Decentralized Architecture for Cloud Service ...

International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 11 No.4 (2016) © Research India Publications; http/www.ripublication.com/ijaer.htm

Decentralized Architecture for Cloud Service Discovery based-on XML Representation Samer Hasan

V. Valli Kumari

Research Scholar

Professor

Andhra University, Visakhapatnam, India [email protected]

Andhra University, Visakhapatnam, India [email protected]



ABSTRACT- Cloud computing is a paradigm for delivering ubiquitous, convenient, on-demand resources based on pay-as-you-go financial model. The Cloud Service Providers (CSPs) typically publish service descriptions, pricing policies and Service Level Agreement (SLA) rules on websites in various formats. With enormous growth of the cloud services and number of cloud providers discovering cloud services is a significant challenge and time-consuming task, especially when using general search mechanisms. Clients need to browse several websites online and across multiple cloud providers to select the appropriate cloud service. This paper presents new XML-Decentralized architecture to improve the effectiveness and efficiency of cloud service discovery. This architecture is based on Crawler Search Engine to collect the data from Cloud Service Providers. This data includes syntax and semantic information about cloud services. With new architecture Cloud Service Providers don't need to register service descriptions with every Cloud Service Discovery Systems (CSDSs) and only need to publish the service descriptions publicly. On the other hand, CSDS can directly access the service descriptions of Cloud Service Providers to index them. The cloud service discovery process is expected to be easier and faster by adopting the new architecture. KEYWORDS- Cloud Computing, Crawler, Service Discovery, XML.

I. INTRODUCTION th

Cloud computing is considered as the 5 utility after (water, electricity, telephony and gas) based on payas-you-go financial model. The Cloud computing architecture enables three abstract Service Models [1]. Firstly, Software as a Service (SaaS) provides access to complete applications as a service, such as Customer Relationship Management (CRM). Secondly, Platform as a Service (PaaS) provides a platform for developing other applications on top of it, such as the Google App Engine (GAE). Finally, Infrastructure as a Service (IaaS) provides an environment for deploying, running and managing virtual machines and storage. Currently, the capability of discovering cloud services online across multiple cloud providers and selecting the best suitable one are a significant challenge and time-consuming, especially when using general search mechanisms. Cloud providers typically publish service descriptions, pricing policies and Service Level Agreement (SLA) rules on their websites in various formats. Users need to browse several websites to select the appropriate service. Studies point out that around 85% of Internet users use search engines to find information from the World Wide Web (www) [2]. Search engines (such as Google, Yahoo, Bing etc...) generally not designed to provide small set of relevant and complete services that meet consumer's requirements. Using of general purpose search engines for searching for cloud services may result in imprecise and irrelevant search results. Recently, a number of cloud review websites and directories (such as CloudReviews and GetApp [20]) have appeared to provide a listing of available cloud services. Services information in these sites is usually collected from the cloud provider websites and presented via a single portal. Although the cloud review websites could be useful for

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selecting a provider but do not provide up-to-date service information. As a result, these websites may contain unreliable service information, for instance the service price, and the Quality of Service (QoS) at the time the consumers are making a service selection decision. In the past few years, a lot of research focus on proposing and developing intelligent methods for cloud services discovery and selection. These methods assume the availability of information by cloud service providers that match end-users search queries or requirements, and focus on developing methods to select the cloud services that best matches a user‘s requirements. This paper presents a new XML-Decentralized Cloud Service Discovery Architecture that uses Crawling Search Engine (CSE) to collect data from Could Service Providers and match it to the client requirements. Paper is organized as follows. Section 2 illustrates the top challenges for Cloud Service Discovery System. Section 3 demonstrates the previous works in this domain. Description of the proposed architecture is shown in section 4 which includes architecture, operations and XML repetition. I conclude the paper in Section 5.

