CN112306665B - Service integration method driven by sequence QoS - Google Patents
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Abstract
The invention discloses a service integration method driven by a sequence QoS, which comprises the following steps: presetting a sequence combination flow chart, and selecting QoS attribute characteristics; processing the QoS attribute characteristics to obtain an initial vector CQoS of a service sequence sc, normalizing the initial vector CQoS to obtain a normalized vector CQoS ', and further calculating a service score ScorCQoS'; extracting a data object subset which is not dominated by any other candidate service object from each candidate service object database corresponding to the task node in the preset sequence combination flow chart by using Skyline calculation, and taking the data object subset as an initial search space for solving an ant colony algorithm; and solving an allocation scheme corresponding to the optimal solution in the initial search space according to the ant colony algorithm. The method solves the problems of data flooding and user preference, and improves the efficiency of the integration process and the service quality of the service sequence.
Description
Technical Field
The invention relates to the technical field of service integration, in particular to a service integration method driven by a sequence QoS.
Background
Currently, more and more enterprises, organizations, or individuals release owned physical resources into the internet in the form of services. The service provider virtualizes service resources according to the business process division, and builds an independent service module. These modules often come in the form of applications or APIs and can be deployed, run, and managed independently in the environment. The user can call the service with the corresponding function only through the lightweight application software, the interface or the API (Application Programming Interface). However, due to limited service functions provided by the service provider, in many cases, a single service may not meet the functional requirements of the user, and at this time, effective integration needs to be performed in a service set with limited functions to meet the functional requirements of the user, and the combined service is guaranteed to have good service quality (Quality of Service, qoS), but there is a certain challenge to solve the service integration problem by relying on the user.
Services in the real world have multiple execution sequences, such as sequential execution, concurrent execution and sequential and concurrent mixed execution, due to different business functions in the integration process. To better solve the service integration problem, many scholars have given solutions to parallel service serialization. Therefore, only the sequentially executed service sequences are considered. As shown in fig. 1, in a service flow, each service cluster corresponds to a task node t i ，i∈[1，5]Each task node t i Corresponding to candidate service set T i I.e., each service cluster corresponds to a candidate service set. In order to meet the service requirements of users, a specific service instance should be scheduled from each task node, so that the combined service composed of a plurality of atomic services can provide the functions which cannot be provided by a single atomic service for users.
As shown in FIG. 1, since service instances within a candidate service set are not unique, multiple integration schemes are generated, corresponding to multiple service instance sequences. In order to obtain the best service sequence set, the scheme also adopts the service quality as the basis for distinguishing the service sequences. But differs from atomic services in that the quality of service of a service sequence cannot be directly considered and directly handled independently of the quality of service of each atomic service that participates in its composition. Firstly, the problem of "flooding" of data and the problem of user preference caused by different operation units of each characteristic attribute of the service quality can not give a complete solution, and secondly, many integration methods are suitable for small business fields, and the efficiency and the service quality of the complex and large-scale service integration process are not satisfactory.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the present invention is to provide a service integration method driven by a sequential QoS, which can improve the solving quality and the solving rate of a service integration algorithm.
To achieve the above objective, an embodiment of the present invention provides a service integration method driven by a sequential QoS, including the following steps: step S1, presetting a sequence combination flow chart, and selecting quality of service (QoS) attribute characteristics; step S2, processing the QoS attribute characteristics to obtain an initial vector CQoS of a service sequence sc, normalizing the initial vector CQoS to obtain a normalized vector CQoS ', and further calculating a service score ScorCQoS'; step S3, using Skyline to calculate and extract a data object subset which is not dominated by any other candidate service object from each candidate service object database corresponding to the task node in the preset sequence combination flow chart, and using the data object subset as an initial search space for solving an ant colony algorithm; and S4, solving an allocation scheme corresponding to the optimal solution in the initial search space according to the ant colony algorithm.
