CN101719933B - Combination method of manufacturing grid resource services orienting whole life cycle and supporting semantemes - Google Patents
Combination method of manufacturing grid resource services orienting whole life cycle and supporting semantemes Download PDFInfo
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- CN101719933B CN101719933B CN2009102412039A CN200910241203A CN101719933B CN 101719933 B CN101719933 B CN 101719933B CN 2009102412039 A CN2009102412039 A CN 2009102412039A CN 200910241203 A CN200910241203 A CN 200910241203A CN 101719933 B CN101719933 B CN 101719933B
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Abstract
The invention relates to a combination method of manufacturing grid (MGrid) resource services orienting a whole life cycle and supporting semantemes, in particular to a novel implementation method for discovering, combining and optimizing resource services under an MGrid key technology. The method mainly aims to meet the requirements of long period, variety, complexity and high reliability on resource services and the transferring thereof in an MGrid system at present. The invention comprises a resource service discovery framework for supporting an MGrid resource service combination whole life cycle and the semantemes and an MG resource service combination flow under the framework. Via a Web service combination technology in the traditional distributed system and aiming at the characteristics and combined requirements of the MGrid resource services, eight key technologies of task description, task decomposition/demand analysis, resource service function demand matching, resource service flow demand matching, resource service gathering, resource service QoS comprehensive processing, resource service combination and optimization, combined resource service execution engines, and the like in the MGrid service combination are designed and stated according to a thought of bottom-up gathering and top-down decomposition so as to meet the requirement of sharing of the MGrid manufacturing resources. The invention has the advantages of supporting the resource service whole life cycle demand and the semantemes and haing high-efficiency combination, distinctive nuance and strong expansibility.
Description
Technical field
The present invention relates to a kind of semantic manufacturing grid (MGrid) resource service combined method of supporting towards Life cycle; Be a kind of, belong to distributed manufacturing system information integration technical field in novel resource service discovery, combination and the preferred implementation method made under the mesh model.
Background technology
The realization that Development of Grid Technology makes networking make resource-sharing becomes possibility, and (ManufacturingGrid, research MGrid) becomes whole world research focus to make grid.MGrid has realized manufacturing enterprise geographical dispersion, isomery resources integration and has shared, and supports resource optimization reorganization and the collaborative back-up environment of making between enterprise.Its approach is to utilize grid, information, computer and advanced administrative skill etc.; Overcome the obstacle that the distance on the space is brought to the cooperation between enterprise through grid (next generation network), realize geographical comprehensive connection of going up all kinds of manufacturing resources (like design resource, manufacturing resource, technical resource, human resources, Service Source, application system resource, computational resource etc.) of disperseing; And the encapsulation through all kinds of manufacturing resources and integrated; The isomerism and the geographical distribution property of shielding resource; With the transparent way is that the user provides all kinds of manufacturing services; Make the user can be as using local resource to use the resource service among the MGrid easily. its objective is the integrated of all kinds of resources such as the design that realizes enterprise and society to the full extent, manufacturing, technology, manpower, service, application system, calculating and share, make the group of enterprises reach TQCSEFK (quickest to the market-based on the operation of MGrid environment
TIme, highest
QUality, lowest
COst, best
SErvice, cleanest
ENvironment, greatest
FLexibility, and high
KNowledge) target.
In manufacturing industry; What at first carry out grid application is joint study plan Information Power Grid (IPG) project of US National Aeronautics and Space Administration (NASA) and U.S.'s Natural Science Fund (NSF); Its objective is the management environment of setting up a complete distributed computational resource and data resource, ask with the distributed collaborative of supporting large-scale science and engineering problem; Internationally famous company such as FORD and BOEING also all attempts using grid to carry out design and emulation, the magnanimity computational problem of complex products such as automobile, aircraft; And for example jaguar automobile product research and development information centre combines GT3.0, adopts BPEL4WS to define the workflow of management engineering field demand as intermediate language, and has been applied in the jaguar Automobile Design manufacture process; Japan KazuoMuto has studied use XML system integration various device, through the XML system, makes to be distributed in not comprovincial user through Internet-browser, not only can see facility information, and can operate these equipment.
