A Personalized Cloud Services Recommendation Based on Cooperative Relationship between Services

doi:10.4236/jsea.2013.612074

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Journal of Software Engineering and Applications, 2013, 6, 623-629

Published Online December 2013 (http://www.scirp.org/journal/jsea)

http://dx.doi.org/10.4236/jsea.2013.612074

Open Access JSEA

623

A Personalized Cloud Services Recommendation Based on

Cooperative Relationship between Services

Chengwen Zhang1, Jiali Bian1, Bo Cheng2, Lingfei Li3

1Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts & Telecommuni-

cations, Beijing, China; 2State Key Laboratory of Networking and Switching Technology, Beijing University of Posts & Telecom-

munications, Beijing, China; 3Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) of Ministry of Education,

Beijing University of Posts & Telecommunications, Beijing, China.

Email: paperbupt@126.com

Received October 26th, 2013; revised November 20th, 2013; accepted November 27th, 2013

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-

ABSTRACT

A personalized recommendation for cloud services, which is based on usage history and the cooperative relationship of

cloud services, is presented. According to service groups, a service group could be defined as several services that were

used together by one user at a time, and cooperative relationship between each two services can be calculated. In the

process of recommendation, the services which are highly related to the service that the user has selected would be ob-

tained firstly, the result should then take the QoS (Quality of Service) similarity between service’s QoS an d user’s pref-

erence into account, so the final result combining the cooperative relationship and similarity will meet the functional

needs of users and also meet the user’s personalized non-functional requirements. The simulation p roves that the algo-

rithm works effectively.

Keywords: Personalized Recommendation; Cloud Service; Quality of Service; Similarity; Co operative Relationship

1. Introduction

Recommendation system is an in telligent agent system to

solve the information overload problem on the Internet,

which recommends choices that meet users’ needs or

interests from a lot of information on the Internet to the

users automatically. Personalized recommendation sys-

tem is put forward as an indepe ndent concept in the 20th

century until 1990, the first presented recommendation

system is based on collaborative filtering recommenda-

tion [1], including content-based similarity [2] and user-

based similarity [3] recommended methods. And in re-

cent years label concept [4] has been presented to im-

prove for user’s single evaluation and the defects on the

description of users and objects. Recommendation algo-

rithm based on diffusion [5,6] and making use of graph

theory bipartite graph [7-10] has also been presen ted, the

physical diffusion theory is also used in the recommend-

dation system in order to improve accuracy and reduce

complexity.

With the process of cloud computing in resent years,

people begin to take their focus on the development of

cloud service selection and recommendation [11-16].

Cloud computing environments utilize the SOA archi-

tecture, including three aspects, they are service provid-

ers, cloud providers and service users. Service providers

no longer provide runtime environment for the release of

the service, but utilize the cloud computing environment

to run their specific services. Based on certain strategies,

service providers select the appropriate cloud provider

from the numerous cloud providers to run the specific

services. And service users in the cloud computing envi-

ronment select services which can meet their needs. The

advantage of this architecture is that service providers

and cloud providers run separately and have distinction

permission, making logic service and the service envi-

ronment separated, as well as bring about improving sca-

lability and flexib ility.

Cloud computing environment offers a wide variety of

services that can be divided into different levels, from

higher level to the lower level, they are Software applica-

A Personalized Cloud Services Recommendation

Based on Cooperative Relationship between Services

624

tion level services, platforms or environmental level ser-

vices and infrastructure level services. Actually, the users

usually choose a number of services spontaneously to

form a combination to meet their own needs instead of

using fixed combinations or a single service that cannot

meet the needs of users. For example, a user wants to edit

text online, in addition to using text editing functions

(This feature can be seen as a software application level

functions), it also needs to be uploaded to the Internet

and can be downloaded in different places, it can be seen

as a memory function in basic level of service. Cloud ser-

vices are complementary to each other in functional as-

pects to achieve different goals. These services may in-

clude a variety of service units, such as hardware, soft-

ware, platform, storage, computing, data and other ser-

vices. According to different requirements and rules,

services are combined to realize the complex functions.

According to the circumstances described above, a

recommendation algorithm which is based on usage his-

tory and the combined relationship of cloud services is

presented. Firstly, system will extract the users’ usage of

the group from services, then according to the group,

calculate the cooperative relationship among services.

While recommending, according to the service that user

has chosen, system selects services which are highly re-

lated to the service that the user has chosen, and gives

final result which combines the cooperative relationship

and similarity between QoS and user’s interests.