The service integration method driven by the sequence QoS in the embodiment of the invention provides complete definition by aiming at the service quality calculation method in service sequence integration, and solves the problem of data inundation in the traditional service integration problem and the problem that users have different preference on different attributes of QoS; the ant colony algorithm based on Skyline improves the solving quality and the solving speed of the service integration algorithm.
In addition, the service integration method of the sequential QoS driver according to the above embodiment of the present invention may further have the following additional technical features:
further, in one embodiment of the invention, the quality of service QoS attribute characteristics include response time, availability, throughput, and success rate.
Further, in an embodiment of the present invention, in the step S2, based on the preset sequence combination flowchart, response time, availability, throughput and success rate in the QoS attribute feature are calculated, respectively, to obtain an initial vector CqoS of the service sequence sc.
Optionally, in one embodiment of the present invention, the response time is a sum of response times of atomic service calls passing from the start node to the end node; the availability is the product of the availability of each atomic service passing from the start node to the end node; the throughput is the minimum value of each atomic service throughput passing from the starting node to the ending node; the success rate is the success rate product of success of each atomic service call passing from the starting node to the ending node.
Further, in one embodiment of the present invention, the initial vector CQoS normalization processing in step S2 is: according to the QoS attribute characteristics, the availability, the throughput and the success rate are specified as positive attribute characteristics, and the response time is specified as positive and negative attribute characteristics; respectively carrying out positive Qos normalization and negative Qos normalization according to a normalization strategy to obtain a normalized vector CQoS'; the service score is calculated from the user preference settings and the normalized vector CQoS'.
Further, in one embodiment of the present invention, the ant colony algorithm includes a foraging rule, an optimal solution calculation rule, and a pheromone update principle.
Further, in one embodiment of the present invention, the step S4 includes: step S401, selecting a specific service instance for each task node in the initial search space based on the foraging rule; step S402, after all task nodes are distributed with service examples, based on the optimal solution calculation rule, the quality of the current service combination scheme is measured by using a service score ScorCQoS'; step S401 is iteratively executed until all ants are solved to obtain an allocation mode and a corresponding service score ScorCQoS', namely an allocation scheme corresponding to the optimal solution; and step S404, carrying out pheromone updating based on the pheromone updating principle through the allocation scheme corresponding to the optimal solution.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a diagram of an example service sequence;
FIG. 2 is a flow chart of a sequential QoS driven service integration method according to one embodiment of the present invention; .
FIG. 3 is a flow chart of an ant colony algorithm solution of one embodiment of the present invention;
FIG. 4 is a run-time histogram comparison of the ant colony algorithm ACO, skyline in combination with the ant colony algorithm SkylineACO, the reinforcement learning method RL and the modified particle swarm algorithm MPSO of an embodiment of the present invention;
FIG. 5 is a schematic diagram of Skyline computation time columns according to one embodiment of the invention;
FIG. 6 is a comparison graph of solving quality broken lines of an ant colony algorithm ACO, skyline combined with an ant colony algorithm SkylineACO, a reinforcement learning method RL and a modified particle swarm algorithm MPSO according to one embodiment of the present invention;
FIG. 7 is a graph showing a quality comparison of Skyline combined with ant colony algorithm SkylineACO and reinforcement learning method RL solution according to an embodiment of the present invention;
FIG. 8 is a graph showing the sensitivity of the iteration number with the number of task nodes of 10 for the ant colony algorithm ACO, skyline, skylineACO, reinforcement learning method RL and improved particle swarm algorithm MPSO, in accordance with one embodiment of the present invention;
fig. 9 is a graph showing the sensitivity of the number of iterations of 20 for the ant colony algorithm ACO, skyline in combination with the ant colony algorithm SkylineACO, the reinforcement learning method RL, and the modified particle swarm algorithm MPSO according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The following describes a sequential QoS driven service integration method according to an embodiment of the present invention with reference to the accompanying drawings.
Fig. 2 is a flow chart of a sequential QoS driven service integration method of one embodiment of the present invention.
As shown in fig. 2, the service integration method of the sequence QoS driver includes the following steps:
in step S1, a sequence combination flowchart is preset, and a quality of service QoS attribute feature is selected.