II. CHALLENGES Firstly, cloud services are offered at different levels, not only providing data or business logic, but also infrastructure capabilities. Secondly, lack of standards for describing and publishing cloud services [3]. Unlike Web services which use standard languages such as the Web Services Description Language (WSDL) or Unified Service Description Language (USDL) to expose their interfaces and the Universal Description, Discovery and Integration (UDDI) to publish their services to service registries for discovery, the majority of the publicly available cloud services are not based on description standards [4] which makes the cloud service discovery a challenging problem. For example, some publicly available cloud services do not mention ―cloud‖ at all (such as dropbox[5]). On the other hand, some businesses that have nothing to do with Cloud computing (e.g., cloud9carwash [6]) may use cloud in their names or service descriptions. Thirdly, varied features of cloud services make Cloud service identification and discovery a hard problem [4]. Fourthly, Due to the increase in the number of cloud providers compared to many similar cloud services (such as storage services), decision making either along a single or multiple dimensions is a time-consuming process. Fifthly, Dynamic behavior is one of the unique characteristics of cloud services. In this sense new cloud services appear on the Web while old cloud services might discontinue around the clock. Sixth, interoperable issue when one Cloud provider goes out of business again a compliant Cloud provider according to the requirements must be chosen and it must be ensured that services are interoperable. Seventy, legal challenge is appeared because of geographical distribution of the cloud data centers that cross the country borders. Imagine a European organization that considers migrating customer relationship management (CRM) and its billing and accounting applications onto the Cloud. Because of regulatory guidelines, the Cloud provider must be located within the borders of the European Union. Cloud providers must be searched according to their geographical location.

III.

RELATED WORK

Service discovery is considered as one of the fundamental approaches in several research areas such as ubiquitous computing, mobile ad-hoc networks, peer-to-peer (P2P) systems, transportation domain, Web services, and service oriented computing [7][8][9][10]. Service discovery in these domains has attracted a lot of research attention in the past few years, resulting in several methods that improve service discovery [7]. For cloud services discovery and selection challenges need to be reconsidered. Solutions for effective cloud service discovery and selection are very limited [11]. Centralized architecture [12] based on a middleware service called broker is used for the service discovery. Service descriptions are stored at the central repository. The centralized architectures have a lack of scalability and single point of failure. To overcome the obstacles of the centralized architecture, Researchers proposed distributed architecture to store service descriptions. DHT (Distributed Hash Table) [13] is used to discover the cloud services in

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structured P2P networks. Others [14] propose the concept of cloud peer that extends Distributed Hash Tables (DHTs) overlay to support indexing and matching of multidimensional range queries (i.e., the dimensions can include service type, processor speed, available memory, etc.) for better discovery and load- balancing of cloud services. Database approach based-on [15] the kd-tree is proposed to establish a data space for all service descriptions. Kd-tree is decomposed and distributed to different nodes. A fourstages (election, evaluation, filtering, and recommendation), agent-based (User Agent, Provider Agent and Broker Agent) Cloud service discovery protocol is proposed in [16]. Utilizing an ontology description in which each resource is described semantically and relatively to other resources. Software agents are used for cloud service discovery in the agent based architecture [17]. A unified Cloud and business service ontology with querying capabilities is presented in [18]. The list of Cloud providers together with their offered Cloud services are parts of this ontology. All proposed architecture assuming that Cloud Service Providers (CSPs) should register themselves into the Discovery System. This assumption is not applicable in the real life. To overcome this problem, paper present decentralized architecture based-on XML representation. With new architecture CSPs need only to publish service descriptions file and make them accessible. Crawler Search Engine will collect service descriptions information independently. In this case, any Cloud Service Discovery System (CSDS) can use this service description to perform service discovery task. As it will be demonstrated in the following sections, the proposed system will make CSDS independent of Cloud Service Providers.

IV.

