CN114640587A - Web service combination optimization method, device, server and storage medium - Google Patents
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Abstract
The invention discloses a Web service combination optimization method, a Web service combination optimization device, a Web service combination optimization server and a storage medium. The method comprises the following steps: randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services; updating the pareto solution set according to the set number of solutions and releasing the initial pheromone; in each iteration process, constructing a solution according to the transition probability among different specific services, and updating the pareto solution set and the initial pheromone by using the solution; if the iteration stop condition is met, outputting a pareto optimal solution set; and generating an optimized Web service combination model according to the output pareto optimal solution set for service calling. The technical scheme improves the performance of the combinatorial optimization algorithm, can adapt to the dynamic property of the combinatorial optimization problem, and provides a Web service combinatorial model with the best QoS on the basis.
Description
Technical Field
The embodiment of the invention relates to the technical field of network services, in particular to a method, a device, a server and a storage medium for Web service combination optimization.
Background
With the rapid development of computer applications, people's research focus has migrated from network layer system interconnection to application layer Service integration, and a new Service-oriented Computing mode, Service Computing, has come into play. As an important expansion direction of a Service Oriented Architecture (SOA), a Web Service technology is also developed and gradually becomes a core of a next generation distributed processing system, and Web services shared on a network tend to be more stable and easy to use. But the single Web service function is simple and limited, and is difficult to meet the requirements of some practical applications. Therefore, it is necessary to combine a plurality of Web services to construct a powerful combination of Web services to meet various practical application requirements.
Currently, the research on the Web Service combination is generally based on the Quality of Service (QoS), the optimization of the Service combination is an NP-hard problem, and due to the reasons that the Web Service state is unstable, the QoS attribute value of the Service is sometimes ambiguous and often changes, and the like, the Service combination optimization algorithm is required to have good performance and to be adaptable to the situations that the Web Service state is unstable, the QoS value of the Service is ambiguous, the QoS in the Service changes, and the like.
Disclosure of Invention
The invention provides a Web service combination optimization method, a Web service combination optimization device, a Web service combination optimization server and a storage medium, which are used for improving the performance of a combination optimization algorithm and providing a Web service combination model with the best QoS.
In a first aspect, an embodiment of the present invention provides a service composition optimization method, including:
randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services;
updating the pareto solution set according to the set number of solutions and releasing the initial pheromone;
in each iteration process, constructing a solution according to the transition probability among different specific services, and updating the pareto solution set and the initial pheromone by using the solution;
if the iteration stop condition is met, outputting a pareto optimal solution set;
and generating an optimized Web service combination model according to the output pareto optimal solution set for service call.
In a second aspect, an embodiment of the present invention provides a service composition optimization apparatus, including:
the generation module is used for randomly generating a set number of solutions, each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services;
the initial release module is used for updating the pareto solution set according to the set number of solutions and releasing the initial pheromone;
the iteration module is used for constructing a solution according to the transition probability among different specific services in each iteration process, and updating the pareto solution set and the initial pheromone by using the solution; if the iteration stop condition is met, outputting a pareto optimal solution set;
and the optimization module is used for generating an optimized Web service combination model according to the output pareto optimal solution set for service calling.
In a third aspect, an embodiment of the present invention provides a server, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the service composition optimization method of the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the service composition optimization method according to the first aspect.
The embodiment of the invention provides a Web service combination optimization method, a Web service combination optimization device, a Web service combination optimization server and a storage medium. The method comprises the following steps: randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services; updating the pareto solution set according to the set number of solutions and releasing the initial pheromone; in each iteration process, constructing a solution according to transition probabilities among different specific services, and updating the pareto solution set and the initial pheromone by using the solution; if the iteration stop condition is met, outputting a pareto optimal solution set; and generating an optimized Web service combination model according to the output pareto optimal solution set for service calling. The technical scheme improves the performance of the combinatorial optimization algorithm, can adapt to the dynamic property of the combinatorial optimization problem, and provides a Web service combinatorial model with the best QoS on the basis.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a flowchart of a service composition optimization method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a service flow according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a service composition optimization apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. In addition, the embodiments and features of the embodiments in the present invention may be combined with each other without conflict. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, and the like.