It should be noted that the QoS attribute features selected in the embodiment of the present invention are response time (response time), availability (Availability), throughPut (ThroughPut), and success rate (success), but are not limited thereto, and may be adjusted by those skilled in the art according to actual requirements.
In step S2, the quality of service QoS attribute feature is processed to obtain an initial vector CQoS of the service sequence sc, and the initial vector CQoS is normalized to obtain a normalized vector CQoS ', and then a service score CQoS' is calculated.
In particular, to describe QoS for a service sequence, embodiments of the present invention will atomic service s ij Is denoted as vector QoS (QoS) ResponseTime ，qos Availability ，qos ThroughPut ，qos Successability ) I represents the task node number, and j represents the candidate service combination internal service instance number corresponding to the task node i. For example, optionally s 11 ∈T 1 ，s 21 ∈T 2 ，s 31 ∈T 3 ，s 41 ∈T 4 ，s 51 ∈T 5 Then take s 11 、s 21 、s 31 、s 41 Sum s 51 Constitute a service sequence sc 1 . QoS of service sequence sc is expressed as CQoS (CQoS ResponseTime ，cqos Availability ，cqos ThroughPut ，cqos Successability ) The calculation methods are as follows:
response time: a preset sequence combination flow chart, wherein the response time of the combination service is the sum of the response time of each atomic service call passing from the starting node to the ending node, and the response time cqos of the combination service ResponseTime Calculated by equation (1).
Wherein N represents the number of atomic services related to the sequential combination service, namely the number of task nodes, s ij And representing service instances numbered j in the candidate service set corresponding to the ith task node, wherein the j is dependent on the number of the instances in the candidate service set.
Availability of: a preset sequence combining flowchart whose combined service availability is the product of the availability of each atomic service passing from the start node to the end node is calculated by formula (2).
Wherein N represents the number of atomic services related to the sequential combination service, namely the number of task nodes, s ij And representing service instances numbered j in the candidate service set corresponding to the ith task node, wherein the j is dependent on the number of the instances in the candidate service set.
Throughput: a preset sequence combining flowchart, the combined service throughput of which is the minimum value of each atomic service throughput passing from the start node to the end node, is calculated by the formula (3).
cqos ThroughPut ＝Min(qos ThroughPut (s ij )),i∈[1,N] (3)
Success rate: the combined service success rate of the preset sequence combined flow chart is the successful success rate product of each atomic service call passing from the starting node to the ending node, and the combined service success rate is calculated by a formula (4).
Further, in one embodiment of the present invention, the initial vector CQoS normalization processing in step S2 is:
according to the QoS attribute characteristics, the usability, throughput and success rate are specified as positive attribute characteristics, and the response time is specified as positive and negative attribute characteristics;
and respectively carrying out positive Qos normalization and negative Qos normalization according to the normalization strategy to normalize the initial vector CqoS and obtain a service object set CQoS'.
Specifically, the initial vector CQoS (CQoS) of the service sequence sc is calculated by the above steps ResponseTime ，cqos Availability ，cqos ThroughPut ，cqos Successability ) When calculating the service sequence aggregate value by CQoS, the CQoS value needs to be normalized to be [0,1 ] in order to prevent the larger range data from causing the smaller range data to be "submerged" due to the different characteristic value ranges]Within the range. According to the QoS attribute characteristics of the quality of service (QoS), the availability, the throughput and the success rate are positive QoS attribute characteristics, the response time is negative QoS attribute characteristics, the normalization strategies are divided into positive QoS normalization and negative QoS normalization, and the normalization formulas are shown in the following formulas (5) and (6):
front face:
negative:
note that, the CQoS of the service sequence is related to the CQoS value in the normalization process max And cqos min Maximum and minimum values of the cqos values of the service sequences corresponding to all possible combining strategies. Taking fig. 1 as an example, the number of task nodes is 5, assuming that the number of candidate services corresponding to each task node is 100, the combined strategy is 100 x 100, corresponding to 1005 of the combined services, generating 1005 CQoS values, CQoS for example, response time ResponseTimeMax For maximum response time, CQoS, in CQoS in the 1005 group services ResponseTimeMin The minimum response time in CQoS in these 1005 service sequences.
