Semantic Technologies for Enterprise Cloud Management

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Semantic Technologies for Enterprise Cloud Management

  1. 1. Peter Haase, Tobias Mathäß, Michael Schmidt, Andreas Eberhart, Ulrich Walther fluid Operations AG Semantic Technologies for Enterprise Cloud Management ISWC, November 11, 2010, Shanghai
  2. 2. Motivation • Cloud Computing as a model in support of „everything-as-a-service“ • Several benefits for the consumer • Sold on demand • Elastic • Fully managed by provider • Private clouds becoming increasingly important • Enterprise-internal virtualization • Can be linked to public cloud solutions • Scalable access to computing resources and IT services vision: fully automated data center
  3. 3. Enterprise Clouds – the eCloud Vision All resources of an adaptive, cloud-enabled IT environment can be set up, monitored, and maintained from a single, unified, and intuitive management console:  Internal and external IT resources accessible across stack without vendor lock-in  High degree of automation and IT provisioning at click of button on the level of enterprise landscapes  Internal portal of private/public IT services with e.g. pay-as-you-go cost models
  4. 4. Manage IT like an eCloud Stack virtualization and semantic integration as foundational capabilities for efficient automation CXOsIT admins Application customers Different user groups with diverse demands: administration, documentation, reporting, analysis, …
  5. 5. Challenge 1: Data Integration MonitoringandManagement ApplicationTemplates Hardware Layer Landscape Layer Virtualization Layer Network Computing ResourcesNetw.-Att. Storage V L VLM VL VLM VL VLM VL VLM • Awareness of full IT stack required, from storage to application layer • Heterogeneity of resources across layers of IT stack • Heterogeneity across different vendors and product versions
  6. 6. Challenge 1: Data Integration MonitoringandManagement ApplicationTemplates Hardware Layer Landscape Layer Virtualization Layer Network Computing ResourcesNetw.-Att. Storage V L VLM VL VLM VL VLM VL VLM • Awareness of full IT stack required, from storage to application layer • Heterogeneity of resources across layers of IT stack • Heterogeneity across different vendors and product versions Use semantic data model for integrating semantically heterogeneous information to get a complete picture of the entire data center
  7. 7. Challenge 2: Collaborative Documentation and Annotation • Technical base information retrieved automatically from provider APIs • Challenges • Free-text documentation and augmentation of technical data • Associate bussiness information with technical data • Address heterogeneous data in a unified way • Use Cases • Which gold-level customers are affected if a storage filer breaks? • Which resources did department X consume within the last months?
  8. 8. Challenge 2: Collaborative Documentation and Annotation • Technical base information retrieved automatically from provider APIs • Challenges • Free-text documentation and augmentation of technical data • Associate bussiness information with technical data • Address heterogeneous data in a unified way • Use Cases • Which gold-level customers are affected if a storage filer breaks? • Which resources did department X consume within the last months? Apply Semantic Wiki technology to support collaboration
  9. 9. Challenge 3: Intelligent Information Access and Analytics • Different user roles with varying information needs • Administrators • Which resources am I responsible for? • What underlying components may cause application X to freeze? • Which IP addresses are currently in use? • Customers (service consumers) • What is the status of my systems? • Which projects am I involved in? • CXOs • Which compute resources are currently available? • What is the average CPU load of all VMs running on host X?
  10. 10. Challenge 3: Intelligent Information Access and Analytics • Different user roles with varying information needs • Administrators • Which resources am I responsible for? • What underlying components may cause application X to freeze? • Which IP addresses are currently in use? • Customers (service consumers) • What is the status of my systems? • Which projects am I involved in? • CXOs • Which compute resources are currently available? • What is the average CPU load of all VMs running on host X? Expressive ad-hoc queries that overcome the border of data sets. Visualization and visual exploration tools for structured data.
  11. 11. Our Solution: Widget-based UI • Resource-centric presentation • Living UI, which exploits semantics of underlying data • Large collection of predefined widgets, easily extendable Search and information Access • Coexistence of structured and unstructured data • Different search paradigms Data integration through providers • Convert data from a data source into RDF data format • High degree of reusability • Customizable, easily extensible