It should be noted that the terms "first", "second", and the like in the embodiments of the present invention are only used for distinguishing different apparatuses, modules, units, or other objects, and are not used for limiting the order or interdependence of the functions performed by these apparatuses, modules, units, or other objects.
Example one
Fig. 1 is a flowchart of a service composition optimization method according to an embodiment of the present invention, where the embodiment is applicable to a situation of optimizing a Web service composition model. In particular, the service composition optimization method may be performed by a service composition optimization apparatus, which may be implemented by software and/or hardware and integrated in a server. Further, the server includes, but is not limited to: the system comprises an industrial integration server, a system background server and a cloud server.
In this embodiment, a non-inferior Web service combination optimization model is solved by synthesizing various optimization objectives regarding QoS based on an ant colony algorithm. The ant colony algorithm has the advantages of self-organization, positive feedback and the like, and has strong capability of searching non-inferior solutions. When solving the multi-objective optimization problem, ants rely on pheromone accumulation in the solution space to search for non-inferior solutions. As shown in fig. 1, the method specifically includes the following steps:
s110, randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services.
Specifically, the process of randomly generating the solution may be understood as a process of initializing the service composition model.
A Concrete Service (CS) is a Web service that can be executed, registered by a service provider in a unified description, discovery and integration registry. A CS is a triplet < S, G, P >, where S is the basic description, i.e. the service name and text description; g is service function description, namely service function and behavior description; p is an attribute description, and mainly includes non-functional attributes such as Cost (Cost), Time (Time), and the like.
An Abstract Service (AS) only contains function description and interface information, and is a basic logic unit constituting a business process. An AS contains multiple CSs, which may be provided by different service providers, have different QoS values, but all have the same functions and invocation interfaces. The number of CSs contained in each class of AS may vary.
And (4) analyzing, modeling and verifying Business Processes (BP) according to the functional requirements of the user to construct the Business Processes meeting the requirements of the user. The business process is formed by connecting a plurality of AS, and the abstract services have control relations of sequence, selection, parallelism, circulation and the like.
Suppose a BP consists of N ASs { AS1，AS2，…，ASNAnd each AS has M CS candidates with the same function, all CS candidates are { CS }11，CS12，…，CS1M，CS21，CS22，…，CS2M，…，CSN1，CSN2，…，CSNMOne CS has QN QoS attributes (QoS)1，QoS2，…，QoSQNSuch as (c). The Web service combination is to select a concrete service for each abstract service of the business process, so that the overall evaluation of the business process is optimal, and the selected CS set is called as a combined service SC.
Fig. 2 is a schematic diagram of a service flow according to a second embodiment of the present invention. AS shown in fig. 2, a BP may be composed of three ASs or four ASs, and overall QoS corresponding to a service flow composed of different CS sets may be different. Therefore, the Web services with small granularity are mutually communicated and cooperated, the service function with large granularity can be realized, and a service developer can meet various complex function requirements.
In this embodiment, a set number of solutions are randomly generated, each solution corresponding to a different AS or CS set. By utilizing the ant colony algorithm, non-inferior solutions can be continuously searched to form a pareto optimal solution set based on the solutions and other constructed solutions, and a service combination model in the pareto optimal solution set has better QoS and can be finally called by a service.
It should be noted that, due to the incommerability and contradiction between QoS attributes of services, it is difficult to normalize values to a uniform measurement space, the service field is diversified, and uniform weight information cannot be provided, so that a QoS-based service composition problem generally does not have an optimal solution in a general sense, and a general means for solving such a multi-objective problem is to obtain a solution set, which is not inferior to other solutions, called a non-inferior solution set, that is, a Pareto (Pareto) optimal solution set, by performing trade-off and compromise processing on each optimization objective.