PROPOSED SYSTEM

1. Cloud Service Discovery System Architecture As shown in Fig. 1 the general architecture of proposed discovery architecture consists of two mean parts:

Figure 1. system architecture

A. Cloud Service Providers (CSPs) CSP is our target where the service of interest is available. CSPs should describe their services functional and non-functional attributes based-on XML representation. This representation should be accessible by the Crawler Search Engine (CSE). Cloud service providers need only to publish their service descriptions

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and no need to register themselves into the Cloud Service Discovery System (CSDS). In this model any CSDS can access the description file of CSPs and add it to the indexed repository. B. Cloud Service Discovery System (CSDS) It is the main part of the proposed architecture. CSDS is responsible for the CSP websites crawling to find the Service descriptions file (SDF). On the other hand, CSDS is responsible for receiving queries from the users through a Web Interface (WI) and matching them with CSPs services. Finally CSDS should display the CSP that best matched to users requirements. CSDS is consists of: i. Crawler Search Engine (CSE) A crawler is a program that visits Web sites and reads their pages and other information in order to create entries for a search engine index. Proposed CSE is optimized to visit CSPs websites and read the SDF to update the index. ii. Web Interface (WI) Users are able to access the system through WI to submit their quires and get the results which contain the best matched CSPs. Users also are able to make advanced search to select the functional and nonfunctional requirements of desired cloud service. 2. Cloud Service Discovery System Operations The figure below show the general steps that illustrate the work process in the discovery system: 1. Cloud service providers (CSPs) publish all service descriptions for functional and non-functional attribute into XML file and make this file accessible for Crawler Search Engine (CSE). 2. Crawler Search Engine (CSE) visit CSP websites and crawl the Service Descriptions File to update repository data based on XML representation. 3. Data is saved into repository. 4. User can access the system through Web Interface and submit quires about desired service. 5. Cloud Service Discovery System (CSDS) receives user quires and perform matching processes (Syntactic and Semantic). Finally, a set of best matched CSPs will be displayed to user.

Figure 2. system operations

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3. XML Representation: In the world of Web Services (WS) several XML standards and initiatives have been used to simplify the service discovery process. Standards including Universal Description Discovery and Integration (UDDI), Web Services Description Language (WSDL), Simple Object Access Protocol (SOAP). On the other hand, the majority of the publicly available cloud services are not based on description standards as explained in section 2. Proposed system introduces a new architecture where Cloud Service Providers (CSPs) should describe cloud services based-on XML representation. Syntactic and Semantic matching is used to map values of user requirements with the services present in the data repository. Syntactic match is done based on the service name and the tags present in the tag section. If matching not done then then semantic matching can be used by the Synonym concept of WorldNet [19]. As an example Synset isa set of synonyms that share a common meaning. Synset is used to describe the concept with more than one word like AWS and Amazon Web Services. In this case user can use any of two words to search for the same concept. Fig. 3 demonstrates Synset representation of Platform Service from two CSPs, which is referred as Microsoft platform service or Microsoft Azure and Gcloud provider refers it as platform service.

Figure 2. XML representation for cloud services

V. CONCLUSION Cloud computing is a commercial model for accessing to a shared pool of configurable computing resources based on pay-as-you-go financial model. Cloud computing is aiming to deliver computing resources for organizations and Individuals in low cost, fast and easy way. The exponential growth of cloud computing market and the huge increase of CSPs number are making cloud service discovery Hard and time-consuming task. The proposed architecture will not make the task easy only for the users but also for CSPs. Cloud Service Providers publish their service descriptions for one time and users can find the best matching service in easy and fast way. .

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[1] [2] [3] [4] [5] [6] [7]

[8]

[9] [10] [11]

[12] [13]

[14]

[15] [16]

[17]

[18]

[19]

[20]