Considering that the candidate service set corresponding to each task node may be largerIt is not possible to construct all possible combination strategies to compute all CQoS to find CQoS max And cqos min The embodiment of the invention therefore proposes the following calculation method to solve the cqos of each QoS attribute max And cqos min 。
First, qos for each candidate service set is calculated max And qos min . Taking the workflow of fig. 1 as an example, there are a total of 5 candidate service sets T 1 、T 2 、T 3 、T 4 And T 5 Respectively calculating qos of each attribute value in candidate service set max And qos min Solving each QoS attribute feature to obtain N maximum values and N minimum values, wherein N is the number of candidate service sets, 5 in the number, and calculating cqos according to the solving result max And cqos min The respective attribute feature calculation methods are as follows.
Response time: according to the candidate service set T i Calculating to obtain a response time maximum qos ResponseTimeMaxi Minimum value qos of response time ResponseTimeMini The maximum response time of the combined service sequence is the sum of the maximum response time of the atomic services in each candidate service set, the minimum response time of the combined service sequence is the sum of the minimum response time of the atomic services in each candidate service set, and then cqos ResponseTimeMax And cqos ResponseTimeMin The calculation method is shown in formula (7), wherein i is the task node number.
Availability of: according to the candidate service set T i Calculating to obtain the availability maximum qos AvailabilityMaxi And availability minimum qos AvailabilityMini The maximum value of the combined service sequence availability is the product of the maximum availability of the atomic service in each candidate service set, the minimum value of the combined service sequence availability is the product of the minimum availability of the atomic service in each candidate service set, cqos AvailabilityMax And cqos AvailabilityMin The calculation is shown in formula (8).
Throughput: according to the candidate service set T i Calculating to obtain throughput maximum value qos ThroughPutMaxi And throughput minimum value qos ThroughPutMini The maximum throughput of the combined service sequence is qos ThroughPutMaxi The maximum value of (2) and the minimum throughput of the combined service sequence is qos ThroughPutMini Minimum value of (c), cqos ThroughPutMax And cqos ThroughPutMin The calculation is shown in formula (9).
Success rate: according to the candidate service set T i Calculating to obtain qos SuccessabilityMaxi And qos SuccessabilityMini Method for calculating maximum and minimum success rate of combined service sequence and similar to availability, cqos SuccessabilityMax And cqos SuccessabilityMin The calculation is shown in formula (10).
Initial combining of CQoS (CQoS) of services through the above steps ResponseTime ，cqos Availability ，cqos ThroughPut ，cqos Successability ) Standardized as CQoS' (CQoS ResponseTime '，cqos Availability '，cqos ThroughPut '，cqos Successability '). Given that users have different preferences for different attributes of QoS, a QoS feature (QoS 1 ，qos 2 ，qos 3 ，...，qos m ) QoS weights P (P) are set according to user preferences 1 ，p 2 ，p 3 ，...，p m ) Representing the user's attention to QoS features, which satisfies the constraint as shown in equation (11).
The CQOS ' aggregate value, i.e., the service score CQOS ', is calculated from the user preference setting and CQOS ' via equation (12).
In step S3, a subset of data objects that are not dominated by any other candidate service objects is extracted from each candidate service object database corresponding to the task node in the preset sequence combination flowchart by Skyline calculation, and the subset of data objects is used as an initial search space for solving the ant colony algorithm.
It should be noted that Skyline computation extracts a set of data objects from a database that are not governed by any other data objects, so as to find potential applications in multi-objective decision making, data mining, data visualization, etc., where the classical example is a hotel selection problem, i.e. finding a hotel that is close to the sea and inexpensive in a large amount of hotel information. Essentially, skyline computation is a data extraction method that reflects the internal features of the data set.
