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Decision support for partially moving applications to cloud environments The example of Business Intelligence Adrián Juan-Verdejo Dr. Henning Baars University.

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Presentation on theme: "Decision support for partially moving applications to cloud environments The example of Business Intelligence Adrián Juan-Verdejo Dr. Henning Baars University."— Presentation transcript:

1 Decision support for partially moving applications to cloud environments The example of Business Intelligence Adrián Juan-Verdejo Dr. Henning Baars University of Stuttgart and CAS Software AG 21st of April 2013

2 Motivation 2 / 20 Legacy applications can leverage cloud computing: scalability, availability, cost... Adapting an application is a complicated decision-making process Many factors Interdependent factors Security and privacy QoS requirements Decision-support system Partial migration Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo

3 Migration Process 3 / 20 Partial Migration Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo

4 Goal and Challenges Assist the partial migration of legacy applications to cloud computing environments Methodology and decision support system Provide the needed functionality Respect different set of requirements Privacy and security-related requirements QoS requirements Components interdependencies Select the right cloud provider Still be economically beneficial Goal Challenges Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 4 / 20

5 Topics Motivation & Challenges Research Gap Approach: Moving applications to cloud env. Moving BI to cloud environments Conclusion & Acknowledgements Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 5 / 20

6 Research workFocusGranularityApplication's Setting Moving-to-the-cloud problem[4] Moving apps. To cloud envs.Components Pre existing applications Cloudward Bound [6]MCDM migrationComponentsEnterprise App Volley [11]Data partitioningMapReduce JobsMapReduce Manticore [12]Code partitioningCode entitiesSoftware services HybrEx [13] Data and system partitioning focusing on privacy Components Distributed Applications COPE [14] Automated orchestration using declarative languages Virtual Machines Distributed Applications CloudGenious [15]Web servers MCDM migrationVirtual MachinesWeb Apps (MC 2 ) 2 [16]MCDM migrationVirtual Machines Conceptual framework Conductor [17]Orchestration deploymentMapReduce JobsMapReduce Research Gap (1/2) Multi-criteria decision analysis for the migration of components within a legacy enterprise application Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 6 / 20

7 Research Gap (2/2) Holistic multiple-criteria decision-making approach to find the best suitable deployment Heterogeneous and interdependent users requirements: QoS, privacy, security, business, economics Legacy applications architecture Selection of the right cloud provider Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 7 / 20

8 Topics Motivation & Challenges Research Gap Approach: Moving applications to cloud env. Moving BI to cloud environments Conclusion & Acknowledgements Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 8 / 20

9 Approach: Moving applications to cloud env. Description of pre-existing system Architecture Dependencies Data sensitivity Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 9 / 20

10 Approach: Moving applications to cloud env. Division and scattering of components Max Benefits (M) – InternetCosts(M) Subject to Privacy, security, gobernance policies QoS requirements Pre existing interdependencies Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 10 / 20

11 Approach: Moving applications to cloud env. Multi-criteria decision-making Analytical Hierarchy Process (AHP) [38] … … Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 11 / 20

12 Topics Motivation & Challenges Research Gap Approach: Moving applications to cloud env. Moving BI to cloud environments Conclusion & Acknowledgements Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 12 / 20

13 What is BI? Integrated and multi-layered IT-based management and decision support Cloud-BI Heterogeneous components Different requirements Interdependent Baars, H. and H.G. Kemper, Business Intelligence in the Cloud?, in 14th Pacific Asia Conference on Information Systems (PACIS), 2010, Taipeh, Taiwan. Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 13 / 20

14 Inclusion of specialized data analysis functionality on CC Interdependencies with data Data sensitivity Volume of data to be moved Cost Scenario 1: data analysis functionality Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 14 / 20

15 Move a reporting or Online Analytical Processing frontend Security & privacy Data updates Consistency Cost Performance Scenario 2: Move OLAP frontend Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 15 / 20

16 Scenario 3: Move Operational BI Operational BI Sometimes called real-time BI Decisions based on real-time data e.g. call centre Move an Operational BI solution BI triggers events in other systems (active BI) Data updates in both directions Where to place functionality to trigger events Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 16 / 20

17 Scenario 4: Move Selected ETL procedures Move selected Extract-Transform-Load procedures Data sources in cloud environments Diminish traffic Routines into ETL before feeding data to DWH Pre-processing unstructured data Discovering non-evident duplicate entries Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 17 / 20

18 Topics Motivation & Challenges Research Gap Approach: Moving applications to cloud env. Moving BI to cloud environments Conclusion & Acknowledgements Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 18 / 20

19 Conclusion and Acknowledgements Generic cloud migration framework interdependent users requirements: QoS, privacy, security, business, economics applications architecture selection of the right cloud provider Apply it to the real-case scenario of BI Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 19 / 20

