A Novel User-Preference-Driven Service Selection Strategy in Cloud

A Novel User-Preference-Driven Service Selection Strategy in Cloud Computing *1 Yanbing Liu, 2 Mengyuan Li, 3 Qiong Wang *1,Corresponding Author,2 College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China, liuyb@cqupt.edu.cn, ruoyin0207@gmail.com, 3 Chongqing CYIT Communication Technologies Co., Ltd, Chongqing, China, wangqiong@cqupt.edu.cn Abstract Service selection is becoming one of the current research focuses due to the commercial characteristics of cloud computing. This paper addresses the service selection problem according to the user s demand in Cloud computing, and proposes a novel service selection strategy in the virtualization-based cloud computing environment. In the proposed strategy, service sets are first clustered by the attribute of service, and then the analysis between service sets and user s QoS requirements on data services is conducted so that a preferable service can be provided. Both theoretical analysis and experimental results show that the strategy presented in this paper can provide much desirable service to users in Cloud computing. Keywords: Cloud Computing, Service Selection, QoS Preferences, Analytic Hierarchy Process 1. Introduction Cloud computing based on Internet technology is a hot research topic in recent years. It is derived from distributed computing and grid computing, furthermore combines other key technologies such as virtualization, massive data processing. Its basic principle is collecting ultra-scale computing storage resources via the Internet to form a virtual pool of computing resources, and meet users demand by way of providing services [1]. It can significantly improve the utilization of computing resources, reduce the energy consumption of services, and effectively shield against errors of computing resources. Cloud computing employs a mode of on-demand delivery and provision for the application [2], it integrates with a large number of virtual resources by means of virtualization technologies, and provides terminal users with better service support and user experience. Web Services as the distributed module in cloud environment are widely used in Service-Oriented Architecture (SOA) and other applications. In the cloud model, resources are shared and reserved according to users requirements. When users requirements are approved, the cloud platform must ensure strict implementations of Service Level Agreement (SLA) [3] to satisfy users demand. However, the actual demand for resources is often dynamic. With the continuous expansion of applications, there has been a class of more rigorous constraints [4, 5] on QoS requirements. Therefore, viewing on the different needs of users for various virtual services, it is increasingly important to employ an efficient service selection method to find the preferable service. Cloud computing effectively collects a variety of interconnected computing resources, at the same time, processes these resources by means of multi-level virtualization and abstraction. Due to special business benefits of clouding computing, it has been focusing on the quality of service provided to users. However, the pros and cons of the service selection model affect users satisfaction directly, so there are many literatures discussing around this point. In [6], a decision is made through the stability of services provided by each Internet Service Provider (ISP) in cloud model. In traditional distributed computing and network computing, numerous service selection strategies have been proposed. Pan J and coauthors in [7] introduced an information weight factor representing a trust relationship established between service provider and presenter, and then returned a performance evaluation value of services after mathematical operations on the information weight factor. Y.C. Lee et al. [8] contributed on the profit driven service request scheduling for workflow. Li Y et al. proposed a service International Journal of Advancements in Computing Technology(IJACT) Volume4, Number21,November 2012 doi: 10.4156/ijact.vol4.issue21.48 414

