | 610769 | 1717076 |
| 6 | 55.112 | 7329233 | 22982484 |
TABLE 8
| Abstracting the number of services | Specific number of services | Number of states | Number of |
| 4 | 4 | 50897 | 126804 |
| 4 | 5 | 122946 | 307025 |
| 4 | 6 | 253177 | 633198 |
| 4 | 7 | 466754 | 1168587 |
| 4 | 8 | Breakdown | Breakdown |
TABLE 9
| Abstracting the number of services | Specific number of services | Number of states | Number of transfers |
| 5 | 4 | 610769 | 126804 |
| 5 | 5 | 5710693 | 9405846 |
| 5 | 6 | 34264165 | 56435094 |
| 5 | 7 | Breakdown | Breakdown |
Watch 10
| Abstracting the number of services | Specific number of services | Number of states | Number of transfers |
| 6 | 3 | 1328602 | 4153065 |
| 6 | 4 | 7329233 | 22982484 |
| 6 | 5 | Breakdown | Breakdown |
The above results show the scalability of the method of the invention on the target number. Meanwhile, when the key target and the constraint target are changed, the optimal solution of the key target is solved by changing the multi-target sequential logic formula, so that the uncertainty of the user requirement can be solved.
For the Web service combination process in a complex open environment, the non-functional requirements of users have certain uncertainty and multi-objective. Different users have different requirements, and the method of the invention considers that one requirement corresponds to one target. The user's priority to the target also differs between different targets. The multi-objective sequential logic formula can appropriately describe the multi-objective. The Web service composition process is modeled as a finite state model, namely a multi-target Markov decision process. Different requirements are respectively abstracted into different reward (reward) structures, and then modeling is carried out to form a multi-target sequential logic formula according to the preference of a user. And finally, verifying whether the finite state model meets a multi-target sequential logic formula by using a probability model detection technology. In the multi-objective Markov decision process, the action is to select a service. Therefore, the verification result of the multi-target sequential logic formula is obtained, and the state conversion path corresponding to the result, namely the optimal Web service combination mode, can be obtained. Dynamic changes in environmental conditions can affect the value of rewards for a combination of Web services and thus the selection of Web services. Therefore, the environment model is introduced to interact with the Web service combination model, and the accuracy of the Web service combination method can be improved.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (6)
1. A Web service combination multi-target verification method under an open environment is characterized by comprising the following steps:
(1) abstracting a Web service combination process and a QoS attribute to be verified according to the characteristics of an object to be researched;
(2) modeling the Web service combination process into a multi-target Markov decision process according to the Web service combination process and the QoS attribute in the step (1);
(3) determining environmental conditions capable of influencing the Web service combination process and the QoS attribute according to the Web service combination process and the QoS attribute in the step (1); abstracting the process of the random change of the environmental conditions, and modeling into a traditional Markov decision process; all states of the traditional Markov decision process correspond to different states of the environmental conditions, and the transition between the states of the Markov decision process corresponds to the random variation process of the environmental conditions; in the modeling process, a traditional Markov decision process model in the step is interacted with the multi-target Markov decision process model in the step (2) through an action synchronization mechanism provided by a probability model detection tool PRISM;
(4) analyzing the user preference and the QoS attribute in the step (1), and expressing by using a multi-target sequential logic formula;
(5) the multi-target Markov decision process in the step (2) and the traditional Markov decision process in the step (3) form a finite state model for describing a probability system; and (4) expressing the system attribute to be verified by the multi-target sequential logic formula in the step (4), verifying whether the finite state model meets the system attribute to be verified by adopting a probability model detection technology, and obtaining a quantitative verification result and a corresponding path of the finite state model, wherein the corresponding path is a Web service combination mode.
2. The method for Web service combined multi-target verification in an open environment according to claim 1, wherein the step (1) specifically includes:
(11) analyzing tasks to be completed by an object to be researched, and defining a group of abstract service description system behaviors;
(12) analyzing the abstract services in the step (11), wherein the same abstract service is provided by different concrete services, and a set of the concrete services is defined as a group of concrete services of each abstract service;
(13) abstracting the object to be researched into a Web service combination process according to the analysis results in the steps (11) and (12);
(14) and abstracting the QoS attribute to be verified according to the user requirement.
3. The method for Web service combined multi-target verification in an open environment according to claim 2, wherein the step (2) specifically includes:
(21) modeling into a set of actions in the multi-target Markov decision process according to the specific service obtained by analyzing in the step (12);
(22) establishing different reward structures according to the QoS attributes in step (14).
4. The method for Web service combined multi-target verification in open environment according to any one of claims 1-3, wherein the multi-target Markov decision process in step (2) is created by a probabilistic model detection tool.
5. The method for Web service combined multi-target verification in open environment according to any one of claims 1-3, wherein the conventional Markov decision process in step (3) is created by a probabilistic model detection tool.
6. The method for Web service combined multi-target authentication in an open environment according to any one of claims 1-3, wherein the step (4) specifically includes:
(41) determining the number of user targets, wherein one QoS attribute is a target;
(42) according to the preference of users to different targets, the method is divided into two categories: the key target is the most interesting target of the user; the constraint target is a target which is secondary or not noticed by the user;
(43) representing the constraint target in the step (42) by using a time-series logic formula, and verifying by using a probability model detection technology to obtain a constraint range;
(44) and (5) integrating the constraint range obtained in the step (43) and the key target and the constraint target obtained in the step (42) for modeling to obtain a multi-target sequential logic formula.
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