Domesticly also carried out correlative study in recent years, China " 863 " plans that research and the application work to grid has given to support energetically from fields such as computer, automations in Tenth Five-Year Plan Period.China national grid (CN-GRID) project objective is to build the high-performance computing environment with hundreds of millions polymerization computing capabilitys of 5-7, the grid software that exploitation one cover has independent intellectual property right.The e-Institute that Shanghai declaration in the end of the year 2002 drops into more than 200,000,000 yuan of construction is intended to integrate the resource of many colleges and universities such as Shanghai Communications University, Fudan University, East China science and engineering with grid, realizes resource-sharing, team teaching and scientific research." Vega grid " project that the Computer Department of the Chinese Academy of Science carries out; Its core concept is based on broadband and wireless network; The interior various parts of computer can both independently be surfed the Net; And develop specific product and technology such as server towards grid, router, operating system, agreement. country's 863 problems that Tsing-Hua University bears---resource grid (NetManGrid) system research is made in networking; The resource grid system platform is made in main research networking, and researches and develops the quick mesh services system that networking is made, and comprises the logistics grid, sells grid, client's grid, partner's grid, MGrid and planning grid etc.
Current research to MGrid is mainly around MGrid notion, architectural framework, application prototype platform etc. in certain industry, gone research in that abstract aspect is enterprising, and few to the combination research of resource service among the MGrid.And the combination of MGrid resource service realizes the key of MGrid, also is the effective way that realizes manufacturing enterprise's resource service value-added service, is to make problem demanding prompt solution in the grid at present.To above deficiency, the present invention proposes a kind of support semantic, towards the manufacturing gridding resource service combining method of Life cycle.
Summary of the invention
(1) purpose: the present invention relates to a kind of support semantic, towards manufacturing grid (MGrid) the resource service combined method of Life cycle; Be a kind of in novel resource service discovery, combination and the preferred implementation method made under the mesh model; It has overcome the deficiency of prior art; A rational MGrid resource service group frame and flow process have been made up; And realized the wherein foundation of most critical, making it becomes manufacturing mesh services combined platform good in the distributed manufacturing system, is implemented in collaborative the manufacturing and resource-sharing of complex product under the wide area network distributional environment.
(2) technical scheme: a kind of support semantic, towards manufacturing grid (MGrid) the resource service combined method of Life cycle; Be a kind of in novel resource service discovery, combination and the preferred implementation method made under the mesh model, these method concrete steps are following:
Step 1: task description and resource service step of polymerization; That is: at first adopt corresponding task description language; Form, content, function and the quality of service requirement of the manufacturing gridding task that the user is submitted to, task implementation strategy, implementation, task time started, task termination time, task execution time etc. carry out formalization and digitlization is described, and simultaneously the resource service demand is carried out description of overall importance to support subsequent operation related data and information (serving preferably, make up execution like resource service-task function coupling, flow process coupling, QoS extractions, QoS assessment, QoS comparison, combined resource monitors etc.).Then, through all kinds of resource service descriptor similarity matching algorithms, computational resource service similarity, the resource service polymerization that will have identity function in advance is to improve follow-up resource service matching efficient;
Step 2: the step of task decomposition and demand analysis thereof; That is: at first; Formulate task decomposer rule accordingly according to service quality or procedure relation; Through the task decomposer, manufacturing gridding task or the request that will pass through after the step format description are decomposed into a series of subtask or activities with flow process dependence.Then, extract the functional requirement of each subtask, form the flow process relation between each subtask through reasoning, thereby form abstract combined resource service procedure template.
Step 3: the step of functional requirement coupling; That is: through after the decomposition of step 2; Task function demand according to the generation of mission requirements resolver; Mate with the existing resource service in the MGrid resource service storehouse; Service in task and the service library is carried out overall matching (comprising title, description etc.), input and output coupling, service quality coupling, integrated coupling etc. under the support of (comprising literal similarity matching algorithm, sentence similarity matching algorithm, structure similarity matching algorithm, numerical value similarity matching algorithm) of all kinds of descriptor similarity matching algorithms; Meet the resource service collection corresponding to be selected of each subtask functional requirement thereby generate, and feed back to flow process demand adaptation.
Step 4: the step of flow process demand coupling, that is: the flow process dependence between the subtask after decomposing to task (like serial, parallel, circulation etc.) is set up corresponding procedural model, thereby is made up abstract service built-up pattern or template.Resource service collection to be selected to being produced according to step 3 functional requirement matching result further screens according to the flow process demand, eliminates part resource service to be selected, reduces follow-up resource service combination and preferred complexity, improves the resource service combination quality.
Step 5: the step of QoS integrated treatment, that is: to assessing through the total quality of resource service assembled scheme to be selected selected after function and the flow process coupling.At first utilize resource service QoS information extraction modules from corresponding resource service descriptor, to extract corresponding QoS information.Utilize QoS dynamic evaluation module that the information of obtaining is carried out comprehensive assessment then.Relatively solution to be selected is compared with QoS at last and sort, eliminate part QoS resource service of low quality, thereby simplify resource service combination and preferably difficulty and complexity with order module.