For a service object with multidimensional QoS information, namely, the QoS attribute characteristics of the service object are not unique, the service object can be regarded as a data object with multidimensional QoS, and the data object with better quality is selected by comparing different data objects, so that a foundation is laid for the next service decision. The invention uses Skyline calculation to extract the data object subset which is not dominated by other data objects from the service object set with multi-dimensional service quality, namely, to reduce the service search space and improve the next service combination decision search efficiency.
For example, taking services s1 and s2 as examples, the quality of service is QoS1 (QoS 1 ，qos 2 ，qos 3 ，qos 4 ) And QoS2 (QoS) 1 ，qos 2 ，qos 3 ，qos 4 ) If s 1 Is superior or equal to s in each dimension 2 And at least in one dimension s 1 Is better than s 2 Then call s 1 Dominating s 2 。
For positive QoS, its formalized definition is shown in equation (13), where M is the quality of service dimension.
For negative QoS, the formalized definition is as shown in equation (14).
And extracting service objects which are not dominated by any other candidate service object from each candidate service object database corresponding to the task node through Skyline calculation, thereby forming Skyline service, and taking the Skyline service as an initial search space for solving an ant colony algorithm.
In step S4, the allocation scheme corresponding to the optimal solution in the initial search space is solved according to the ant colony algorithm.
The inspiration of the ant colony algorithm is derived from foraging behavior of ants in nature, the basic principle is that the ants release pheromones related to path information in foraging paths, so that selection basis is provided for all ants to meet the intersection next time, in the advancing process, the ants randomly select intersections which do not walk, otherwise, intersections with the maximum pheromone concentration are selected, finally, the pheromone concentration on the optimal paths is larger and larger, and the ant colony finds the optimal foraging paths.
The ant colony algorithm involves the following several important processes in solving the best service sequence.
Foraging rules: the process of selecting ants to find food each time through the intersection is equivalent to selecting specific service examples from candidate service sets, wherein the service example selection rules mainly comprise two types, if the current ant number is in front of the ant number critical point of the task node, the maximum pheromone distribution is adopted, the ant can select the service example with the highest pheromone concentration, and if the ant is behind the critical point, the service example is randomly selected.
For example: the task point [ i ] =10 indicates that the critical point of the ant number of the ith task node is 10, when the ant selects a service instance at the ith task node, if the ant number is before (including 10) the 10 th task node, the service instance with the maximum pheromone concentration is selected, and if the ant number is after the 10 th task node, the service instance is randomly selected in the candidate service set.
And (3) calculating an optimal solution rule: after selecting service instances for all task nodes, an ant obtains an allocation scheme, namely a combined service scheme, and takes the normalized combined service score ScorCQoS 'as a standard for measuring the quality of the allocation scheme, wherein the higher the score value of the ScorCQoS' is, the better the allocation scheme is.
Principle of updating pheromone: the pheromone plays an important role in selecting service instances when ants perform service instance selection, for example, phenomenonmatrix [ i ] [ j ] =0.1 represents that the concentration of the pheromone for selecting the jth service instance at the ith task node is 0.1. And updating the pheromone after each iteration is finished, wherein the concentrations of all the pheromones are attenuated by p percent, and the concentration of the pheromone corresponding to the optimal solution in the iteration is increased by q percent.
Therefore, step S4 of the present invention specifically includes:
step S401, selecting a specific service instance for each task node in the initial search space based on foraging rules;
step S402, after all task nodes are distributed with service examples, based on an optimal solution calculation rule, measuring the quality of a current service combination scheme by using a service score ScorCQoS';
step S403, iteratively executing step S401 until all ants are solved, and obtaining an allocation mode and a corresponding service score ScoeCQoS', namely an allocation scheme corresponding to the optimal solution;
step S404, carrying out pheromone updating based on the pheromone updating principle through optimally solving the corresponding allocation scheme.