The remaining chapters are organized as follows: Sec-

tion 2 describes some related works; Section 3 discusses

in detail including model design and algorithm steps de-

sign; Section 4 presents the simulation work and results;

Section 5 concludes the work and notes further research

content.

2. Related Works

The most mature algorithm is the collaborative filtering

recommendation algorithm [1], which includes user-bas-

ed and content-based method. [2,3] discussed the two

collaborative filtering algorithms in detail. With the de-

velopment of the research on recommendation system,

recommendation algorithm is no longer limited to col-

laborative filtering methods, but also make use of the

network topology, this type of algorithms treats users and

the projects as nodes, [5,6] discussed a network-based

diffusion recommendation algorithm, according to the

projects selected by user, it takes advantage of diffusion

theory to find users wh o has si milar interests to the targ et

user and makes recommendation.

[8,9] presented an algorithm by making use of two

sub-networks (also known as two parts diagram), ac-

cording to the user’s choice of products and creating the

relationships through a diffusion algorithm, algorithm

finds out users’ potential demand or interest and makes

recommendation. Algorithm presented in [9] weights

edges in the network in order to improve the precision in

recommending.

[7] presented a recommendation and clustering algo-

rithm that works by creating a network topology based

services relationship. Services are divided into two parts:

services with large and small granularity, called SOS and

S. SOS is usually composed by a variety of services S

and some programming supplemented, the relationships

of composition can be shown by a SOS-S network. Ac-

cording to this netwo rk structure, the diagrams that show

the similarity relationship between large-grained services

and the relationship between small-grained services can

be calculated. Recommendation makes use of the com-

bination relationship of small-grained services, and rec-

ommends the services which are closely related to ac-

cording to the selected service.

3. Recommendation Models and Methods

Users and services are treated as nodes in this recom-

mendation system, according to users’ usage record, ser-

vices will be combined to form a service gro up. Based on

the group’s composition and relationships, the coopera-

tive relationship between services could be calculated,

and system makes recommendatio n with the relationship.

Compared to [7], service group is calculated and formed

according to the usage of services, it is a logical concept

to help analysis the relationship between serv ices, but not

a real entity existing in cloud. In the cloud environment,

services that belong to different levels are often used

together and form a service group, that brings about the

situation that some lower-level services may become the

fundamental member of service group, namely, this kind

of service such as storage or platform is very necessary

for most service group and can achieve a great many

user’s requires, and a higher-level service would be a

professional characteristics of the group, this kind of ser-

vice is just used in parts of service groups and is neces-

sary for particular users in some professional fields.

There are two kinds of relationships between services,

which can be different for the user’s current selection of

services, and there are also large differences between

recommending a base service when using a professional

service and recommending a professional service when

using a base service. So a two-way standard is introduced

to distinguish the relationship of A to B and B to A when

calculating the co operativ e relationship between services.

On the other hand, user’s personalized interest should be

taken into account while making recommendation.

3.1. Node Model

Users and services are treated as nodes in the system.

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A Personalized Cloud Services Recommendation

Based on Cooperative Relationship between Services 625

User often needs a set of services instead of a single one

to complete a task within a certain period of time. In

Figure 1, User 1

U selected services A, B and C to

achieve his task, user 2

U selected services C, D and

selected services C, E.

In users’ history records, one user has chosen numbers

of sets of service, so service group node was introduced

to distinguish these sets and make it easy to analysis. In

Figure 2, a service group includes some services, so the

situation above could be described just like the structure

in Figure 2, user 1

U used service group and

, and user used group .

2 23

User Model: User is defined as a set

which describes all users in the

system. The user has a set i, defined as

SC USC



123

,,,,,

UUUUU



,, ,SC ,DSUSC SC

ii mn

, which describes all service

groups that

had used. There is a vector QoS

,,,abcd for each user to describe the user’s prefer-

ence for stability, security, network quality and charge

mode.

Service Group Model: Service group is defined as

Figure 1. Structure of users and services.

Figure 2. Model of service group.

a set





123

,,,,,

SC SCSCSCSC, which describes all

service groups in the system.

