CN102158533A - Distributed web service selection method based on QoS (Quality of Service) - Google Patents
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Abstract
The invention relates to the field of a computer and particularly discloses a distributed web service selection method based on QoS (Quality of Service). The web service selection is performed in a distributed way in the method and the method comprises the following steps: dividing the web service selection which is a big problem requiring extremely huge computing power into many small parts; distributing the small parts to many computers to be processed in parallel; and finally, combining all computing results to obtain a final result. In the invention, different machines perform high-efficiency parallel computing through a pre-prune-refine model, some web services which do not meet conditions are deleted in advance by the pre-prune course and the response time is shortened remarkably, thereby rapidly and flexibly finding the service which meets the requirements of users. Because the input and output in the middle of the course are temporary files, the additional input and output overhead is not generated, the transmission efficiency of a network can be obviously strengthened, and the selection efficiency is greatly enhanced.
Description
Technical field
The present invention relates to computer realm, related in particular to a kind of distribution Web method for service selection based on QoS.
Background technology
Distributed Calculation is a kind of new account form that proposes in recent years, the PROBLEM DECOMPOSITION how its research could solve a very huge computing capability of needs becomes many little parts, then these little parts are distributed to many computers and carry out parallel processing, each result of calculation is integrated obtain final result at last.Can be placed on program on the computer of optimum bootup window by Distributed Calculation and to move.
Web service selects to come to this a big problem that the very huge computing capability of needs could solve.It is exactly to select the more excellent service of quality to satisfy the demand of service requester from the suitable set of service of a large amount of functions that Web service is selected.Along with the development of service compute and cloud computing, the different service of a large amount of functional similarity not function attribute has appearred.Though Web service is registered in registration center storehouse (UDDI), in fact flatly be distributed on the different server in geographical position, and be connected to each other by Internet.The continual growth of Web service requires accurate, extendible, an efficient and reliable solution to seek and select only service.But when service during, this complexity very from different service provider, therefore how in a large amount of set of services of disperseing fast and flexible ground select the service of meeting consumers' demand, become web and served a important step in the evolution.The research of distribution Web services selection is suggested under this background just.
Along with the continuous development of the Internet, the actual storage of ISP and service becomes and more and more disperses.Service is stored on the different servers usually, and these servers might be in different geographical position.Current web services selection technology based on QoS supposes that mostly all services all are stored on the same platform, there is not to consider the distributed environment of extensive service, along with the increase of quantity of service and the distribution Web service environment of reality, the efficient of traditional services selection technology will reduce greatly, and because the very long experience that might influence the user of response time.The service of how selecting to satisfy different QoS requirements under the distributed environment of extensive set of service has become academia and the common problem of paying close attention to of industrial quarters.
A distributed framework more tallies with the actual situation, because Distributed Architecture allows to carry out better local service management, littler renewal cost, higher fault-tolerance.If but the web services selection technology that direct application has existed can produce very big expense, therefore take all factors into consideration the efficient and the distributed service environment of method for service selection, the Distributed Services choice mechanism of developing a kind of efficient parallel becomes the active demand of industry.
Summary of the invention
The efficient that the present invention is directed to services selection technology in the prior art is low excessively, response time is very long, influence the shortcoming of user experience effect, skyline technology in a kind of binding data storehouse is provided, propose distributed parallel web services selection mechanism, realized the distribution Web method for service selection based on QoS of web services selection efficiently.