selection method based on data credibility in [9], where QoS attributes are classified and computed according to data credibility. The new QoS attributes can more accurately reflect the real situation of the entity, thus a better quality of service composition is achieved. Fully considering different users' demand for various services and following the services running processes based on SOA, we propose a service selection mechanism which supports a user-oriented and visual operation to meet individual preference. The major features of our proposed mechanism are: a) The service selection mechanism has friendly man-machine interface. b) A fuzzy clustering on services adopted by this paper can effectively reduce the complexity of the algorithm, at the same time highlight the advantages of services. c) The similarity between service attributes and user preferences is introduced to determine the more appropriate services for user demand. Experimental results show that this model and presented algorithms are generic, effective, and are applicable to practical cloud computing as well. The rest of this paper is organized as follows. Section 2 introduces the related concepts; Section 3 describes service selection mechanism and functions of each module; Section 4 gives the specific analysis about service selection algorithm supporting user preferences; Section 5 presents the experiments and results analysis and Section 6 concludes the paper. 2. Preliminaries In cloud system, the service compositions after an abstract process are denoted as S, S { S1, S2,, S n }, where n represents the number of optional service compositions. The performance of each service can be measured by Sop, Sop { Sti, Sst,, Sse}, Sti, Sst,, Sse represent the timeliness performance, stability performance, security performance of a service respectively. Timeliness, stability, security is respectively measured by network bandwidth, the bit error rate, data integrity. Data integrity involves ensuring data from attack, AES encryption and so on. This paper will normalize S op in order to eliminate the incommensurability among attributes, facilitate the comparison and comprehensive calculation of properties, and take impact of each attribute on service into account. Definition 1: service comprehensive performance can be calculated as: 2 2 2 ( Sti ) ( Sst ) ( Sse ) Sop Where,, respectively represent the weight factors for timeliness, stability, security. Definition 2: Information Matrix R( m, n) which stores the quantified value can be expressed as: R R R 1,1 1,2 1, n R R R 2,1 2,2 2, n Rmn (, ) (2) R R R m,1 m,2 m, n R( m, n ) is matrix which stores the quantified value of users demand. Where m is the number of users, and n is the number of parameters for services. R i, j represents the demand of user i for parameter j of services. In this matrix, some elements will be zero, which means user has no special demand for some parameters. Similarly, R s, i represents matrix which stores the attribute parameters of each service. Definition 3: Services-Demand Similarity (SIM), describing a matching degree between services and user s demand can be expressed as: n d s i 1 Rdi, Rsi, SIM ( ds, ) cos( ds, ) (3) d s n 2 n 2 i 1Rdi, i 1Rsi, d ( d, d,, d n ) s ( s, s,, s n ) Where D, individual demand, can be denoted as a vector 1 2 in n-dimensional space, and S, service performances, can be denoted as a vector 1 2 in n-dimensional space. (1) 415

It s an optimization process to select the proper service among the service compositions based on user preference. To get the optimal solution, two conditions should be satisfied: a) Value of QoS property for services should be fully considered, that is to say, Q( s) is equivalent to the maximum of comprehensive performance value S op. b) User demand for services is often strongly subjective, so we should take a more personalized QoS requirements into consideration. Selecting the proper service composition needs to enumerate all possible candidates. What s more, there are many types of services in cloud environment, so it will take much time, and it is also difficult to ensure a demand assignment. 3. Framework of user-preference-based service selection In addition to problems mentioned above, it is also difficult for users to fully understand a wide range of services in the cloud environment, so a service selection mechanism is proposed. It makes decisions not only based on user preferences, but also considering application characteristics of various service resources in this article. The framework of this mechanism is showed in Figure 1. Figure 1. Framework of user-preference-based service selection Functions of each module are listed as follows: a) Man-machine Interface The user can submit the relevant demand parameters through the interactive interface, such as the bandwidth of the service system, the connection delay, bit error rate. The module takes charge of the user s demand and transfers to User Preference Analysis Module. It also receives service information from the cloud and displays them. b) User Preference Analysis Module This module receives user preference data transferred from Man-machine Interface, and then conducts qualitative and quantitative analysis on them. In other words, we can grasp user s demand rapidly and exactly by Analytic Hierarchy Process introduced here. c) Service Resource Cluster Module 416

This function module clusters on the standardized services, so that advantages of services can be fully exploited and actual search space can be reduced. Services could be classified into three categories according to timeliness, stability and security. d) Service Provider Module This module is an integrated architecture which performs distributed management, analysis and usage for massive data in distributed heterogeneous environment. It aims at achieving a safe, reliable and efficient operation for data migration and access. In addition, different services can be provided here. The implementation methods of User Preference Analysis Module and Service Resource Cluster Module are described in Section 4. 4. An user-preference-based service selection mechanism Currently, most service selection methods are proposed based on getting an optimum of QoS property values to offer a better selection scheme to users. However, neither is it the best criterion, nor does it always match user preferences. To attack these problems, a new proposal is addressed. In this article, we build a three-dimensional QoS model, which takes the Web services QoS mode in literature [10], integrates the characteristics of network virtualization in clouding environment, and takes the user preference with a strong subjective into consideration. After that, a method called Analytic Hierarchy Process [11] is adopted to conduct a qualitative and quantitative analysis. Specific three-hierarchical structure is shown as Figure 2. Figure 2. Three-hierarchical structure a) On the basis of in-depth analysis on practical problems, especially in view of users with different demands for different services, a three-hierarchical structure model for service selection is built with a top-down mechanism according to different properties. As shown in Figure 2, the top layer is decision layer which focuses on selecting an optimal service for user. Middle layer is the criterion layer, which has three parts, respectively called timeliness, stability and security. Bottom layer offers specific optional solutions. b) From the three-hierarchical structure, judgment matrix is constructed through the weight of each principle. Importance is described by relative weights a ij ; it is got by comparison of element between i-th and j-th. c) Single hierarchy ranking and its uniformity inspection. Normalize the column vector: n w ij aij / i 1 aij (, i j [1, n]) (4) Calculate the sum of each row: n w i j 1 w ij(, i j [1, n]) (5) Normalize: n T w w / w, w,, w (6) i i i 1 i ( w1, w2 n) 417

