Investigating Techniques for Automating the Selection of Cloud Infrastructure Services’ MIRANDA ZHANG 1, 2 , RAJIV RANJAN 1 , DIMITRIOS GEORGAKOPOULOS 1 , PETER STRAZDINS 2 , SAMEE U. KHAN 3 , ARMIN HALLER 1 1 Information Engineering Laboratory, CSIRO ICT Centre, Australia 2 Research School of Computer Science, ANU, Australia 3 Electrical & Computer Engineering Dept, North Dakota State University, USA ________________________________________________________________________ The Cloud infrastructure services landscape advances steadily leaving users in the agony of choice. As a result, Cloud service identification and discovery remains a hard problem due to different service descriptions, non- standardised naming conventions and heterogeneous types and features of Cloud services. In this paper, analysis the research challenges and present a Web Ontology Language (OWL) based ontology, the Cloud Computing Ontology (CoCoOn). It defines functional and non-functional concepts, attributes and relations of infrastructure services. We also present a system, CloudRecommender, that implements our domain ontology in a relational model. The system uses regular expressions and Structured Query Language (SQL) for matching user requests to service descriptions. We briefly describe the architecture of the CloudRecommender system, and demonstrate its effectiveness and scalability through a service configuration selection experiment based on a set of prominent Cloud providers’ descriptions including Amazon, Azure, and GoGrid. Keywords: Cloud Computing, Semantic Web, Recommender System, Service Computing, Operation Research ________________________________________________________________________ 1. INTRODUCTION 1.1 Overview The Cloud computing [Armbrust et al. 2010, Wang et al. 2010] paradigm is shifting computing from in-house managed hardware and software resources to virtualized Cloud- hosted services. Cloud computing assembles large networks of virtualized services: hardware resources (compute, storage, and network) and software resources (e.g., web server, databases, message queuing systems and monitoring systems.). Hardware and software resources form the basis for delivering Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). The top layer focuses on application services (SaaS) by making use of services provided by the lower layers. In this paper, we focus on IaaS that is the underpinning layer on which the PaaS/SaaS services are hosted. Cloud computing embraces an elastic paradigm where applications establish on- demand interactions with services to satisfy required Quality of Service (QoS) such as response time, throughput, availability and reliability. QoS targets are encoded in Legal Service Level Agreement (SLA) documents, which state the nature and scope of the QoS parameters. However, selecting and composing the right services meeting application requirements is a challenging problem. From a service discovery point of view, the selection process on the IaaS layer is based on a finite set of functional (e.g. CPU type,
18
Embed
Investigating Techniques for Automating the Selection of ...
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Investigating Techniques for Automating the Selection of Cloud Infrastructure Services’
MIRANDA ZHANG1, 2, RAJIV RANJAN1, DIMITRIOS GEORGAKOPOULOS1, PETER STRAZDINS2, SAMEE U. KHAN3, ARMIN HALLER1 1 Information Engineering Laboratory, CSIRO ICT Centre, Australia 2 Research School of Computer Science, ANU, Australia 3 Electrical & Computer Engineering Dept, North Dakota State University, USA ________________________________________________________________________
The Cloud infrastructure services landscape advances steadily leaving users in the agony of choice. As a result,
Cloud service identification and discovery remains a hard problem due to different service descriptions, non-standardised naming conventions and heterogeneous types and features of Cloud services. In this paper, analysis
the research challenges and present a Web Ontology Language (OWL) based ontology, the Cloud Computing
Ontology (CoCoOn). It defines functional and non-functional concepts, attributes and relations of infrastructure services. We also present a system, CloudRecommender, that implements our domain ontology in a relational
model. The system uses regular expressions and Structured Query Language (SQL) for matching user requests
to service descriptions. We briefly describe the architecture of the CloudRecommender system, and demonstrate its effectiveness and scalability through a service configuration selection experiment based on a set of
prominent Cloud providers’ descriptions including Amazon, Azure, and GoGrid.
Keywords: Cloud Computing, Semantic Web, Recommender System, Service Computing, Operation Research
domain – one that aims to apply declarative and widget programming technique for
solving the Cloud service selection problem.
7. CONCLUSION AND FUTURE WORK
We have proposed ontology for classifying and representing the configuration
information related to Cloud-based IaaS services including compute, storage, and
network. The proposed ontology is comprehensive as it can not only capture static
confiugration but also dynamic QoS configuration on the IaaS layer. We also presented
the implementation of the ontology in the CloudRecommender system. The paper will
help readers in clearly understanding the core IaaS-level Cloud computing concepts and
inter-relationship between different service types. This in turn may lead to a
harmonization of research efforts and more inter-operable Cloud technologies and
services at the IaaS layer.