In order to solve the problems of the technologies described above, the present invention is solved by following technical proposals:
Distribution Web method for service selection based on QoS comprises the steps:
Step a: master server is being managed all data servers, and allocating task, K map (mapping) task is arranged in this process, S pre-prune (pre-beta pruning) task and 1 refine (refining) task are assigned with (K＞0, S＞0), master server is given a map task, pre-prune task or refine Task Distribution in the machine of a free time;
Step b:Map process: the machine that has been assigned with the map task reads relevant web service list and is the input data, to import data parsing then and become the QoS vector, interim key/value is to (key/value to) in the middle of generating, and be buffered in the internal memory, the input of this process is the web service list, and output is the QoS vector;
Step c: the key/value that is buffered in the internal memory is regional to be divided into S by the subregion function, periodically be written on the local disk afterwards, the key/value of buffer memory is returned to master server to the memory location at local disk, is responsible for the key/value of buffer memory is sent to the pre-prune task again to the memory location on local disk by master server;
Steps d: after the pre-prune task receives the data storage location information that master server sends, the interim key/value in centre that reads buffer memory from the machine at map task place is right, after the machine at pre-prune task place had read all middle ephemeral datas, key (key) sorted outputed on the uniform machinery value (numerical value) with identical interim key in centre;
Step e:Pre-prune process: deletion in advance can not be the service of skyline service, on each machine, according to the skyline algorithm of selecting the QoS vector set of reading is operated, the service that deletion is arranged, obtain local skyline set of service, the middle interim local skyline set of service of generation is buffered in the internal memory;
Step f: the local skyline set of service of buffer memory is returned to master server in the memory location of this locality, by master server these local skyline set of services is sent to the refine task again in the memory location of this locality;
Step g: Refine process: receive the stored position information of the local skyline set of service that master server sends when the refine task after, read the interim local skyline set of service of buffer memory from the machine at pre-prune task place, after the machine at refine task place has read all local skyline set of services, by the local skyline service that the skyline algorithm deletion of selecting is arranged, obtain final overall skyline service;
Step h: after all map, pre-prune and refine task were all finished, master server woke user program up, at this moment, in user program calling just of pre-prune-refine is returned;
After completing successfully all tasks, the output of this model is divided into the M class, is stored in N output file, wherein M=N.
As preferably, the input and output of the Pre-prune task among the described step e are respectively: the interim key/value in the centre of reading to the skyline set of service of this locality.By the process of this pre-beta pruning, can significantly reduce the quantity of candidate skyline service, thereby reduce number of comparisons, therefore the last response time also can significantly reduce.
As preferably, described QoS vector definition: vectorial QVs=(q
1(s) ..., q
d(s)) be the QoS vector of web service S, wherein q
i(s) value by i the QoS attribute of serving the S issue determines.Adopt distributed mode to carry out the selection of web service, by this big PROBLEM DECOMPOSITION that needs very huge computing capability to solve of web services selection is become a lot of little parts, then that these are little part is distributed to many calculating and is carried out parallel processing, each result of calculation is merged to obtain final result at last.
System based on the distribution Web method for service selection of QoS comprises as the lower part:
Expansion service registration center: expanded traditional service register center, had the function of traditional services registration center, the service describing registration center for searching for registers the ISP that all have been issued, and the position of record web service cluster; Service requester just can be selected from its nearer ISP like this, thereby has reduced cost and response time;
Master server: be the core of web service cluster, be used to write down the position of each data server and the concrete service of each data server storage, provide and manage the directory information of whole system, and managing each data server;
Data server: be the actual cluster under the distributed environment, storing the relevant information of real service;
Skyline set of service storehouse: storing the skyline service that service requester requires of satisfying that each regional web service cluster chooses, only writing down the description of skyline service and the web service cluster at place.
The definition 1. service dominance relations based on the QoS vector: for service A and B, their QoS vector is respectively QV
A=(q
1(A) ..., q
dAnd QV (A))
B=(q
1(B) ..., q
d(B)).Service A domination service B, the value on the QoS vector one dimension in office of and if only if service A is all poor unlike service B, and better than B on a dimension at least, and its formula definition is
And
Definition 2.Skyline service: not by the service of other service dominations.
Services set { A with 17 services with 2 QoS attributes
1(1.1,4.7), A
2(1.8,4), A
3(2,4.7), A
4(1.3,4.2), A
5(1.6,4.34), B
1(3.1,2.1), B
2(3.5,1.5), B
3(3.8,1.8), B
4(3.22,1.6), B
5(3.6,2.2), B
6(4,4.6), C
1(6.1,2.2), C
2(6.3,1.8), C
3(6.8,2.4), C
4(6.5,2.5), C
5(6.9,1.6), C
6(7,5), C
7(7.5,1) } be example, by the Map task, we can be divided into three groups according to first QoS attribute with set of service: { A
1, A
2, A
3, A
4, A
5, { B
1, B
2, B
3, B
4, B
5, B
6, { C
1, C
2, C
3, C
4, C
5, C
6, C
7, each group is put into respectively on the machine and carries out the pre-prune task, after carrying out the pre-prune task, first group can obtain local skyline service { A
1, A
2, A
3, second group can obtain { B
1, B
2, B
3, the 3rd group can obtain { C
1, C
2, C
3, C
7, with the local skyline set { A that obtains
1, A
2, A
3, B
1, B
2, B
3, C
1, C
2, C
3, C
7Output on the machine of carrying out the refine task, after carrying out the refine task, we can obtain the skyline set { A of the overall situation
1, A
2, A
3, B
1, B
2, B
3, C
7.