That is, as shown in fig. 3, the combined service procedure for solving the optimal score cqos' using the ant colony algorithm is: selecting a specific service instance for each task node based on foraging rules, measuring the quality of the distribution scheme, namely the service combination scheme based on optimal solution calculation rules after the service instance distribution for all task nodes is completed, repeating the process until all ants solve to obtain a distribution mode and corresponding scheme quality, and updating the pheromone based on an pheromone updating principle through the distribution scheme corresponding to the optimal solution, thereby completing one iteration.
Therefore, the embodiment of the invention reduces the service of a large-scale candidate service set by using Skyline calculation, performs local optimization so as to reduce the solving scale of the ant colony algorithm, improve the solving speed and quality, and then uses the ant colony algorithm to perform global optimization according to the QoS of the combined service to find a service instance sequence solution with the best QoS.
A specific embodiment is presented below to further explain the sequential QoS driven service integration method of the present invention.
The present embodiment employs an international public dataset: the QWS dataset underwent experimental verification of the effectiveness of the algorithm. The QWS dataset is a widely accepted dataset by researchers in the field of service computing, and this particular embodiment retains 4 QoS attribute information of response time, availability, success rate, and throughput on the dataset.
In a specific embodiment, different combined service complexity is simulated by workflow templates of 5, 10, 15, 20 and 25 task nodes, each task node candidate service is 100, processes of solving a combined service solution by an ant colony Algorithm (ACO), a Skyline combined ant colony algorithm (SkylineACO), a reinforcement learning method (RL) and an improved particle swarm algorithm (MPSO as to respectively test the four algorithms, and comparison experiments of the four algorithms are respectively carried out in the aspects of execution time of the algorithm, combined service solution quality, iteration times and the like, the score cqos' of the formula (12) is used as an evaluation standard of the combined service solution quality, and the higher the score is, the better the solution quality is represented.
(1) Algorithm execution time
Setting ACO, skylineACO, RL and MPSO iteration times to be 100, setting the ant colony of ACO and SkylineACO and the particle colony size of MPSO to be 50, and comparing the execution time of the algorithm with that of FIG. 4. The Skyline calculation time is shown in FIG. 5.
Under the same iteration number setting, the execution time of the algorithm increases significantly as the number of task nodes increases. The average execution time of the ACO algorithm is obviously higher than that of other methods, so that the SkylineACO algorithm provided by the invention can obviously reduce the execution time of the original ACO algorithm. Second, the RL algorithm has the lowest average execution time, and SkylineACO is slightly higher than the MPSO algorithm.
(2) The combined service solving quality collects the ScoeCQoS 'of four algorithms under different task node numbers on the basis of the experiment of fig. 4, and the average value is obtained through multiple experiments to obtain the final ScoeCQoS' value of the algorithm, and the experimental comparison result is shown in fig. 6.
As can be seen from fig. 6, in the experiments of different task node numbers, the solution quality of the SkylineACO algorithm provided by the present invention is optimal, and the solution quality of the original ACO algorithm can be significantly improved by SkylineACO. In experimental comparison of the task node numbers of 10, 15 and 20, the quality of the solution is obvious in comparison effect, the quality of the SkylineACO solution is better than that of the MPSO solution, and the RL solution is worst. As the number of task nodes increases, the overall solution quality exhibits a decreasing trend.
From the comparison results of fig. 4 and fig. 6, it can be seen that SkylineACO is superior to ACO algorithm in terms of both algorithm execution time and algorithm solving quality, and that RL algorithm execution time is shortest in algorithm execution time and SkylineACO is highest in algorithm solving quality.
For this reason, to further compare the SkylineACO algorithm with the RL algorithm, the execution time of the RL algorithm is increased by increasing the number of iterations of the RL algorithm, so that the execution time is about the same as that of the SkylineACO algorithm, thereby comparing the quality of solutions of the two algorithms. The experimental results are shown in fig. 7, and the SkylineACO algorithm solution quality is better than the RL algorithm solution quality.
(3) Iteration number sensitivity
In order to compare the influence of the iteration times on the solving quality, the experiments respectively collect the solving quality of four algorithms under the iteration times of 50, 100, 150, 200 and 250, each algorithm carries out 10 experiments under the appointed iteration times, and the average combined service score is calculated to be used as the solving quality standard. Fig. 8 and 9 show the experimental results of the number of task nodes 10 and the number of task nodes 20, respectively.