In future work, we intend to extend the ontology with the capability to store PaaS and
SaaS configurations. Moreover, we would also like to extend our ontology to capture the
dependency of services across the layers. For example, investigating concepts and
relationships for identifying the dependencies between compute service (IaaS)
configurations and the type of appliances (PaaS) that can be deployed over it. Before
mapping a MySQL database appliance (PaaS) to a Amazon EC2 compute service (IaaS),
one needs to consider whether they are compatible in terms of virtualization format.
Another avenue that we would like to explore is how to aggregate QoS configurations
across the IaaS, PaaS, and SaaS layers for different application deployment scenarios
(e.g., multimedia, eResearch, and enterprise applications). Notably, QoS aware service
selection problem [Jaeger et al. 2005] is a multi-criteria optimization problem, in order to
solve it, two distinct techniques will be explored: i) evolutionary optimization techniques,
the process of simultaneously optimizing two or more conflicting objectives expressed in
the form of linear or non-linear functions of criteria; ii) Multi-criteria decision-making
techniques, including Analytic Hierarchy Process (AHP) and others can handle mixed
qualitative and quantitative criteria.
REFERENCES
AmazonEC2 2012. http://aws.amazon.com/ec2/instance-types/ AWS Case Study 2012, The Server Labs. Available: http://aws.amazon.com/solutions/case-studies/the-server-
labs/
ARMBRUST, M., FOX, A., GRIFFITH, R., JOSEPH, A. D., KATZ, R., KONWINSKI, A., LEE, G.,
PATTERSON, D., RABKIN, A., STOICA, I., AND ZAHARIA, M. 2010. A view of Cloud Computing, Communications of the ACM Magazine, Vol. 53, No. 4, ACM Press, 50-58.
BRODSKY, A., BHOT, M. M., CHANDRASHEKAR, M., EGGE, N. E., AND WANG, X. S. 2009. A
Decisions Query Language (DQL): High-level Abstraction for Mathematical Programming over Databases. In Proceedings of the 35th SIGMOD international conference on Management of data (SIGMOD '09), RI,
USA, June 29 - July 02, 2009, C. BINNIG AND B. DAGEVILLE, Eds. ACM Press, New York, NY, USA.
BRUIJN, J.D., BUSSLER, C., DOMINGUE, J., FENSEL, D., HEPP, M., KELLER, U., KIFER, M., KONIG-RIES, B., KOPECHY, J., LARA, R., LAUSEN, H., OREN, E., POLLERES, A., ROMAN, D., SCICLUNA,
J., AND STOLLBERG, M. 2005. Web service modeling ontology (WSMO), W3C, Tech. Report.
CALDWELL, D., GILBERT, A., GOTTLIEB, J., GREENBERG, A., HJALMTYSSON, G., AND REXFORD, J. 2004.The cutting EDGE of IP router configuration. SIGCOMM Comput. Commun. Rev.34, 1 (January
2004), 21-26.
CHEN, X., MAO, Y., MAO, Z. M., AND MERWE, J. V. D. 2010. Declarative Configuration Management for Complex and Dynamic Networks. In Proceedings of the 6th ACM International Conference on emerging
Networking Experiments and Technologies (CoNEXT), 10 pages, Philadelphia, USA, ACM Press.
DOBSON, G., LOCK, R., AND SOMMERVILLE, I. 2005. QoSOnt: a QoS ontology for service-centric systems, In Proceedings of the 31st EUROMICRO Conference on Software Engineering and Advanced
Applications, Porto, Portugal, 30 August - 3 September 2005, 80-87.
ESKRIDGE, T., HAYES, P., AND HOFFMAN, R. 2006. Formalizing the informal: A Confluence of Concept Mapping and the Semantic Web. In Proceedings of the Second Int. Conference on Concept Mapping, San
José, Costa Rica, 2006, A. J. CANAS AND J. D. NOVAK, Eds. 247-254.
GENS, F. 2010. IDC’s Public IT Cloud Services Forecast: New Numbers, Same Disruptive Story. Available: http://blogs.idc.com/ie/?p=922
JAEGER, M. C., MUHL, G., AND GOLZE, S. 2005. QoS-aware composition of Web services: a look at
selection algorithms. In Proceedings of IEEE International Conference on Web Services (ICWS’05), IEEE Computer Society, Orlando, Florida, USA, July 11-15, 2005, 800-808.