  12. 12. Unifying OWL Data Model Extract of the eCloudManager Intelligence Edition data model
  13. 13. Data Integration by Example Predicate Subject Object Predicate Object Predicate Predicate Object Predicate Object Object Object Subject Predicate Predicate Object Subject Predicate Object EMC Storage Provider Data Provider Layer
  14. 14. Data Integration by Example Predicate Subject Object Predicate Object Predicate Predicate Object Predicate Object Object Object Subject Predicate Predicate Object Subject Predicate Object EMC Storage Provider Data Provider Layer Subject Predicate Object Predicate Predicate Object Predicate Object Object Object Subject Predicate Object Virtualization Software Automatical alignment by flexible, key-based generation of unique URIs for the same components across different providers vmware Provider
  15. 15. Collaborative Documentation and Annotation • Technical Documentation • Resource-centric view • Edit wiki pages associated with data center resources • Interlinkage of Resources • User-defined Semantic Links in the Semantic Wiki • Completion of missing data • Ontology-driven edit forms Wiki Page in Edit Mode … … and Displayed Result Page
  16. 16. Flexible, Living UI • UI flexibly adjusts to semantics of underlying data • Which widgets to display for a resource depends on its properties • UI thus automatically composed based on the semantics of the underlying data • Widgets with varying functional focus • Visualization (e.g., PivotViewer) • Navigation (e.g., browsable graph view) • Collaboration (e.g., Semantic Wiki pages) • Mashups (e.g., connected product catalogs)
  17. 17. Search and Querying • Coexistence of structured and unstructured content requires hybrid search • Different search paradigms • Simple keyword search • Structured queries using SPARQL • Form-based search • Faceted Search • Query translation diversity covers different use cases and user groups
  18. 18. Dashboards, Analytics, Reporting • Queries can be directly included into Wiki pages/templates -> considerably lowers effort in maintaining Wiki • Evaluated dynamically when user visits the Wiki page • Type-based template mechanism • Visualization of queries as • Table Results • Bar Diagrams • Time plots over historical data • … Stacked Chart: Virtual Machines over time grouped by status
  19. 19. Ad-hoc Data Exploration • Leverage Pivot Viewer for Linked Data • Set-based exploration of heterogeneous resources • Integrated view on techical and business-level resources • Filtering with faceted search • Grouping by different aspects Visual data exploration with the PivotViewer
  20. 20. Experiences and Lessons Learned • RDF-based data integration approach with provider concept brings significant advantages in heterogeneous environments • Flexible, easily extendable • Fast setup (typically less than one day for new data centers) • Integration of additional data sources unproblematical • Semantic Wiki brings many benefits • Step from Wiki to Semantic Wiki feasible • Integration of live data (tables, charts, timeplots, etc.) in Wiki perceived as great benefit • Fast customization often replaces development of new modules
  21. 21. Experiences and Lessons Learned • Positive feedback on novel interaction paradigms • Visual exploration with Pivot viewer offeres unprecedented user experience • Graph view to better understand connections between resources • Semantic Technologies scale well to large data centers • For large data centers few millions of RDF triples • Aggregation of historic data to keep dataset manageable • Particular technical challenges we had to address • Scalability: take care on how you do it! • Missing features in current SPARQL implementation • Aggregation • Annotations
  22. 22. Related Projects • Benefit: high reusability of underlying technologies • Generic technologies for data integration, search, exploration etc. • Can seamlessly be applied to other domains • Core technologies of eCloudManager Intelligence Edition available as Open Source Platform for self-service Linked Data application development: Visit our • Linked Open Data demonstrator and • Life Science demonstrator at http://iwb.fluidops.com! The Information Workbench is publicly available as Open Source project
  23. 23. Thank you for your attention! CONTACT: fluid Operations AG Email: info@fluidOps.com Altrottstr. 31 Website: www.fluidOps.com Walldorf, Germany Tel.: +49 6227 3849-567 Interested in more information? Then check out our Information Workbench brochure in your ISWC 2010 starter pack!

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