Step 6: resource service makes up preferred step, that is: the manufacturing grid subtask ST that obtains to each decomposition
i(i=1,2 ... N), establish after function match, Ni resource service to be selected that meets the STi functional requirement arranged, then have ∏ in theory at least
I=1 NN
iIndividual composite services scheme.Under multiple target, multi-constraint condition, utilize intelligent optimization algorithms such as genetic algorithm in the algorithms library, particle cluster algorithm, quantum intelligence body algorithm, from ∏
I=1 NN
iSelect the optimum task of going to carry out the user in the individual service assembled scheme to be selected.
Step 7: the step of engine is carried out in combination, that is: carry out engine through combination service binding is carried out in the selected selected composite services of assembled scheme, i.e. resource service instantiation begins to carry out then.Before in the implementation combination being carried out with carry out; The information such as withdraw from of the adding of new resources service, selected resource service is monitored in the system; And carry out the adjustment of assembled scheme, and then adjusted resource service assembled scheme is carried out resource service reselect according to monitor message
(3) advantage and effect:
1, the invention has the beneficial effects as follows and considered to support needs semantic, that make gridding resource service combination lifecycle process; The design phase (Des-phase), resource service combination deployment phase (Dep-phase) and the resource service combination that comprise the resource service combination are carried out and monitor stages (E&M-phase), possess systematicness and integrality.The functional characteristics of each stage and realization thereof is as shown in Figure 3.
2, the present invention has provided newly for making the flow process matching process of mesh services combination Life cycle on the basis of the coupling of service function in the past, service function coupling and flow process coupling is decomposed come, and makes the service combination have more controllability and autgmentability.
3, the present invention is incorporated into assessment and the intelligent optimization algorithm of resource service QoS in the preferred realization of service combination; Design resource service QoS integrated treatment functions of modules (comprising QoS extraction, QoS assessment, QoS comparison, QoS ordering etc.), guaranteed the global service quality of service combination and the optimum of assembled scheme.
Description of drawings
Fig. 1 supports the resource service of MGrid resource service combination Life cycle to find framework;
Fig. 2 is a MGrid resource service combination flow chart;
Fig. 3 is MGrid resource service combination lifecycle process figure.
Embodiment
Below in conjunction with accompanying drawing the present invention is described in further detail.
The present invention includes one and support resource service semantic, MGrid resource service combination Life cycle to find MGrid resource service combined method under framework and this framework, referring to Fig. 1.This flow and method has related to eight big functional modules such as resource service polymerization, task description, the parsing of task decomposition/demand, functional requirement coupling, flow process demand coupling, QoS integrated treatment, composite services are preferred, combined resource service execution engine.The main performing step of this method is following:
The first step is carried out description of overall importance and is carried out the resource service polymerization task or resource service demand that the user submits to, referring to Fig. 2.
At first, adopt resource service descriptive language and process description language and instrument to describe corresponding task or demand for services, the information of promptly user being submitted to such as task form, content, function, implementation strategy, process, beginning and concluding time thereof are described.On this basis, the extended resources service description language (sdl) is created rules such as QoS extraction, assessment, ordering, carries out description of overall importance, the extensibility of enhancement service description of overall importance to the resource service demand specially.
Then; Adopt all kinds of resource service descriptor similarity calculating methods (comprising literal similarity matching algorithm, sentence similarity matching algorithm, structure similarity matching algorithm, numerical value similarity matching algorithm); Computational resource service similarity; The resource service polymerization that will have identity function in advance is to improve follow-up resource service matching efficient;
Second step, will decompose according to service quality or procedure relation through the manufacturing gridding task after step formatization and digitlization are described and carry out demand analysis, see Fig. 2.
Development task decomposer at first, task decomposer realize that mainly the automation of task decomposes.In the decomposable process, require the subtask to have certain independence, and can try one's best and carried out by single resource service.Simultaneously, because in the task decomposable process, the decomposition granularity is excessive; The degree of coupling can increase thereupon between the subtask, is prone to cause the failure of follow-up function match, flow process coupling, service combination, and undersized; Then can cause the sharp increase of subsequent combination service optional program, and then increase the synthetic difficulty of preferred time and space complexity and execution result, therefore; Task is decomposed in the journey, by the correlation technique of fuzzy control theory, and the granularity that control task is decomposed in execution algorithm.