The specific algorithm such as the table 1 of these three tasks:
Table 1 distribution Web method for service selection is described
Map：
Input：Key：the?name?of?server?Values：web?services?list
Output：Key：group?range?Values：QoS?vectors?of?Web?services
2 WS?is?distributed?to?each?node?by?DistributedCache
3 foreach?s∈WS?do
4 service＝extract?QoS?vector?from?s；
6 output_key＝an?exact?value?as?a?function?of?QoS；
output_value＝service；
7 EmitIntermediate(AsString(output_key)，output_value)；
8 end?for
Pre-prune：
Input?Key：the?intermediate?Key?produced?by?Map?function
Values：QoS?vectors?Vectors
Output?Key：an?identical?key?Values：candidate?skyline?services
3 SkylineComputeClass?skylineCompute＝new?SkylineComputeClass()；
4 foreach?v∈Vectors
5 services.add(s)；
6 end?for
7 candidateServices＝skylineCompute.computeSkyine(services)；
8 output_key＝new?IntWritable(1)；
9 output_value＝candidateServices；
10 Emit(output_key，output_value)
Refine：
Input?Key：the?only?one?key?produced?by?Combiner?function
Values：candidate?web?services
Output?Key：group?range?Values：global?skyline?services
2 globalServices＝computeSkyline(Values)；
3 for?each?global?service?gs?in?globalServices
4 output_key＝an?exact?value?as?a?function?of?QoS
5 EmitIntermediate(output_key，gs)；
6 end?for
The present invention has significant technique effect owing to adopted above technical scheme:
The present invention is by the pre-prune-refine model, on different machines, carry out parallel computation efficiently, owing to can delete some web that does not satisfy condition services in advance through the pre-prune process, thereby shorten the response time significantly, thereby can find the service of meeting consumers' demand fast and flexible.Because the middle input and output that produce all are temporary files, can not produce extra input and output expense, can significantly strengthen the transmission efficiency of network, thereby improved the efficient of selecting greatly.
Description of drawings
Fig. 1 is the architectural framework figure of distribution Web services selection of the present invention.
Fig. 2 is the specific algorithm flow chart of distribution Web services selection of the present invention.
Embodiment
Below in conjunction with accompanying drawing 1 to Fig. 2 and embodiment the present invention is described in further detail:
Embodiment 1
Based on the distribution Web method for service selection of QoS, to shown in Figure 2, comprise the steps: as Fig. 1
Step a: master server is being managed all data servers, and allocating task, K map (mapping) task is arranged in this process, S pre-prune (pre-beta pruning) task and 1 refine (refining) task are assigned with (K＞0, S＞0), master server is given a map task, pre-prune task or refine Task Distribution in the machine of a free time;
Step b:Map process: the machine that has been assigned with the map task reads relevant web service list and is the input data, to import data parsing then and become the QoS vector, interim key/value is to (key/value to) in the middle of generating, and be buffered in the internal memory, the input of this process is the web service list, and output is the QoS vector;
Step c: the key/value that is buffered in the internal memory is regional to be divided into S by the subregion function, periodically be written on the local disk afterwards, the key/value of buffer memory is returned to master server to the memory location at local disk, is responsible for the key/value of buffer memory is sent to the pre-prune task again to the memory location on local disk by master server;
Steps d: after the pre-prune task receives the data storage location information that master server sends, the interim key/value in centre that reads buffer memory from the machine at map task place is right, after the machine at pre-prune task place had read all middle ephemeral datas, key (key) sorted outputed on the uniform machinery value (numerical value) with identical interim key in centre;
Step e:Pre-prune process: deletion in advance can not be the service of skyline service, on each machine, according to the skyline algorithm of selecting the QoS vector set of reading is operated, the service that deletion is arranged, obtain local skyline set of service, the middle interim local skyline set of service of generation is buffered in the internal memory;
Step f: the local skyline set of service of buffer memory is returned to master server in the memory location of this locality, by master server these local skyline set of services is sent to the refine task again in the memory location of this locality;
Step g: Refine process: receive the stored position information of the local skyline set of service that master server sends when the refine task after, read the interim local skyline set of service of buffer memory from the machine at pre-prune task place, after the machine at refine task place has read all local skyline set of services, by the local skyline service that the skyline algorithm deletion of selecting is arranged, obtain final overall skyline service;
Step h: after all map, pre-prune and refine task were all finished, master server woke user program up, at this moment, in user program calling just of pre-prune-refine is returned;
After completing successfully all tasks, the output of this model is divided into the M class, is stored in N output file, wherein M=N.
