1 Teaching Cloud Computing and Windows Azure in Academia Domenico Talia UNIVERSITA DELLA CALABRIA & ICAR-CNR Italy Faculty Days 2010 – September 16, ROME

2 Sommario Obiettivi del Corso Struttura e Contenuti Concetti di Base e Sistemi Sistemi Cloud Commerciali e Open Source Windows Azure Alcune attività di Ricerca allUNICAL

3 Obiettivi del Corso Il materiale didattico é principalmente volto a favorire l'introduzione dei concetti di base e le architetture dei sistemi Cloud. Alcuni corsi che potrebbero includere il materiale didattico: Cloud Computing Sistemi Paralleli/Calcolo Parallelo Sistemi Distribuiti Modelli e Architetture per il Web

4 Contenuti del Corso ArgomentoDurata 1.Cloud computing: definizioni e concettiCloud computing: definizioni e concetti2 h 2. Web services, Grid e CloudWeb services, Grid e Cloud2-3 h 3. Modelli service-oriented di Cloud computingModelli service-oriented di Cloud computing2-3 h 4. Sistemi Cloud commerciali e open sourceSistemi Cloud commerciali e open source2-3 h 5. Il sistema Azure: architetture e serviziIl sistema Azure: architetture e servizi2 h

5 Contenuti del Corso Il materiale offre una introduzione generale a tutti gli argomenti trattati e poi descrive con maggior dettaglio i concetti principali del Cloud computing e i dettagli tecnici ed architetturali dei sistemi descritti. Come possibili progetti di fine corso il docente può creare uno o più progetti basati sulluso di sistemi Cloud commerciali come Azure, Google o Amazon il cui costo di accesso ed utilizzo è molto limitato, oppure basati sulluso di uno o più dei sistemi Cloud open source che sono scaricabili ed istallabili anche su computer di limitate dimensioni.

6 Lezione 1: Cloud Computing: Definizioni e Concetti

7 Lezione 2: Web Services, Grid e Cloud

8 Lezione 3: Modelli Service-Oriented per il Cloud

9 Lezione 4: Cloud Commerciali e Open Source

10 Lezione 5: Windows Azure: Architetture e Servizi

11 Materiale sul Web

12 Distributed Discovery Services Exploiting the SOA model it is possible to define basic services for supporting distributed data mining tasks/ knowledge discovery applications in large scale distributed systems for science and industry (from a private Cloud to Interclouds). Those services can address all the aspects that must be considered in data mining and in knowledge discovery processes data selection and transport services, data analysis services, knowledge models representation services, and knowledge visualization services.

13 Collection of Services for Distributed Knowledge Discovery It is possible to define services corresponding to Single KDD Steps All steps that compose a KDD process such as preprocessing, filtering, and visualization are expressed as services. Single Data Mining Tasks Here are included tasks such as classification, clustering, and association rules discovery. Distributed Data Mining Patterns This level implements, as services, patterns such as collective learning, parallel classification and meta-learning models. Data Mining Applications or KDD processes This level includes the previous tasks and patterns composed in a multi-step workflow.

14 This collection of data mining services can constitute an Open Service Framework for Grid-based Data Mining Allowing developers to program distributed KDD processes as a composition of single and/or aggregated services available over a Cloud. Those services should exploit other basic Cloud services for data transfer, replica management, data integration and querying. Knowledge Discovery Services Open Service Framework for Cloud-based Knowledge Discovery

15 Knowledge Discovery Cloud Services By exploiting the Cloud services features it is possible to develop knowledge discovery services accessible every time and everywhere (remotely and from small devices). This approach may result in Service-based distributed data mining applications Data mining services for communities/virtual organizations. Distributed data analysis services on demand. A sort of knowledge discovery eco-system formed of a large numbers of decentralized data analysis services.

16 Grazie