Service Model: Service is defined as a set





123

,,,,,

SSSSS, which describes all services in

the system. The service group , has a set i, which

is defined as i

SC D

,,,

iijk

DSCSS, describes the mem-

ber services that make up the group i.ki



is defined

as the relationship of i and k, it equals 1 when

service i is one of members of service group k,

there will have an edge connected between the i-th ser-

vice node and k-th service group node. k is defined to

record the number of members of service group k.ij

SSC

NSC



is defined to tell whether the i-th and j-th service are in

the same service group or not. The relationship between

services can be calculated only when they are in the same

service group.

ki ik











(1)







(2)

1, ,1

0, ,0

ki kj

ij ki kj





















 (3)

Cooperative Relationship between Services: Two

services in one service group have two edges to connect.

One is from i to j, another is from j to i. For the edge

, there is a weight assigned to it, defined as SSij



means the cooperative relation ship from to

S. ij



is defined as follow:

ki kj

ij ki











 (4)

In the formula,





ki kjk



 means the proportion

for service

S cooperates with i in all services that

cooperate with i in the k-th group.

Sij



shows the

probability of service

S cooperates with .







(5)

It is obviously that the relationship from i to S

Sand the relationship from

S to i are different.

Since i and S

S may belong to different service grade

or level, such as base services and professional services.

When user selects a professional service, the recommen-

dation system may give a base service with high proba-

bly, because the relationship from the professional ser-

vice to the base service should be higher. Contrarily,

when user selects a base service, the system might be

confused to decide which professional service should be

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Based on Cooperative Relationship between Services

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given because it does know user’s profession, and the

relationship from the base service to a professional ser-

vice should be lower.

Similarity Between Users and Services: Vector rep-

resentation is used to describe the QoS of services and

users’ preference, given two vector x and y, using cos(x,y)

multiplied by mode of y to rep resent the similar y from y

to x.

 

112

Sim ,cos ,

xyxy yy

xy x















(6)

Result of Recommendation: The final result should

combine the cooperative relationship with QoS similar.

So the result is defined as

 

SimSim .

kijijkj ijkj

Rf gab





where a = 20 and b = 0.004, a and b are set to standard-

ize relationship and similarity.

3.2. Recommendation Method

The recommendation process is divided into four sec-

tions as followed: Section 1, data processing and creating

set S and U; Section 2, getting users’ preference; Sec-

tion 3, obtaining users’ usage and relationship of services;

Section 4, service recommendation. Detail discussing

follows:

1) According to records, the set of users U and the set

of services S will be counted, and then the set of service

group SC will be added up.

2) According to the evaluation of the users to the ser-

vices, the vector of users’ preference and Qos of services

will be got.

3) The relationship between service groups and ser-

vices and relationship between services will be calcu-

lated.

4) Recommendation. The input is user k

U and ser-

vice i that the user has chosen. This process is divided

into two steps; the first step is to calculate the top k ser-

vices to which service i has higher cooperative rela-

tion, to meet the user’s functional needs. This step must

be done first, that means the service should match user’s

functional needs first. And then the second step is to cal-

culate the QoS similarity relation between these k ser-

vices and the user to meet user’s non-functional needs.

When the user has already selected more than one service,

system should combine the result sets from each service

according to the two steps above and give the final re-

sults.

4. Simulation and Analysis

4.1. Data Creating

Experimental data are created by a semi-random method,

firstly, the services and their QoS are produced randomly;

and users are produced as the same way. A vector is

given to each user to show user’s preference, which will

be only used in the next step to produce service groups.

The rule in the step of producing group sets is that, A

user selects several services from the service sets, and

system will calculate the similarity of service QoS and

user’s preference, then the top 2 - 4 services will be taken

as a service group, the number of members of a group is

got randomly fro m 2 to 4, and each user has a number of

groups. Actually, when users select the services in actual

life, there will be a number of services provided for the

user, and user will select those which could fit user’s

preference, this step of producing service group above

has just simulated the process of users’ selection of ser-

vices. The vector just given is only used in the step to

produ ce gro ups , an d th e v e c tor of us er s’ pr ef e r en c e in the

recommendation process will be recalculated in recom-

mendation steps later.

In the data producing process, the QoS of services that

the user has chosen is similar to the user’s preference, so

the service which has poor QoS would be eliminated.

Meanwhile, services which have similar QoS will be

selected by users who have similar preference, in other

words, users who have similar preference will choose

same services, these services will be combined as a

group for many times, and the relationship of these ser-

vices will be relatively high. The argument above is just

to proof that, in the data created, the relationships be-

tween services are not completely random, there are

some services within which, every one of them has rela-

tively high relationship to each other. So, the recom-

mendation based on the date is m eaningf ul.