d) Hierarchy general ranking and uniformity inspection. If the hierarchical structure [11] has k levels (decision layer at the top), each vector of weights can be aggregated into a performance matrix ( k ) for each level of the hierarchy W, where k is the number of levels in the structure. The Service Selection based on User Preferences algorithm (SSUP) flow is as follows. Input: Available Service Set; Output: Optimum Service; Step 1: Build Information Matrix storing user requirements R di, ; Step 2: Normalize R di, with (4) (5) (6); Step 3: Get QoS weight with analytic hierarchy process; Step 4: Build Service Attribute Matrix R s, i ; Step 5: Normalize matrix R s, i ; Step 5: Construct Subordinate Matrix for S ( m, n) with the data standardized; Step 6: Set threshold, maximum count of iterations MaxTime and the count of service categories s; Step 7: Ensure clustering centre c 0 ; 0 Step 8: Compute the value of objective function Cv ; Step 9: Update clustering centre c i ; i Step 10: Update the value of objective function Cv ; Step 11: If i i 1 ( Cv Cv ) & & i MaxTime, output matrix, otherwise return to Step 7; Step 12: Determine the type of services requested by user; Step 13: Compute Service-Demand Similarity SIM ; Step 14: Fix the optimum service; Step 15: Finish. 5. Experimental results In order to analyze the efficiency and correlation property of SSUP proposed in this paper, the experiments are conducted using one type of datasets. This Web service QoS dataset is a real-world named WSDREAM [12]. The dataset contains 150 files, where each file includes 10,000 Web service invocations on 100 Web services by a service user. All the experiments are run on the same computer with Pentium 3.2GHz processor, 1.96GB of RAM, Windows XP SP3. Through selecting 20 users and analysis on status of Web services randomly, the distribution of services shows a similarity at a curtain time, in other words, service resources have a characteristic of clustering. Figure 3 shows a status of the various service resources when a user accessed Web service, where 1 in y axis indicates that the connection is in good condition, 2~4 are not normal. 4 Service Status of access 3 2 1 0 50 100 150 200 250 300 Access time /ms Figure 3. Status of access services 418

In the face of a kind of users with individual QoS sensitivity constraints in cloud computing, the most concern is how to offer many desirable services. Nevertheless, dynamic cloud environment and numerous services also need consideration. In this section, we compare our approach with the Analytic Hierarchy Process (AHP) introduced in [13] by conducting two experiments. These experimental data is helpful to draw an objective evaluation. The first experiment compares the execution time when the user accesses services. We conduct the other experiment to compare SSUP approach with AHP on the user satisfaction. In this paper, user satisfaction is obtained by success ratio which is a proportion of picking out high-level available service from service set according user s demand. The results of comparison are shown as Figure 4 and Figure 5. The first experiment indicates that the execution time consumed by SSUP is shorter than AHP, and the second shows that the approach has higher user satisfaction than the other. 10 AHP SSUP 8 Execution time /s 6 4 2 0 0 200 400 600 800 1000 Available services Figure 4. Execution time comparison between SSUP and AHP 35 30 AHP SSUP User satisfaction % 25 20 15 10 5 0 50 100 150 200 250 300 350 400 450 500 Available services Figure 5. User satisfaction comparison between SSUP and AHP These figures show the comparison performance between SSUP and AHP on execution time and user satisfaction with dataset. From Figure 4, with different number of service candidates, the execution time is always lower than that of AHP. As shown in Figure 5, user satisfaction of SSUP is higher for this dataset. It s well known that for a service selection approach, the higher its user satisfaction, the better its performance. Since SSUP can effectively filter out the redundant candidates according to user s demand, it needs less time to select the appropriate services while ensuring highlevel satisfaction for users. What is different from AHP, SSUP exposes services characteristics by focusing on clusters of services. Moreover, SSUP analyzes user preferences and selects the preferable service by means of QoS capabilities similarity. Based on the analysis above, we can conclude that SSUP can select reliable service in Cloud computing efficiently. 419