20 Questions? Adrián Juan-Verdejo University of Stuttgart and CAS Software AG Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 20 / 20

21 Future steps Continue Decision model refinement Further characterize criteria Apply DM to the identified BI scenarios Experiment with EMF tools for legacy application description Incorporate cloud alternatives specifics SMICloud 21 / 23 Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo

22 Papers HotTopics with Vahid and Bholanath Juan-Verdejo A., Baars, H. Decision support for partially moving applications to the Cloud – the example of Business Intelligence. In Proceedings of the International Workshop on Hot Topics in Cloud Services (HotTopiCS 2013) within the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013), April 18-24, Prague, To be submitted Refinement of topic Paper with Vahid and Bholanath Adrián Juan-Verdejo, Bholanathsingh Surajbali, Seyed Vahid Mohammadi, Henning Baars, and Hans-Georg Kemper. Moving Business Intelligence to cloud environments: A security-enhanced Framework 22 / 23 Papers since mid-term Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo

23 Cloud migration process Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 23 / 17

24 Approach: Moving applications to cloud env. Division and scattering of components Max Benefits (M) – InternetCosts(M) Subject to Privacy, security, gobernance policies QoS requirements Pre existing interdependencies 24 / 18 Decision support for partially moving applications to the Cloud Adrián Juan-Verdejo

25 BI system running within a Cloud Provider Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 25 / 17

26 CMI Qualitative Parameters (NNci) – Sensitivity of the data to be migrated Regulated data, most sensitive. Credit card numbers, bank accounts, driver's license, health information... Confidential data, high sensitivity. Personnel information, financial information, contracts Public data, low sensitivity. Maps, publicly available product info

27 Cloud Migration Index Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 27 / 17 Hard conditions Relative ConditionConclusion Policiy constraints not satisfiedNot migrated Constraints on delay/latency increase exceeded Not migrated ConditionConclusion Criteria+ or – (or depends)

28 Cloud Migration Index Assesses the suitability of a component to be migrated to a cloud environment: Calculate quantitative values Pair-wise comparisons (or other methods) to calculate the qualitative values Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 28 / 17

29 Framework Prototype (0/3) Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 29 / 17 Cloud provider and VM image selected Specify its characteristics as they affect the criteria to move components or data –SMICloud: Quality model based on SMI framework –STRATOS:

30 Framework Prototype (1/3) Define legacy application, application components requirements, and components hard constraints: policies and performance

31 Framework Prototype (2/3) EMF-based Frameworks CAFE VbMF Present new deployment to the user by using the same graphical tools

32 Framework Prototype (3/3) Estimate systems behaviour after migration Tune ranking Decision-making process related to migration options

33 Bibliography 1.Armbrust, M., et al., A view of cloud computing, Communications of the ACM, 2010, Vol. 53 (4), p Khajeh-Hosseini, A., et al., Decision support tools for cloud migration in the enterprise, in IEEE International Conference on Cloud Computing (CLOUD), 2011: IEEE. 3.Rygielski, P. and S. Kounev, Network Virtualization for QoS-Aware Resource Management in Cloud Data Centers: A Survey, Praxis der Informationsverarbeitung und Kommunikation, 2013, Vol. 36 (1 ), p. 55–64. 4.Leymann, F., et al., Moving applications to the cloud: an approach based on application model enrichment, International Journal of Cooperative Information Systems, 2011, Vol. 20 (03), p Rizou, S.a.P., A., Towards value-based resource provisioning in the Cloud, in IEEE 4th International Conference on Cloud Computing Technology and Science, Hajjat, M., et al., Cloudward bound: planning for beneficial migration of enterprise applications to the cloud, 2010: ACM. 7.Moss, S.T. and S. Atre, Business Intelligence Roadmap – The Complete Project Lifecycle for Decision Support Applications, 2003, Boston: Addison-Wesley 8.Turban, E., R. Sharda, and D. Delen, Decision Support and Business Intelligence Systems, 9th edition ed, 2010, Bosten u.a.: Pearson. Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 33 / 17