4.2. Results and Analysis

The date have simulated a small cloud environment, data

is created as follow: there are 100 users and every user

has 100 service group records, each group has 2 to 4

members. In recommendation, the system will recom-

mend to one user whom we call is the target user. To

judge the recommendation is effective or not, the user’s

record will be divided into two parts, one is the experi-

mental group, the other part is the comparison group. The

experimental group as the usage history record to get the

target user’s preference. In each service group in the

comparison parts, one or two services will be selected as

the input, output is just the recommendation result, and it

will be juxtaposed with the other services in this group. If

one of the other services in the group is also a member of

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A Personalized Cloud Services Recommendation

Based on Cooperative Relationship between Services 627

result, this case of recommendation will be treated as

valid.

The simulation will be designed and analyzed on the

bases of the number of total services, the proportion of

the experimental group and the number of services that

user has already selected. The result set will have five

services, the simulation will carry out 100 users to make

recommendation respectively, and system makes account

of the average and max rate of the valid recommendation.

The number of total services is set to 20, 30, 40, 50, the

proportion of the experimental group is set to 20%, 40%,

60%, 80%, the number of services that user has already

selected is set to 1, 2.

If the total number of services is set to 50; there will

be 2500 relationships between services, and the max

number of service groups in theory will reach

, about 250,000. There are 10,000 groups

in the data, these groups are small parts of the number in

theory, but it covers all the possibilities of all relation-

ships. If the number of total services is set 40, 30 and 20,

the max number of services will reach about 100,000,

30,000 and 6000.

50 50 50

CCC

Figure 3 shows the account of the average and max

rate of the valid recommendation when the number of

services that user has already selected is set to 1 and in

Figure 4, the number is set to 2.

The line marked MAX represents the max rate of valid

recommendation, and the one marked AVERAGE repre-

sents the average rate. Result shows that, the rate de-

creases with the increase of the number of total services,

one reason is that the lager the number of total services is,

the fewer chances that the groups in system can express

the relationships between services, another reason is that

the lager the number of total services is, the lower the

percentage proportion the result set shares of all services

and the lower proportion that the results can cover the

selection of the target user.

Figure 3. Results of rate when one service is selected.

Figure 4. Results of rate when two services are selected.

In Figure 5, the number of services that user has al-

ready selected is set to 1, and the line above represents

the average rate of valid recommendation when, the line

below represents the rate in theory when the recommend

to the target user randomly and with on recommend rules.

When there are 20 services in system, the result set has 5

services, 25% of total services, but without recommend

rules, system selects 5 services randomly, the proportion

that the results can cover the target user’s selection is

about 43%, if the recommendation algorithm is used in

the system, the coverage will reach 80%. It is believed

that the recommendation algorithm is effective according

to the two lines in Figure 5.

On the other hands, in the Figure 6, the #2 line repre-

sents the average rate of valid recommendation when the

number of services that user has already selected is set to

2, and #1 line shows the rate when the number is 1. The

more the services that user has already selected, the

higher the rate is. The reason is that, when user has se-

lected 2 services, the services that will be recommended

must have high relationship with both of the two services,

the range of selection will be decreased and the result

will be more accurate.

To analysis the affection of user’s preference to the

result, the proportion of the experimental group is set to

20%, 40%, 60%, 80%, the number of total ser vices is set

to 50. In Figure 7, the lines from #1 to #4 represent the

max and average rate of valid recommendation when the

number of services that user has already selected is set to

2 and the max and average rate when it is set to 1. The

larger proportion of the experimental group is, the more

accurate result is. It is believed that considering users’

preference and making the similarity of services’ QoS

and users’ preference as one of bases of recommendation

has just improved the accuracy of the recommendation

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A Personalized Cloud Services Recommendation

Based on Cooperative Relationship between Services

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Figure 5. Recommendation and randomly select ion re sults.

Figure 6. Average rate when one and two services are se-

lected.

Figure 7. Results in different proportion of experimental

group.

according to the #1 and #3 lines which represent the max

rate. When the proportion is relatively small, the result is

not too low. It is friendly to new users who do not have

too many history records and system cannot get too much

information about their preference.

5. Conclusions

Above all, a personalized recommendation for cloud ser-

vices, which is based on usage history and the coopera-

tive relationship of cloud services, is presented. Recom-

mendation works according to the cooperative relation-

ship among services to find out the functional comple-

mentarity among them, and to recommend the results.

Performance in the simulation proves that the recom-

mendation works well in a small-scale cloud environ-

ment with not so many services. One advantage of this

recommendation is that, new users can get relatively ef-

fective recommendation without so much information

about preference.