LI, A., YANG, X., KANDULA, S., AND ZHANG, M. 2010. CloudCmp: comparing public cloud providers.
In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement(IMC '10), Melbourne, Australia, November 01 - 03, 2010, ACM, New York, USA, 1-14.
LIU, C., LOO, B. T., AND MAO, Y. 2011. Declarative automated cloud resource orchestration. In Proceedings of the 2nd ACM Symposium on Cloud Computing (SOCC '11). ACM, New York, NY, USA, Article 26, 8
pages.
MARTIN, D.L., PAOLUCCI, M., MCILRAITH, S.A., BURSTEIN, M.H., MADERMOTT, D.V., MCGUINNESS, D.L., PARSIA, B., PAYNE, T.R., SABOU, M., SOLANKI, M., SRINIVASAN, N., AND
SYCARA, K.P. 2004. Bringing Semantics to Web Services: The OWL-S Approach. In Proceedings of the
First International Workshop on Semantic Web Services and Web Process Composition, San Diego, CA, USA, July 6, 2004, J. CARDOSO AND A. P. SHETH, Eds. Springer, 26-42.
MAO, Y., LIU, C., MERWE, J.E.V.D., AND FERNANDEZ, M. 2011. Cloud Resource Orchestration: A Data-
Centric Approach. In Proceedings of the 5th biennial Conference on Innovative Data Systems Research (CIDR), Asilomar, California, USA, January 9-12, 2011, 241-248.
OWL2 2009. Web Ontology Language: Document Overview. W3C Recommendation, http://www.w3.org/TR/owl2-overview/.
OZSOYOGLU, G., AND AL-HAMDANI, A. 2003. Web Information Resource Discovery: Past, Present, and
Future. In Proceedings of the 18th International Symposium on Computer and Information Sciences, Antalya,
Turkey, November 2003, A. YAZICI AND C. SENER, Eds. Springer, Berlin, Heidelberg, 9-18.
Pingdom 2011. Web pages are getting more bloated, and here’s why. Available: http://royal.pingdom.com/2011/11/21/web-pages-getting-bloated-here-is-why/
RUIZ-ALVAREZ, A., AND HUMPHREY, M. 2011. An Automated Approach to Cloud Storage Service Selection. In Proceedings of the 2nd international workshop on Scientific cloud computing (ScienceCloud '11), San Jose, California, USA, June 08 - 11, 2011, ACM Press, New York, USA, 39-48.
Windows Azure Calculator 2013. http://www.windowsazure.com/en-us/pricing/calculator/. WADA, H., SUZUKI, J., YAMANO, Y., AND OBA, K. 2011. Evolutionary Deployment Optimization for
Service Oriented Clouds. Software: Practice and Experience 4, 469 – 493. WANG, L., RANJAN, R., CHEN, J., AND BENATALLAH, B. (editors) 2011. Cloud Computing:
Methodology, Systems, and Applications. Taylor and Francis Group , London, UK.
WANG, L., LASZEWSKI, G., YOUNGE, A., He, X., KUNZE, M., TAO, J., AND FU, C., 2010, Cloud Computing: a Perspective Study. New Generation Comput. 28(2): 137-146
WANG L., KUNZE, M., TAO, J., AND LASZEWSKI, G., 2011, Towards building a cloud for scientific
applications. Advances in Engineering Software 42(9): 714-722. WANG, L., AND FU, C., 2010, Research Advances in Modern Cyberinfrastructure. New Generation Comput.
28(2): 111-112.
WANG, L., LASZWSKI, G., CHEN, D., TAO, J., KUNZE, M., 2010, Provide Virtual Machine Information for
Grid Computing. IEEE Transactions on Systems, Man, and Cybernetics, Part A 40(6): 1362-1374.
WANG, L., Chen, D., HUANG, F.,, 2011, Virtual workflow system for distributed collaborative scientific
applications on Grids. Computers & Electrical Engineering 37(3): 300-310.
Yelp 2012. AWS Case Study, http://aws.amazon.com/solutions/case-studies/yelp/
YOUSEFF, L., BUTRICO, M., AND SILVA, D. D. 2008. Toward a Unified Ontology of Cloud Computing. In Grid Computing Environments Workshop, Austin, TX, USA, Nov 2008, GCE '08. IEEE Computer Society,