6. Conclusion This paper presents a strategy for dynamic cloud computing environment which includes a model and SSUP. In this strategy, services could be classified into three categories according to timeliness, stability and security metrics. Meanwhile, with user preferences taken into account, service for user s demand could be further subdivided into timeliness preference, stability preference or security preference. It not only offers a priority to gain a kind of high-capacity services among compositions, but also reflects a better demand assignment principle. Experimental results and theoretical analysis show that this strategy can ensure a higher service performance and user satisfaction. 7. Acknowledgement This work was partially supported by program for NCET and the following grants: National Science and Technology Major Project of China (No.2011ZX03002-004-03), National Science Foundation of China (No.60973160), 973 Project (No.2010CB334710), R&D Foundation of Chongqing (No.Kjzh10206), Open Project of Key Lab of Information Network Security of Administration of Public Security (No.C11609) and The Science and Technology Research Project of Chongqing Municipal Education Commission (No. KJ110529). 8. References [1] Chel-Rim Choi, Young-Jae Song, "Relative Weight Decision of Qualiy Attributes in Cloud Computing Service Using ANP", IJACT: International Journal of Advancements in Computing Technology, vol. 4, no. 5, pp.240-248, 2012. [2] Guiyi Wei, Athanasios V, Vasilakos, Yao Zheng and Naixue Xiong, "A Game-Theoretic Method of Fair Resource Allocation for Cloud Computing Services", The Journal of Supercomputing, vol. 54, no. 2, pp.252-269, 2010. [3] Reihaneh Khorsand Motlagh Esfahani, Farhad Mardukhi, Naser Nematbakhsh, "Reputation Improved Web Services Discovery Based on QoS", JCIT: Journal of Convergence Information Technology, vol. 5, no. 9, pp.206-214, 2010. [4] Chengwei Yang, Shijun Liu, Lei Wu, Chengle Yang, Xiangxu Meng, "The Application of Cloud Computing in Textile-order Service", JDCTA: International Journal of Digital Content Technology and its Applications, vol. 5, no. 8, pp.222-233, 2011. [5] Graham Cormode, Balachander Krishnamurthy, "Key Differences between Web 1.0 and Web 2.0", Journal of First Monday, vol. 13, no. 6, 2008. [6] Shangguang Wang, Zibin Zheng, Qibo Sun, Hua Zou and Fangchun Yang, Cloud Model for Service Selection, In Proceeding(s) of IEEE INFOCOM 2011 Worksshop on Cloud Computing, pp.666-671, 2011. [7] Pan Jing, Xu Feng and Lv Jian, Reputation-Based Recommender Discovery Approach for Service Selection", Journal of Software, vol. 21, no. 2, pp.388-400, 2010. [8] Young Choon Lee, Chen Wang, Albert Y. Zomaya and Bing Bing Zhou, "Profit-Driven Service Request Scheduling in Clouds, In Proceeding(s) of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp.15-24, 2010. [9] Li Yan, Zhou MingHui, Li RuiChao, Cao DongGang and Mei Hong, Service Selection Approach Considering the Trustworthiness of QoS Data, Journal of Software, vol. 19, no. 10, pp.2620-2627, 2008. [10] Yutu Liu, Anne H. Ngu and Liang Z. Zeng, QoS Computation and Policing in Dynamic Web Service Selection, In Proceeding(s) of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp.66-73, 2004. [11] Jeonghwan Jin, Ling Rothrock, Patricia L. McDermott and Michael Barnes, Using the Analytic Hierarchy Process to Examine Judgment Consistency in a Complex Multiattribute Task, Journal of IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 40, no. 5, pp.1105-1115, 2010. 420

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