The result of the experiment of fig. 8 and 9 is synthesized to find that the RL algorithm is most sensitive to the iteration times, the solving quality improvement step of the RL algorithm is the largest along with the increase of the iteration times, the SkylineACO algorithm is inferior, the MPSO improvement is smaller, the ACO algorithm is not improved basically, and the ACO algorithm is inferred to be trapped in a local optimal solution.
In summary, experiments prove that the improved ant colony algorithm calculated by Skyline can remarkably improve the execution efficiency of the algorithm and is superior to a comparison method in solving quality, so that the proposed SkylineACO algorithm can quickly find the global optimal Score CQoS’ And their corresponding combined service schemes.
According to the service integration method driven by the sequence QoS, which is provided by the embodiment of the invention, the problem of data inundation in the traditional service integration problem and the problem of different preference of users for different attributes of QoS are solved by giving complete definition for the service quality calculation method in service sequence integration; the ant colony algorithm based on Skyline improves the solving quality and the solving speed of the service integration algorithm.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. A sequential QoS driven service integration method, comprising the steps of:
step S1, presetting a sequence combination flow chart, and selecting quality of service (QoS) attribute characteristics;
step S2, processing the QoS attribute characteristics to obtain an initial vector CQoS of a service sequence sc, normalizing the initial vector CQoS to obtain a normalized vector CQoS ', and further calculating a service score ScorCQoS';
step S3, using Skyline to calculate and extract a data object subset which is not dominated by any other candidate service object from each candidate service object database corresponding to the task node in the preset sequence combination flow chart, and using the data object subset as an initial search space for solving an ant colony algorithm; and
step S4, solving an allocation scheme corresponding to the optimal solution in the initial search space according to the ant colony algorithm;
wherein the quality of service QoS attribute characteristics include response time, availability, throughput, and success rate;
the initial vector CQoS normalization processing in step S2 is as follows:
according to the QoS attribute characteristics, the availability, the throughput and the success rate are specified as positive attribute characteristics, and the response time is specified as positive and negative attribute characteristics;
respectively carrying out positive Qos normalization and negative Qos normalization according to a normalization strategy to obtain a normalized vector CQoS';
the service score is calculated from the user preference settings and the normalized vector CQoS'.
2. The sequence QoS driven service integration method according to claim 1, wherein in step S2, based on the preset sequence combination flowchart, response time, availability, throughput and success rate in the QoS attribute feature are calculated, respectively, to obtain an initial vector CqoS of the service sequence sc.
3. The sequential QoS-driven service integration method of claim 2, wherein the response time is a sum of response times of atomic service calls passing from a start node to a termination node.
4. The sequential QoS driven service integration method of claim 2, wherein said availability is a product of availability of individual atomic services traversed from a start node to a termination node.
5. The sequential QoS driven service integration method of claim 2, wherein said throughput is a minimum of each atomic service throughput passed from a start node to a termination node.
6. The sequential QoS driven service integration method of claim 2, wherein the success rate is a success rate product of success of each atomic service invocation passed from a start node to a termination node.
7. The sequential QoS driven service integration method of claim 1, wherein the ant colony algorithm comprises foraging rules, optimal solution calculation rules, and pheromone update rules.
8. The sequential QoS-driven service integration method according to claim 7, wherein said step S4 comprises:
step S401, selecting a specific service instance for each task node in the initial search space based on the foraging rule;
step S402, after all task nodes are distributed with service examples, based on the optimal solution calculation rule, the quality of the current service combination scheme is measured by using a service score ScorCQoS';
step S403, iteratively executing step S401 until all ants are solved to obtain an allocation mode and a corresponding service score ScorCQoS', namely an allocation scheme corresponding to the optimal solution;
and step S404, carrying out pheromone updating based on the pheromone updating principle through the allocation scheme corresponding to the optimal solution.
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