Peter Haase, Tobias Mathäß, Michael Schmidt, Andreas Eberhart, Ulrich Walther

fluid Operations AG

Semantic Technologies

for Enterprise Cloud Management

ISWC, November 11, 2010, Shanghai

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

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

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, …

Challenge 1: Data Integration

Monitoring

and

Manag

em

ent

Applic

atio

n T

em

pla

tes

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 toapplication layer

• Heterogeneity ofresources acrosslayers of IT stack

• Heterogeneityacross different vendors andproduct versions

Challenge 1: Data Integration

Monitoring

and

Manag

em

ent

Applic

atio

n T

em

pla

tes

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 toapplication layer

• Heterogeneity ofresources acrosslayers of IT stack

• Heterogeneityacross different vendors andproduct versions

Use semantic data model for integrating semantically heterogeneous

information to get a complete picture of the entire data center

Challenge 2: Collaborative Documentation and Annotation

• Technical base information retrievedautomatically 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?

Challenge 2: Collaborative Documentation and Annotation

• Technical base information retrievedautomatically 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

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?

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.

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

Unifying OWL Data Model

Extract of the eCloudManager Intelligence Edition data model

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

ProviderData Provider Layer

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

ProviderData Provider Layer

Subject

Predicate

Object

Predicate

Predicate

Object

Predicate

Object

Object

Object

Subject

Predicate

Object

Virtualization Software

Automatical alignment byflexible, key-basedgeneration of unique URIs for the same componentsacross different providers

vmware

Provider

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

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 theunderlying 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)

Search and Querying

• Coexistence of structured and unstructured content requireshybrid 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

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

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

Experiences and Lessons Learned

• RDF-based data integration approach with provider conceptbrings 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

Experiences and Lessons Learned

• Positive feedback on novel interaction paradigms

• Visual exploration with Pivot viewer offeres unprecedented userexperience

• 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

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

Thank you for your attention!

CONTACT:fluid Operations AG Email: info@fluidOps.comAltrottstr. 31 Website: www.fluidOps.comWalldorf, Germany Tel.: +49 6227 3849-567

Interested in more information?

Then check out our Information Workbench brochure in your ISWC 2010 starter pack!