1. Mastering Intelligent Clouds Engineering Intelligent Data Processing Services in the Cloud Sergiy Nikitin, Industrial Ontologies Group, University of Jyväskylä, Finland Presented at ICINCO 2010 conference Funchal, Madeira

2. ContentsContents Background on Cloud ComputingBackground on Cloud Computing Extending cloud computing stackExtending cloud computing stack UBIWARE platformUBIWARE platform Data Mining services in the CloudData Mining services in the Cloud ConclusionsConclusions

3. Cloud Computing: already on the market SalesForce.com (SFDC)SalesForce.com (SFDC) NetSuiteNetSuite OracleOracle IBMIBM MicrosoftMicrosoft Amazon EC2Amazon EC2 GoogleGoogle etc.etc. (for a complete survey see Rimal et al., 2009)(for a complete survey see Rimal et al., 2009)

4. Cloud Computing stack Cloud computing stack PaaS SaaS IaaS Hardware configuration Virtualization Machine OS-virtualization Raw data storage and network Structured storage (e.g. databases) Solution stack (Java, PHP, Python,.NET) Services (Payment, Identity, Search) Application (business logic) Application as a Service What add-value can we offer to the PaaS level?

5. Autonomic Computing A vision introduced by IBM in 2003 (Kephart et al.)A vision introduced by IBM in 2003 (Kephart et al.)  software components get a certain degree of self- awareness  self-manageable components, able to “run themselves” Why?Why?  To decrease the overall complexity of large systems  To avoid a “nightmare of ubiquitous computing” – an unprecedented level of complexity of information systems due to: drastic growth of data volumes in information systems drastic growth of data volumes in information systems heterogeneity of ubiquitous components, standards, data formats, etc.heterogeneity of ubiquitous components, standards, data formats, etc.

6. Intelligence as a Service in the cloud Agent-driven service API Configuration management Solution stack Domain models Data adaptation Intelligent services PaaS Structured storage (e.g. databases) Solution stack (Java, PHP, Python,.NET) Services (Payment, Identity, Search) UBIWARE Smoothly integrate with the infrastructure Smoothly integrate with the infrastructure Build stack-independent solutions Build stack-independent solutions Automate reconfiguration of the solutions Automate reconfiguration of the solutions

7. UBIWARE platform.class Blackboard Role Script RABRABRAB RAB Beliefs storage UBIWARE Agent Pool of Atomic Behaviours S-APL repository S-APL Data

8. Cloud Platform Provider Virtual machine SW Platform Customer applications and services Extended API PCA PMA API extension: OS perspective PCA – Personal Customer Agent PMA – Platform Management Agent

9. Data Adaptation as a Service Cloud Platform Provider Virtual machine SW Platform Customer applications and services Extended API PCA PMA PCA – Personal Customer Agent PMA – Platform Management Agent Adapter Agent Files Data Service DB/KB

10. Cloud Platform Provider Virtual machine SW Platform Customer applications API Virtual machine API Service execution environment PCA PMA Platform-driven service execution in the cloud PCA – Personal Customer Agent PMA – Platform Management Agent

11. Agent-driven PaaS API extension Agent-driven flexible intelligent service API Agent-driven Adapters Agent-driven intelligent services Smart cloud stack Smart Ontology Standards & compatibility System configuration and policies Domain models Failure-prone maintenance Stack control and updates Embedded and remote services Service mobility Configurable model Proactive self-management Smart data source connectivity Configurable data transformation Proactive adapter management User applications in cloud

12. Intelligent services: PaaS API extension Agent-driven flexible intelligent service API Agent-driven intelligent services Service mobility Configurable model Proactive self-management User applications in cloud

13. Agent-driven data mining services Model Input Output Vector DM model DM result Agent service  Data mining applications are capabilities  Agents can wrap them as services  PMML language - a standard for DM-model representations  Data Mining Group. PMML version 4.0. URL http://www.dmg.org/pmml-v4-0.html

14. Header Model development environment information Version and timestamp PMML model Data dictionary Definition of: variable types, valid, invalid and missing values Data Transformations Data aggregation and function calls Normalization, mapping and discretization Model Description and model specific attributes Mining schema Definition of: usage type, outlier and missing value treatment and replacement Targets Definition of model architecture/parameters Score post-processing - scaling PMML*: data mining model descriptions PMML* - Predictive Model Markup Language (www.dmg.org/pmml-v3-0.html)

15. Data mining service types Model Input Output Vector to be classified: alarm message: V1={0.785, High, node_23} Paper machine alarms classifier neural network model (M1) Vector class of V1 is: “Urgent Alarm” according to model M1 Fixed model service Model player service Model construction service Model Inputs Outputs Set up a model M1 Paper machine alarms classifier neural network model (M1) Model M1 assigned Vector to be classified: alarm message: V1={0.785, High, node_23} Model player Vector class of V1 is: “Urgent Alarm” according to model M1 Model Input Output Learning samples and the desired model settings Model M1 parameters Model constructor

16. A use case for data mining service stack Model Input Output Pattern of learning data to be collected: ?V={?p1, ?p2, ?p3} Distributed query planning and execution A set of learning samples (vectors) 1 Learning samples and the desired model settings Model M1 parametersModel constructor Set up a model M1 Paper machine alarms classifier neural network model (M1) Model M1 assigned Vector to be classified: alarm message: V1={0.785, High, node_23} Model player Vector class of V1 is: “Urgent Alarm” according to model M1 2 3 4  A “Web of Intelligence” case:

17. Data Mining services in UBIWARE Data Mining service Model construction service Computational service Fixed model service Model player service Ontology construction Core DM service ontology Data mining domain Problem domain Mining method Supervised Learning Unsupervised learning ClusteringkNN Neural networks Industry Process Industry Electrical Engineering Power networksPower plant Paper industry

18. UBIWARE in cloud computing stack Cloud computing stack Technologies in cloud Platform as a service Applications and Software as a Service Infrastructure as a service Hardware configuration Virtualization Machine OS-virtualization Raw data storage and network Structured storage (e.g. databases) Solution stack (Java, PHP, Python,.NET) Services (Payment, Identity, Search) Application (business logic) Application as a Service DM model for paper industry Example application DM model wrapped as a service for paper industry Cross-domain Middleware components Componentization & Servicing Connectors, Adapters RABs, Scripts UBIWARE for control and management in cloud Semantic Business Scenarios Domain model (Ontology) & components Domain-specific components as services Cross-layer configuration & management mechanisms Agent-driven service API Data Mining service player

19. ConclusionsConclusions Web intelligence as a cloud serviceWeb intelligence as a cloud service Ubiware is a cross-cutting management and configuration glueUbiware is a cross-cutting management and configuration glue Advanced data adaptation mechanisms as cloud servicesAdvanced data adaptation mechanisms as cloud services  A competitive advantage for cloud providers  Seamless data integration for service consumption and provisioning Autonomous agents as a Service (A4S)Autonomous agents as a Service (A4S)  Supply any resource with the “autonomous manager”

20. Thank you!