For example, QoS describes the ability of a product or service to meet customer demand, and may be described in terms of concurrent processing power, throughput, security, and the like. For practical and scalability reasons, the QoS of the Web service is considered in the embodiment mainly from five aspects of response time T, cost of service (price) C, availability a, reliability R and reputation Re.
And S120, updating the pareto solution set according to the set number of solutions and releasing the initial pheromone.
In this embodiment, the generated solutions of the set number may form a pareto solution set, and in the subsequent iteration process, if the newly constructed solution is better than one or more solutions in the pareto solution set, the newly constructed solution may replace a relatively inferior solution in the pareto solution set, so that the pareto solution set is continuously updated.
In addition, the embodiment also provides an initial pheromone distribution strategy. The initial pheromone values of the basic ant colony are the same and are not distinguished, so that the initial convergence of the algorithm is slow, and therefore, after multiple iterations, the pheromones of all paths generate fall to accelerate the convergence of the ant colony algorithm. In this embodiment, a set number of solutions are randomly generated, the Pareto solution set is updated by using the solutions, and finally, pheromones are released by using the solutions in the Pareto solution set as initial distribution of the pheromones.
The process of releasing the initial pheromone can be described as:
inputting: the number of solutions PN generated randomly;
and (3) outputting: the initial pheromone Tao.
S130, in each iteration process, a solution is constructed according to the transition probability among different specific services, and the pareto solution set and the initial pheromone are updated by the solution.
In the embodiment, through repeated iteration search of non-inferior solutions, a new solution can be constructed according to the transition probability among different specific services in each iteration process. Specifically, there are many possibilities from the CS of one AS to the CS of the next AS, and in the case that one BP includes multiple ASs, there are many possibilities of selecting different CSs among the multiple ASs, and different CSs may finally constitute a complete BP, i.e., a solution is constructed, and in this process, the transition probability between different CSs needs to be calculated.
Then, the Pareto solution set is updated with this constructed solution, and the volatilization operation of the pheromone is performed to update the initial pheromone, with the final pheromone concentration being higher for the solution, the target value being better.
Specifically, in updating the Pareto solution set with the solution, if the solution can dominate one or more solutions in the Pareto solution set, the solution dominated by the solution in the Pareto solution set may be replaced with the solution.
Wherein, when the following 2 conditions are all satisfied, it is called solution x1Dominating solution x2(taking optimization objective as an example):
1)fk(x1)≤fk(x2) (k-1, 2, …, QN), i.e. x1Is no more than x2A difference;
2)
i.e. there is at least one objective function fk(x) So that x is1Target value of better than x2The target value of (2).
That is, the constructed solution is sca, and if there is one solution sci in the Pareto solution set, each target value of sca is not worse than the corresponding target value of sci, and sca has at least one target value better than the corresponding target value of sci, sca can be inserted into the Pareto solution set, and sci can be removed from the Pareto solution set.
The process of updating the Pareto solution set with this solution can be described as:
inputting: constructing a solution sca and a Pareto solution set PList;
and (3) outputting: PList, insert success tag IsInsert.
Wherein, Isgood represents whether the constructed solution meets the requirement; IsInsert indicates whether the constructed solution is inserted into PList.
S140, whether an iteration stop condition is satisfied? If yes, go to S150; otherwise, the next iteration is entered, and the process returns to the step S130.
And S150, outputting the pareto optimal solution set.
Specifically, when the requirement of iteration stop is met, the obtained Pareto solution set is the Pareto optimal solution set, and a service combination model in the Pareto optimal solution set has better service quality, cannot be dominated by any other feasible solution, and can be used as a basis of an optimized Web service combination model.
The iteration stop condition is, for example, that the iteration number reaches a set number, or that updating of the Pareto solution set does not occur in the iteration process exceeding the set number, and the like.
And S160, generating an optimized Web service combination model according to the output pareto optimal solution set for service calling.
Specifically, by using the non-inferior solution in the Pareto optimal solution set, the Web service combination model generated by the corresponding AS and the CS has better service quality and can be used for service invocation.