REFERENCES SP 800-145, The NIST Definition of Cloud Computing (September 2011), is available at: http://dx.doi.org/10.6028/NIST.SP.800-145 Kobayashi, M., and Takeda, K., ‗Information retrieval on the web‘, ACM Computing Surveys (CSUR), 2000, 32, (2), pp. 144-173 Gracia, J., and Mena, E., ‗Semantic heterogeneity issues on the web‘, Internet Computing, IEEE, 2012, 16, (5), pp. 60-67 C.N. Höfer, G. Karagiannis, ―Cloud computing services: taxonomy and comparison,‖ Journal of Internet Service Applications, Issue 2, pp. 81–94, 2011. Dropbox website: https://www.dropbox.com/ cloud9carwash website: http://www.cloud9carwash.com/ Dong, H., Hussain, F.K., and Chang, E., ‗Ontology-learning-based focused crawling for online service advertising information discovery and classification‘:‗Service-Oriented Computing‘ (Springer, 2012), pp. 591-598 E. Meshkova, J. Riihij ̈arvi, M. Petrova, and P . M ̈ah ̈onen, ―A Survey on Resource Discovery Mechanisms, Peer-to-Peer and Service Discovery Frameworks,‖ Computer networks, vol. 52, no. 11, pp. 2097–2128, 2008. E. Al-Masri and Q. Mahmoud, ―Investigating Web Services on the World Wide Web,‖ in Proc. of the 17th Int. Conf. On World Wide Web (WWW‘08), Beijing, China, Apr 2008. A. Mian, R. Baldoni, and R. Beraldi, ―A Survey of Service Discovery Protocols in Mobile Ad Hoc Networks,‖ Pervasive Computing, IEEE, vol. 8, no. 1, pp. 66–74, 2009. J. Kang and K. M. Sim, ―Towards Agents and Ontology for Cloud Service Discovery,‖ in Proc. of the 2011 Int. Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery, Beijing, China, Oct 2011. M. Brock and A. Goscinski, ―Toward Ease of Discovery, Selection and Use of Clusters within a Cloud,‖ 2010 IEEE 3rd International Conference on Cloud Computing, pp. 289–296, Jul. 2010. G. E. Junwei, L. Chao, and F. Yiqiu, ―Research of semantic search algorithm based on cloud computing,‖ International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), pp. 1701–1704, 2012. R. Ranjan, L. Zhao, X. Wu, A. Liu, A. Quiroz, and M. Parashar, Cloud Computing: Principles, Systems and Applications. Springer, 2010, ch. Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing, pp. 195–217. Jing Zhou, Nor Aniza Abdulla and Zhongzi shi, ―A Hybrid P2P Approach to Service Discovery in the Cloud,‖ I. J. I. Technology and C. Science, no. February, pp. 1–9, 2011. J. Kang and K. M. Sim, ―Towards Agents and Ontology for Cloud Service Discovery,‖ in Proc. of the 2011 Int. Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery, Beijing, China, Oct 2011. S. Hamza, B. Aicha-Nabila, K. Okba, and A. Youssef, ―A Cloud computing approach based on mobile agents for Web services discovery,‖ Second International Conference on the Innovative Computing Technology (INTECH 2012), pp. 297–304, Sep. 2012. Tahamtan, A., Beheshti, S.A., Anjomshoaa, A., Tjoa, A.M., ―A Cloud repository and discovery framework based on a unified business and Cloud service ontology," IEEE Eighth World Congress On Services, pp. 203-210, 24-29 June 2012. Miller, G. A., and Fellbaum, C. ―Semantic networks of english‖. InLevin and, S. Pinker Eds. Lexical and Conceptual Semantics Blackwell, Cambridge and Oxford, England, 1992, pp. 197— 229 CloudReviews: http://www.cloudreviews.com/ GetApp: https://www.getapp.com/

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Authors Profile Samer Hasan Received M.tech (SE) from JNTU-Hyderabad in 2014. He is research scholar in the department of Computer Science & Systems Engineering, College of Engineering (A), Andhra University, Visakhapatnam, India. His broad research areas of interest are: Software Engineering Cloud Computing, Web Services, Semantic Web and Ontology. Professor V.Valli Kumari is a professor in the department of Computer Science & Systems Engineering, College of Engineering(A), Andhra university, Visakhapatnam, India. She has over 25 years of teaching experience. Her broad research areas of interest are: Data Engineering, Security and Web Technology.

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