34 Bibliography 9.Zimmer, M., H. Baars, and H. Kemper, The Impact of Agility Requirements on Business Intelligence Architectures, in System Science (HICSS), th Hawaii International Conference on, 2012: IEEE. 10.Thompson, W.J.J. and J.S. van der Walt, Business intelligence in the cloud, SA Journal of Information Management, 2010, Vol. 12 (1), p. 5 pages. 11.Agarwal, S., et al., Volley: Automated data placement for geo-distributed cloud services, in Proceedings of the 7th USENIX conference on Networked systems design and implementation, 02010: USENIX Association. 12.Kaviani, N., E. Wohlstadter, and R. Lea, MANTICORE: a Framework for Partitioning Software Services for Hybrid Cloud, in 4th ieee international conference on cloud computing technology and science2012: Taipei, Taiwan. 13.Ko, S.Y., K. Jeon, and R. Morales, The hybrex model for confidentiality and privacy in cloud computing, in Proceedings of the 2011 conference on Hot topics in Cloud Computing. USENIX Association, Portland, OR, Liu, C., B.T. Loo, and Y. Mao, Declarative automated cloud resource orchestration, in Proceedings of the 2nd ACM Symposium on Cloud Computing, 2011: ACM. 15.Menzel, M. and R. Ranjan, CloudGenius: Automated Decision Support for Migrating Multi- Component Enterprise Applications to Clouds, in International World Wide Web Conference Committee (IW3C2), Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 34 / 17

35 Bibliography 16.Menzel, M., et al., (MC2) 2: A Generic Decision-Making Framework and its Application to Cloud Computing, Software: Practice and Experience, 2011, Vol. 17.Wieder, A., et al., Orchestrating the Deployment of Computations in the Cloud with Conductor, in Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, 2012: USENIX Association. 18.Chong, S., et al., Building secure web applications with automatic partitioning, Communications of the ACM, 2009, Vol. 52 (2), p Hunt, G.C. and M.L. Scott, The Coign automatic distributed partitioning system, Operating systems review, 1998, Vol. 33, p Apache-Foundation, The Apache Hadoop project., 2013, Last Update, Date, Available from: 21.Baars, H. and H.G. Kemper, Management Support with Structured and Unstructured Data – An Integrated Business Intelligence Framework, Information Systems Management, 2008, Vol. 25 (2), p Shollo, A. and K. Kautz, Towards an Understanding of Business Intelligence (Paper 86), in Australasian Conference on Information Systems 2010 (ACIS 2010), 2010, Brisbane (Australia). 23.Negash, S., Business Intelligence, Communications of AIS, 2004, Vol. 13, p Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 35 / 17

36 Bibliography 24.Golfarelli, M., S. Rizzi, and I. Cella, Beyond data warehousing: what's next in business intelligence?, in Proceedings of the 7th ACM international workshop on Data warehousing and OLAP, 2004: ACM. 25.Marjanovic, O., The Next Stage of Operational Business Intelligence: Creating New Challenges for Business Process Management, in 40th Annual Hawaii International Conference on System Sciences (HICSS), 2007: IEEE. 26.Manyika, J., et al., Big data: The next frontier for innovation, competition, and productivity, Jacobs, A., The pathologies of big data, Communications of the ACM, 2009, Vol. 52 (8), p Gutierrez, N., Business Intelligence (BI) Governance, Infosys White Paper, Baars, H. and H.G. Kemper, Business Intelligence in the Cloud?, in 14th Pacific Asia Conference on Information Systems (PACIS), 2010, Taipeh, Taiwan. 30.Baars, H. and H.G. Kemper, Ubiquitous Computing–an Application Domain for Business Intelligence in the Cloud?, 2011, Vol. 31.Thomson, W.J.J. and J.S. van der Walt, Business intelligence in the cloud South African Journal of Information Management, 2010, Vol. 12 (1). 32.Qie, L. and H. Baars, Die Cloud als neuer Ansatz zur Erhöhung der BI-Agilität?, BI Spektrum, 2012, Vol. 7 (2), p Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 36 / 17

37 Bibliography 33.Gartner, Gartner Says Nearly One Third of Organizations Use or Plan to Use Cloud Offerings to Augment Business Intelligence Capabilities, 2012, Last Update, Date [cited th of January 2012], Available from: 34.Zeleny, M., Multiple criteria decision making, Vol. 25, 1982: McGraw-Hill New York. 35.Saaty, T.L., Theory and applications of analytic network process, Vol. 4922, 2005: RWS publications Pittsburgh. 36.Tran, V.X., H. Tsuji, and R. Masuda, A new QoS ontology and its QoS-based ranking algorithm for Web services, Simulation Modelling Practice and Theory, 2009, Vol. 17 (8), p Garg, S.K., S. Versteeg, and R. Buyya, Smicloud: A framework for comparing and ranking cloud services, in Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on, 2011: IEEE. 38.Bain, Henry, Nigel Howard, and Thomas L. Saaty. Using the analysis of options technique to analyze a community conflict. Journal of Conflict Resolution (1971): Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 37 / 17

38 todo Slide 1 Slide 2 Check spelling Check footnotes Change name Decision support for partially moving applications to the Cloud: The example of BI Adrián Juan-Verdejo 38 / 17


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