According to the service combination optimization method provided by the embodiment of the invention, the non-inferior solution is searched through multiple iterations based on the ant colony algorithm, the initial pheromone distribution strategy is provided, the performance of the combination optimization algorithm is improved, the dynamic property of the combination optimization problem can be adapted, and the Web service combination model with the best QoS (quality of service) is provided on the basis.
Optionally, constructing a solution according to transition probabilities between different specific services includes: calculating a selection probability for each ant between a concrete service of one abstract service and all concrete services of the next abstract service; and selecting a concrete service of a next abstract service for the concrete service by adopting a betting rotation method according to the selection probability until a complete solution is obtained.
For example, for an individual ant, the slave ASnCS ofn,1Respectively to ASn+1CS ofn+1,1、CSn+1,2And CSn+1,3Three selection probabilities can be calculated for CSn+1,1、CSn+1,2And CSn+1,3The selection probability of (2) is in direct proportion to the fitness value thereof, and the greater the fitness is, the greater the selection probability of the CS is; by adopting the betting round method, the CS with the maximum fitness can be selected according to the accumulated selection probability of each individual in multiple iterations until a complete solution is obtained. By adopting the betting rotation method, the randomness loss of the algorithm can be avoided, and the diversity of the algorithm is improved, so that more non-inferior solutions can be searched.
Alternatively, the probability of selection from a concrete service of one abstract service to a concrete service of the next abstract service is based on the following disclosureCalculating the formula:
wherein, P(i,a)(j,b)For the selection probability from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ tau [ [ tau ](i,a)(j,b)]For the pheromones from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ eta ] eta(j,b)]For the heuristic information of the b-th concrete service of the j-th abstract service, α and β represent the relative importance of the pheromone and the heuristic information, respectively.
Optionally, updating the initial pheromone with the solution includes: and releasing corresponding pheromones for the concrete services of each corresponding abstract service by using the solution so as to update the initial pheromone.
In this embodiment, in the process of updating the initial pheromone, the difference information L of the QoS corresponding to every two CSs in the solution is used to release the corresponding pheromone, and the smaller the L value, the more pheromones are released by the solution. If the QoS value of each CS of a solution is poor and its values are close, the corresponding L value is small.
Optionally, updating the initial pheromone with the solution includes: calculating an ideal solution and a worst solution of the Web service combination; respectively calculating a first Euclidean distance between the solution and an ideal solution and a second Euclidean distance between the solution and a worst solution; if the solution is able to dominate at least one solution in the pareto solution set, the initial pheromone is updated according to a ratio of the first euclidean distance and the second euclidean distance.
The ideal solution (also referred to as an ideal optimal solution) is a vector f of the ideal solutionbIs the point in the solution space that contains the optimal value for each single target, which may not be reached, and is a target solution that the algorithm tries to achieve; similarly, the vector f of the worst solutionwIs the point in the solution space that contains the worst value for each single target. The closer the solution to the worst solution, the worse.
Definition of db、dwRespectively one solution and the ideal solution and the worst solution, and using the ratio of the two solutionsD is a value Db/dwThe update of the pheromone may be directed. Wherein d isbCan be expressed as:
dwcan be expressed as:
it should be noted that, in the process of performing iteration, only the solution that can be added to the Pareto solution set can perform an update operation on the pheromone. The pheromone update value may be calculated according to the following formula: delta tau(i,a)(j,b)Q is an empirically set constant, typically 1, and D is the ratio of the euclidean distance of one solution to the ideal, worst solution. The more pheromone values of a path accumulate, the more likely the path is the optimal path.
Optionally, calculating the ideal solution and the worst solution of the Web service combination includes: calculating the optimal solution and the worst solution of each optimization target, wherein the optimal solution of each optimization target forms an ideal solution, and the worst solution of each optimization target forms a worst solution; wherein the optimization objectives include response time, cost of service, availability, reliability, and reputation.
The process of calculating the ideal solution and the worst solution can be described as:
inputting: QoS value data of all CSs;
and (3) outputting: ideal solution f of solution spacebAnd worst solution fw。
Optionally, the method further includes: randomly carrying out unit variation on one solution in the pareto solution set to obtain a variation solution; if the variant solution can dominate at least one solution in the pareto solution set, updating the pareto solution set and the initial pheromone according to the variant solution.