The resource service demand of then user being submitted to is carried out the semantization parsing, makes it in flow, can carry out service matching based on semanteme better.After having created task decomposer and demand parsing module, the task decomposition is carried out with demand analysis synchronously in the implementation.
In the 3rd step, the service matching process is divided into the functional requirement coupling carries out respectively, shown in Task-Layer among Fig. 2 with flow process demand coupling.
At first, on existing service function demand coupling correlation technique basis,, mate with existing resource service in the MGrid resource service storehouse according to task decomposer and resulting subtask of demand parsing module and demand.Use all kinds of resource service descriptor similarity matching algorithms (comprising literal similarity matching algorithm, sentence similarity matching algorithm, structure similarity matching algorithm, numerical value similarity matching algorithm) in the matching process; Mission requirements and resource service are carried out overall matching (comprising title, description etc.), input and output coupling, service quality coupling, integrated coupling etc.; Meet the resource service collection corresponding to be selected of each subtask functional requirement thereby generate, and feed back to flow process demand adaptation
Then, on the basis of functional requirement coupling, utilize task Hierarchical Network, semantic net, flow process to describe semanteme and instrument, visioning procedure demand adaptation.According to user's request, the flow process dependence that task is decomposed between the subtask, back is carried out reasoning, draw the flow process dependence of respectively decomposing between the subtask, set up corresponding procedural model.Based on the procedural model of being set up, to the resource service collection to be selected that generates by functional requirement coupling, it is screened once more based on the demand information of procedural model, form the abstract service combination, thereby improve the resource service combination quality.Wherein, The functional requirement coupling divides two steps to carry out with flow process demand coupling; Also used the similarity matching algorithm (comprising literal similarity matching algorithm, sentence similarity matching algorithm, structure similarity matching algorithm, numerical value similarity matching algorithm) of functional requirement coupling in the flow process demand adaptation; Draw the prerequisite of service matching and formula etc. as a result according to the flow process intelligent arranging with describing, thus resource service collection to be selected to be screened.
In the 4th step, the QoS integrated treatment is shown in QoS-Layer among Fig. 2.
The QoS integrated treatment is made up of three big modules.
At first be resource service QoS information extraction, also promptly extract comprehensive semantic QoS descriptor.
Be resource service QoS dynamic evaluation then, it has comprised single resource service QoS assessment and combined resource service aggregating QoS assessment.Here the QoS assessment of resource service being carried out; Owing to must consider the diversity of QoS dynamic and description; QoS dynamic evaluation system and assessment storehouse have been added in the framework; Promptly the assessment and invoked procedure in according to evaluating system real-time update QoS; And when considering the combined resource relationship between services, entity associated (relation in the composite services between two resource service suppliers with flow process relation) related with historical statistics (two resource service are bundled in the probability and the success rate of execution together in the composite services) is assessed.
Be that QoS relatively reaches the resource service ordering based on QoS at last.QoS demand between wherein main more resulting resource service to be selected and the mission requirements is eliminated part QoS resource service of low quality, thereby simplifies resource service combination and preferred difficulty and complexity.
In the 5th step, the resource service combination preferably reaches combination and carries out engine, referring to Composition-Layer. among Fig. 2
After function, flow services coupling and QoS comprehensive assessment, will make up and preferably to each resource service to be selected.The main complex inheritance algorithm of the present invention, particle cluster algorithm and quantum intelligence body algorithm are from ∏
I=1 NN
iSelect the superior in the individual service assembled scheme to be selected.In preferred assessment, multiplexing QoS quality synthesis evaluation system combines optimization algorithm to carry out follow-up assessment.
Then, based on the optimum combination service plan, start combination and carry out engine.Combination is carried out engine and is mainly comprised: (a) the preferred selected composite services of combined resource service are carried out service binding, i.e. resource service instantiation; (b) combination carried out before with carry out, the information such as withdraw from of the adding of new resources service, selected resource service is monitored in the system; (c) carry out the adjustment of assembled scheme according to monitor message; (d) adjusted resource service assembled scheme being carried out resource service reselects.