The input and output of the Pre-prune task among the step e are respectively: the interim key/value in the centre of reading to the skyline set of service of this locality.
QoS vector definition: vectorial QVs=(q
1(s) ..., q
d(s)) be the QoS vector of web service S, wherein q
i(s) value by i the QoS attribute of serving the S issue determines.
Implement the system of above-mentioned distribution Web method for service selection based on QoS, comprise as the lower part:
Expansion service registration center: expanded traditional service register center, had the function of traditional services registration center, the service describing registration center for searching for registers the ISP that all have been issued, and the position of record web service cluster;
Master server: be the core of web service cluster, be used to write down the position of each data server and the concrete service of each data server storage, provide and manage the directory information of whole system, and managing each data server;
Data server: be the actual cluster under the distributed environment, storing the relevant information of real service;
Skyline set of service storehouse: storing the skyline service that service requester requires of satisfying that each regional web service cluster chooses, only writing down the description of skyline service and the web service cluster at place.
Detailed process based on the distribution Web services selection of QoS is as follows:
1. certain regional web ISP cluster is in the service of expansion service registration center issue self-management and storage, and the request of using self service is responded.Service register center is set up index to it, and is writing down service list and description that this cluster provides.
2. service requester sends the services selection request to expansion service registration center, and the corresponding constrained parameters restriction of some QoS attribute (promptly to) are set.
3. expansion service registration center is according to the request of service requester, at existing service, send the request of selecting to each regional web service cluster, each regional web service cluster utilizes the algorithm in the table 1 to carry out local skyline services selection, and the result is sent in the skyline set of service storehouse, skyline set of service storehouse merges the local skyline set of service in each zone, and delete those local skyline that arranged and serve, obtain final overall skyline set, be stored in the skyline set of service storehouse, select for service requester.
The present invention is by the pre-prune-refine model, on different machines, carry out parallel computation efficiently, owing to can delete some web that does not satisfy condition services in advance through the pre-prune process, thereby shorten the response time significantly, thereby can find the service of meeting consumers' demand fast and flexible.Because the middle input and output that produce all are temporary files, can not produce extra input and output expense, can significantly strengthen the transmission efficiency of network, thereby improved the efficient of selecting greatly.
In a word, the above only is preferred embodiment of the present invention, and all equalizations of being done according to the present patent application claim change and modify, and all should belong to the covering scope of patent of the present invention.