In this embodiment, a local optimization strategy is provided, and for a problem that an ant colony algorithm is likely to fall into local optimization, a mutation operation is adopted to perform local optimization, specifically, one solution in a Pareto solution set is randomly copied, one bit of the solution is randomly selected to perform random mutation to form a variant solution, then the Pareto solution set is tried to be updated by the variant solution, if the variant solution dominates one or more solutions in the Pareto solution set, the variant solution is added to the Pareto solution set, and the solution dominated by the variant solution is removed from the Pareto solution set.
The process of local optimization can be described as:
inputting: performing PList on pheromone Tao and Pareto solution sets;
and (3) outputting: pheromone Tao, Pareto disaggregates PList.
Randomly extracting a solution scp from PList;
generating a random number rN with a value of 0N and a random number rM from 0 to M;
the following describes an exemplary procedure of the service composition optimization method of the present embodiment:
step 1: algorithm initialization: initializing various parameters of the algorithm and a Pareto solution set (service composition model), initializing an ant population, and placing ants in a starting S node.
Step 2: release of initial pheromone: the Pareto solution set is updated with randomly generated PN solutions, and the initial pheromone is released with the interpretation in the Pareto solution set. Let the iteration count NC be 1, the iteration starts.
And step 3: calculating the selection probability of all the CS from the current CS to the next AS for each ant, selecting CS by the round-robin method, adding the serial number to the path List of the ant, aggregating the QoS value until the ant obtains a complete solution, and trying to update the Pareto solution set by the solution. In the process of constructing the solution, when an ant reaches a CS under an abstract service, the transition probabilities of the current CS and all candidate CSs included in the next AS need to be calculated.
And 4, step 4: updating pheromone: each path performs the volatilization of pheromones. In the iteration, the solution of the Pareto solution set can be successfully updated, and corresponding pheromones can be released on the passing path.
And 5: and (3) local optimization strategy: randomly copying one solution in the Pareto solution set, carrying out unit variation to form a new solution, and trying to update the Pareto solution set by using the new solution. If the updating is successful, the pheromone is updated by the aid of the information.
Step 6: NC is NC + 1. If NC is larger than NCmax, ending iteration and outputting a Pareto optimal solution set; otherwise, returning to the step 3 and entering the next iteration.
The service combination optimization method of the embodiment provides two improvement strategies based on the ant colony algorithm: the initial pheromone distribution strategy and the local optimization strategy improve the searching performance of the algorithm by improving the ant colony algorithm, more non-inferior solutions can be found, the dynamic property of the combination optimization problem can be adapted, the Web service combination scheme with optimal effectiveness can be obtained in a short time, the accuracy of service calling is ensured, and the service performance is improved.
Example two
Fig. 3 is a schematic structural diagram of a service composition optimization apparatus according to a second embodiment of the present invention. The service composition optimization apparatus provided in this embodiment includes:
a generating module 210, configured to randomly generate a set number of solutions, where each solution corresponds to one business process, each business process includes multiple abstract services, and each abstract service includes multiple concrete services;
an initial release module 220, configured to update the pareto solution set according to the set number of solutions and release an initial pheromone;
an iteration module 230, configured to construct a solution according to transition probabilities between different specific services in each iteration process, and update the pareto solution set and the initial pheromone with the solution; if the iteration stop condition is met, outputting a pareto optimal solution set;
and the optimizing module 240 is configured to generate an optimized Web service combination model according to the output pareto optimal solution set for service invocation.
The service combination optimization device provided by the second embodiment of the invention searches non-inferior solutions through multiple iterations based on the ant colony algorithm, provides an initial pheromone distribution strategy, improves the performance of the combination optimization algorithm, can adapt to the dynamics of the combination optimization problem, and provides a Web service combination model with the best QoS on the basis.