Claims (9)
1. MGrid resource service combined method based on semanteme towards Life cycle, the characteristic of this method is following:
Step 1) adopts makes the gridding task descriptive language; Description of overall importance is carried out in MGrid task or resource service request to the user submits to; Generate the task description document, supply step 2 to carry out task function and the parsing of flow process demand, for subsequent operation provides corresponding data and information support; Carry out the polymerization of MGrid resource service simultaneously, the service aggregating that will have an identity function supplies subsequent step 3 to carry out service matching together, the efficient when improving resource service-task coupling;
Step 2) said task description document is had certain independence according to the subtask and can try one's best and decompose, parse task function demand and flow process demand by the requirement of single resource service execution with by the granularity of correlation technique control task decomposition in algorithm of fuzzy control theory;
Function match is carried out in task function demand that step 3) parses according to step 2 and the service to be selected after step 1 polymerization, generates corresponding resource service collection to be selected;
The flow of task demand that step 4) parses based on step 2; The resource service to be selected that from step 3, generates concentrates further screening to meet the resource service of flow process demand; To the flow process demand between the subtask that forms after said the decomposition; Dependence; Set up corresponding procedural model, thereby generate resource service assembled scheme to be selected or template;
Step 5) is carried out service quality QoS integrated treatment and assessment to each resource service assembled scheme to be selected or model;
Step 6) according to integrated treatment of step 5 service quality QoS and assessment result, is selected optimum resource service assembled scheme under multiple target, multi-constraint condition, realize that the resource service combination is preferred;
Step 7) is carried out service binding, calls, monitors and is managed the preferred selected composite services of said resource service combination.
2. according to the said method of claim 1, it is characterized in that: the description of overall importance described in the step 1) comprises: formalization and digitlization to form, content, function and quality of service requirement, task implementation strategy, the implementation of task are carried out are described.
3. according to the said method of claim 1, it is characterized in that: said resource service polymerization is meant operation that the MGrid resource service polymerization with similar service function or interface descriptor is got up in the step 1).
4. according to the said method of claim 1, it is characterized in that: step 2) said decomposition needs the automation of task to decompose, and the subtask of being decomposed has certain independence, and it is moderate to decompose granularity.
5. according to the said method of claim 1, it is characterized in that: said in the step 3) classification that comprises descriptor is mated in the service to be selected after the polymerization, and the similarity matching algorithm between all kinds of descriptor.
6. according to the said method of claim 1; It is characterized in that: step 4) further comprises the resource service collection to be selected that the result produced that the service to be selected after the polymerization is mated; Further screen according to the flow process demand; Eliminate part resource service to be selected, reduce follow-up resource service combination and preferred complexity, improve the resource service combination quality.
7. according to the said method of claim 1, it is characterized in that: the said service quality QoS integrated treatment of step 5) comprises that resource service QoS information extraction, resource service QoS dynamic evaluation, QoS relatively reach the resource service ordering based on QoS.
8. according to the said method of claim 1; It is characterized in that: the described resource service combination of step 6) preferably is specially: carry out function match to decomposing each subtask that obtains; Obtain corresponding a plurality of composite services scheme, and from service assembled scheme to be selected, select the superior to carry out user's task requests.
9. according to the said method of claim 1, it is characterized in that: step 7) further comprises carries out service binding, i.e. resource service instantiation to the preferred selected composite services of resource service combination; To before the combination execution and in carrying out, the information that withdraws from of the adding of new resources service, selected resource service is monitored in the system; Carry out the adjustment of assembled scheme according to monitor message; Adjusted resource service assembled scheme is carried out resource service to be reselected.
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| CN109194746B (en) * | 2018-09-06 | 2021-03-26 | 贵州我联你联网络技术有限公司 | Heterogeneous information processing method based on Internet of things |
| CN109711810B (en) * | 2018-12-28 | 2024-03-29 | 航天信息股份有限公司 | Information grid resource collaborative service system taking event as center |
| CN111147555B (en) * | 2019-12-13 | 2020-08-25 | 苏州博纳讯动软件有限公司 | Heterogeneous resource mixed arrangement method |
| CN111597233B (en) * | 2020-04-03 | 2022-07-15 | 浙江工业大学 | Design mode recommendation method for resource-constrained environment |
| CN112528083B (en) * | 2020-12-10 | 2022-09-30 | 天津(滨海)人工智能军民融合创新中心 | Message customization method based on distributed semantic template distribution |
| CN113011873A (en) * | 2021-03-03 | 2021-06-22 | 浙江大学 | Trading method and system for one-stop asynchronous service of scientific and technological resources |
| CN113010296B (en) * | 2021-04-01 | 2022-07-26 | 上海交通大学 | Formalized model based task analysis and resource allocation method and system |
| CN113159548A (en) * | 2021-04-13 | 2021-07-23 | 同济大学 | Service matching method for network collaborative manufacturing |
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