Claims (4)
1. based on the distribution Web method for service selection of QoS, it is characterized in that: comprise the steps:
Step a: master server is being managed all data servers, and allocating task, K map (mapping) task is arranged in this process, S pre-prune (pre-beta pruning) task and 1 refine (refining) task are assigned with (K＞0, S＞0), master server is given a map task, pre-prune task or refine Task Distribution in the machine of a free time;
Step b:Map process: the machine that has been assigned with the map task reads relevant web service list and is the input data, to import data parsing then and become the QoS vector, interim key/value is to (key/value to) in the middle of generating, and be buffered in the internal memory, the input of this process is the web service list, and output is the QoS vector;
Step c: the key/value that is buffered in the internal memory is regional to be divided into S by the subregion function, periodically be written on the local disk afterwards, the key/value of buffer memory is returned to master server to the memory location at local disk, is responsible for the key/value of buffer memory is sent to the pre-prune task again to the memory location on local disk by master server;
Steps d: after the pre-prune task receives the data storage location information that master server sends, the interim key/value in centre that reads buffer memory from the machine at map task place is right, after the machine at pre-prune task place had read all middle ephemeral datas, key (key) sorted outputed on the uniform machinery value (numerical value) with identical interim key in centre;
Step e:Pre-prune process: deletion in advance can not be the service of skyline service, on each machine, according to the skyline algorithm of selecting the QoS vector set of reading is operated, the service that deletion is arranged, obtain local skyline set of service, the middle interim local skyline set of service of generation is buffered in the internal memory;
Step f: the local skyline set of service of buffer memory is returned to master server in the memory location of this locality, by master server these local skyline set of services is sent to the refine task again in the memory location of this locality;
Step g: Refine process: receive the stored position information of the local skyline set of service that master server sends when the refine task after, read the interim local skyline set of service of buffer memory from the machine at pre-prune task place, after the machine at refine task place has read all local skyline set of services, by the local skyline service that the skyline algorithm deletion of selecting is arranged, obtain final overall skyline service;
Step h: after all map, pre-prune and refine task were all finished, master server woke user program up, at this moment, in user program calling just of pre-prune-refine is returned;
After completing successfully all tasks, the output of this model is divided into the M class, is stored in N output file, wherein M=N.
2. the distribution Web method for service selection based on QoS according to claim 1 is characterized in that: the input and output of the Pre-prune task among the described step e are respectively: the interim key/value in the centre of reading to the skyline set of service of this locality.
3. the distribution Web method for service selection based on QoS according to claim 1 is characterized in that: described QoS vector definition: vectorial QVs=(q
1(s) ..., q
d(s)) be the QoS vector of web service S, wherein q
i(s) value by i the QoS attribute of serving the S issue determines.
4. implement system, it is characterized in that: comprise as the lower part as the described distribution Web method for service selection based on QoS of claim 1-3:
Expansion service registration center: expanded traditional service register center, had the function of traditional services registration center, the service describing registration center for searching for registers the ISP that all have been issued, and the position of record web service cluster;
Master server: be the core of web service cluster, be used to write down the position of each data server and the concrete service of each data server storage, provide and manage the directory information of whole system, and managing each data server;
Data server: be the actual cluster under the distributed environment, storing the relevant information of real service;
Skyline set of service storehouse: storing the skyline service that service requester requires of satisfying that each regional web service cluster chooses, only writing down the description of skyline service and the web service cluster at place.
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| CN104735166A (en) * | 2015-04-13 | 2015-06-24 | 李金忠 | Skyline service selection method based on MapReduce and multi-target simulated annealing |
| CN105578212A (en) * | 2015-12-15 | 2016-05-11 | 南京邮电大学 | Point-to-point streaming media real-time monitoring method under big data stream computing platform |
| CN105578212B (en) * | 2015-12-15 | 2019-02-19 | 南京邮电大学 | A kind of point-to-point Streaming Media method of real-time in big data under stream calculation platform |
| CN106790536A (en) * | 2016-12-21 | 2017-05-31 | 国网江西省电力公司信息通信分公司 | Composite Web services system of selection based on affairs and QoS |
| CN106790536B (en) * | 2016-12-21 | 2020-06-05 | 国网江西省电力公司信息通信分公司 | Combined Web service selection method based on transaction and QoS |
| CN112787870A (en) * | 2021-02-25 | 2021-05-11 | 苏州大学 | Parallel flexible Skyline service discovery method with service quality perception |
| CN112787870B (en) * | 2021-02-25 | 2021-11-02 | 苏州大学 | Parallel flexible Skyline service discovery method with service quality perception |
| WO2022178917A1 (en) * | 2021-02-25 | 2022-09-01 | 苏州大学 | Parallel flexible skyline service discovery method based on quality-of-service perception |
| US11777820B2 (en) | 2021-02-25 | 2023-10-03 | Soochow University | QoS-aware parallel flexible Skyline service discovery method |
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