On the basis of the above embodiment, the iteration module 230 includes:
the probability calculation unit is used for calculating the selection probability from one concrete service of one abstract service to all concrete services of the next abstract service for each ant;
and the solution construction unit is used for selecting a concrete service of the next abstract service for the concrete service by adopting a betting rotation method according to the selection probability until a complete solution is obtained.
On the basis of the above embodiment, the probability of selection from one concrete service of one abstract service to one concrete service of the next abstract service is calculated according to the following formula:
wherein, P(i,a)(j,b)For the selection probability from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ tau [ [ tau ](i,a)(j,b)]For the pheromones from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ eta ] eta(j,b)]And the alpha and the beta respectively represent the relative importance of the pheromone and the heuristic information for the b specific service of the j abstract service.
On the basis of the above embodiment, the iteration module 230 includes:
and the updating unit is used for releasing corresponding pheromones for the concrete services of each corresponding abstract service by using the solution so as to update the initial pheromone.
On the basis of the above embodiment, the iteration module 230 includes:
the solution calculation unit is used for calculating an ideal solution and a worst solution of the Web service combination;
a distance calculation unit for calculating a first euclidean distance between the solution and the ideal solution and a second euclidean distance between the solution and the worst solution, respectively;
and an updating unit configured to update the initial pheromone according to a ratio of the first euclidean distance to the second euclidean distance if the solution can dominate at least one solution in the pareto solution set.
On the basis of the foregoing embodiment, the solution calculating unit is specifically configured to:
calculating an optimal solution and a worst solution of each optimization target, wherein the optimal solution of each optimization target forms the ideal solution, and the worst solution of each optimization target forms the worst solution;
wherein the optimization objectives include response time, cost of service, availability, reliability, and reputation.
On the basis of the above embodiment, the apparatus further comprises:
a variation module, configured to perform unit variation on one solution in the pareto solution set at random to obtain a variation solution;
an updating module, configured to update the pareto solution set and the initial pheromone according to the variant solution if the variant solution can dominate at least one solution in the pareto solution set.
The service composition optimization device provided by the second embodiment of the present invention can be used for executing the service composition optimization method provided by any of the above embodiments, and has corresponding functions and beneficial effects.
EXAMPLE III
Fig. 4 is a schematic diagram of a hardware structure of a server according to a fourth embodiment of the present invention. Further, the server includes, but is not limited to: the system comprises an industrial integration server, a system background server and a cloud server. As shown in fig. 4, the server provided in the present application includes a storage device 32, a processor 31, and a computer program stored on the storage device and executable on the processor, and when the processor 31 executes the computer program, the service composition optimization method described above is implemented.
The server may also include a storage device 32; the processor 31 in the server may be one or more, and fig. 4 illustrates one processor 31 as an example; storage 32 is used to store one or more programs; the one or more programs are executed by the one or more processors 31, so that the one or more processors 31 implement the service composition optimization method as described in the embodiment of the present application.
The server further comprises: a communication device 33, an input device 34 and an output device 35.
The processor 31, the storage device 32, the communication device 33, the input device 34 and the output device 35 in the server may be connected by a bus or other means, and the bus connection is taken as an example in fig. 4.
The input device 34 may be used to receive entered numeric or character information and to generate key signal inputs relating to user settings and function control of the server. The output device 35 may include a display device such as a display screen.
The communication means 33 may comprise a receiver and a transmitter. The communication device 33 is configured to perform information transceiving communication according to the control of the processor 31.
The storage device 32, which is a computer-readable storage medium, may be configured to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the service composition optimization method according to the embodiments of the present application (for example, the generation module 210, the initial release module 220, the iteration module 230, and the optimization module 240 in the service composition optimization device). The storage device 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the server, and the like. Further, the storage 32 may include high speed random access memory, and may also include non-volatile storage, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the storage device 32 may further include a storage device remotely located from the processor 31, which may be connected to a server over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
On the basis of the above embodiments, the present embodiment further provides a computer-readable storage medium on which a computer program is stored, the program, when executed by a service composition optimization apparatus, implementing a service composition optimization method in any of the above embodiments of the present invention, the method including: randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services; updating the pareto solution set according to the set number of solutions and releasing the initial pheromone; in each iteration process, constructing a solution according to the transition probability among different specific services, and updating the pareto solution set and the initial pheromone by using the solution; if the iteration stop condition is met, outputting a pareto optimal solution set; and generating an optimized Web service combination model according to the output pareto optimal solution set for service calling.