It remains future work to consider classification of us-

ers and services when the cloud environment scale is

larger. Classification should be considered from the point

of view to distinguish users’ professionals and services’

functions.

6. Acknowlegements

The work presented in this paper was supported by Na-

tional Natural Science Foundation of China (Grant No.

61001118); Program for New Century Excellent Talents

in University (Grant No. NCET-11-0592).

REFERENCES

[1] I. Cantador, M. Fe rnáandez and P. Castells, “A Collabo-

rative Recommendation Framework for Ontology Evalua-

tion and Reuse,” Universidad Autóonoma de Madrid,

Spain, 2006.

[2] G. Adomavicius and A. Tuzhilin, “Toward the Next Gen-

eration of Recommender Systems: A Survey of the State-

of-the-Art and Possible Extensions,” IEEE Transactions

on Knowledge and Data Engineering, Vol. 17, No. 6, pp.

734-749.

[3] J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen,

“Collaborative Filtering Recommender Systems,” Adap-

tive Web, 2007.

[4] Knowledge & Data Engineering Group (KDE), “Informa-

tion Systems and Machine Learning Lab (ISMLL),” Tag

Recommendations in Folksonomies, 2006.

[5] J. G. Liu, T. Zhou, Y. C. Zhang and Q. Guo, “Degree

Correlation of Bipartite Network on Personalized Rec-

ommendation,” International Journal of Modern Physics

C, Vol. 21, No. 1, 2010, pp. 137-147.

http://dx.doi.org/10.1142/S0129183110014999

[6] C.-X. Jia, R.-R. Liu, D. Sun and B.-H. Wang, “A New

Weighting Method in Network-Based Recommendation,”

Physics A, Vol. 387, 2008, pp. 5887-5891.

[7] W. F. Pan, B. Li, B. Shao and P. He, “Service Classifica-

Open Access JSEA

A Personalized Cloud Services Recommendation

Based on Cooperative Relationship between Services

Open Access JSEA

629

tion and Recommendation Based on Software Networks,”

Chinese Journal of Computers, Vol. 34, No. 12, 2011, pp.

2355-2369.

[8] T. Zhou, J. Ren, M. Medo and Y.-C. Zhang, “Bipartite

Network Projection and Personal Recommendation,”

Physical Review E, Vol. 76, No. 4, 2007, Article ID:

046115.

[9] X. Pan, G. S. Deng and J.-G. Liu, “Weighted Bipartite

Network and Personalized Recommendation,” Physics

Procedia, Vol. 3, No. 5, 2010, pp. 1867-1876.

http://dx.doi.org/10.1016/j.phpro.2010.07.031

[10] Y. R. Gu and M. Chen, “One Tag Time-weighted Rec-

ommend Approach on Tripartite Graphs Networks,”

Computer Science, Vol. 39, No. 8, 2012, pp. 96-98.

[11] S. Kang, S. Kang and S. Hur, “A Design of the Concep-

tual Architecture for a Multitenant SaaS Application Plat-

form,” Computers, Networks, Systems and Industrial En-

gineering (CNSI), 2011 First ACIS/JNU International

Conference on Digital 2011, pp. 462-467.

[12] K. S. Gopalan and S. Nathan, “A Cloud Based Service

Architecture for Personalized Media Recommendations,”

International Conference on Next Generation Mobile Ap-

plications, Services, and Technologies, 2011, pp. 19-24.

[13] P. Bedi, H. Kaur and B. Gupta, “Trustworthy, Service

Provider Selection in Cloud Computing Environment,”

International Conference on Communication Systems and

Network Technologies, 2012, pp. 714-718.

[14] S. X. Yan, C. Q. Chen, G. P. Zhao and B. S. Lee, “Cloud

Service Recommendation and Selection for Enterprises,”

6th International DMTF workshop on systems and Virtu-

alization Management (SVM2012)/CNSM, 2012, pp.

431-433.

[15] S. Bardhan and D. Milojicic, “A Mechanism to Measure

Quality-of-Service in a Federated Cloud Environment,”

Federated Clouds’12: Proceedings of the 2012 Workshop

on Cloud Services, 2012, pp. 19-24.

[16] W. Y. Zeng, Y. L. Zhao and J. W. Zeng, “Cloud Service

and Service Selection Algorithm Research,” GEC’09:

Proceedings of the first ACM/SIGEVO Summit on Ge-

netic and Evolutionary Computation, ACM, 2009, pp.

1045-4048.