Embodiments of the present invention provide a storage medium including computer-executable instructions, which may take the form of any combination of one or more computer-readable media, such as a computer-readable signal medium or storage medium. The computer-readable storage medium may be, for example, but is not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory device (RAM), a Read Only Memory device (ROM), an Erasable Programmable Read Only Memory device (EPROM), a flash Memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. A computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take a variety of forms, including, but not limited to: an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for optimizing a service composition, comprising:
randomly generating a set number of solutions, wherein each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services;
updating the pareto solution set according to the set number of solutions and releasing the initial pheromone;
in each iteration process, constructing a solution according to the transition probability among different specific services, and updating the pareto solution set and the initial pheromone by using the solution;
if the iteration stop condition is met, outputting a pareto optimal solution set;
and generating an optimized Web service combination model according to the output pareto optimal solution set for service calling.
2. The method of claim 1, wherein constructing a solution based on transition probabilities between different specific services comprises:
calculating a selection probability for each ant between a concrete service of one abstract service and all concrete services of the next abstract service;
and selecting a concrete service of a next abstract service for the concrete service by adopting a betting rotation method according to the selection probability until a complete solution is obtained.
3. The method of claim 2, wherein the probability of selection from a concrete service of one abstract service to a concrete service of a next abstract service is calculated according to the following formula:
wherein, P(i,a)(j,b)For the selection probability from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ tau [ [ tau ](i,a)(j,b)]For the pheromones from the a-th concrete service of the ith abstract service to the b-th concrete service of the jth abstract service, [ eta ] eta(j,b)]And the alpha and the beta respectively represent the relative importance of the pheromone and the heuristic information for the b specific service of the j abstract service.
4. The method of claim 3, wherein updating the initial pheromone with the solution comprises:
and releasing corresponding pheromones for the concrete services of each corresponding abstract service by using the solution so as to update the initial pheromone.
5. The method of claim 1, wherein updating the initial pheromone with the solution comprises:
calculating an ideal solution and a worst solution of the Web service combination;
respectively calculating a first Euclidean distance between the solution and the ideal solution and a second Euclidean distance between the solution and the worst solution;
if the solution can dominate at least one solution in the pareto solution set, updating the initial pheromone according to a ratio of the first Euclidean distance to the second Euclidean distance.
6. The method of claim 5, wherein computing the ideal solution and the worst solution for the combination of Web services comprises:
calculating an optimal solution and a worst solution of each optimization target, wherein the optimal solution of each optimization target forms the ideal solution, and the worst solution of each optimization target forms the worst solution;
wherein the optimization objectives include response time, cost of service, availability, reliability, and reputation.
7. The method of claim 1, further comprising:
randomly carrying out unit variation on one solution in the pareto solution set to obtain a variation solution;
updating the pareto solution set and the initial pheromone according to the variant solution if the variant solution can dominate at least one solution in the pareto solution set.
8. A method for optimizing a service composition, comprising:
the generation module is used for randomly generating a set number of solutions, each solution corresponds to one business process, each business process comprises a plurality of abstract services, and each abstract service comprises a plurality of concrete services;
the initial release module is used for updating the pareto solution set according to the set number of solutions and releasing the initial pheromone;
the iteration module is used for constructing a solution according to the transition probability among different specific services in each iteration process, and updating the pareto solution set and the initial pheromone by using the solution; if the iteration stop condition is met, outputting a pareto optimal solution set;
and the optimization module is used for generating an optimized Web service combination model according to the output pareto optimal solution set for service calling.
9. A server, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the service composition optimization method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for service composition optimization according to any one of claims 1 to 7.
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