1. Information Systems & Semantic Web University of Koblenz ▪ Landau, Germany Cloud Computing What, why, how? Noam Bercovici Renata Dividino

2. ISWeb - Information Systems & Semantic Web Oberseminar 2 of 23 Motivation Count how frequent each words appears in the corpus MEDline (18 millions texts)

3. ISWeb - Information Systems & Semantic Web Oberseminar 3 of 23 Motivation I want to extend my research to another corpus Need more computing resources

4. ISWeb - Information Systems & Semantic Web Oberseminar 4 of 23 Agenda  Introduction  Data Grid vs. Computing Grid  Grid Computing  Cloud Computing  Data Grid (HaDoop File System)  Computing Grid (Map Reduce)  Conclusion

5. ISWeb - Information Systems & Semantic Web Oberseminar 5 of 23 Data Grid vs. Computing Grid  Data Grid:  distributed data storage  controlled sharing and management of large amounts of distributed data.  Computing Grid:  Parallel execution  divide pieces of a program among several computers Data Grid + Computing Grid Grid Computing

6. ISWeb - Information Systems & Semantic Web Oberseminar 6 of 23 Grid Computing The Grid Master Slaves Task

7. ISWeb - Information Systems & Semantic Web Oberseminar 7 of 23 Grid Computing  Motivation: high performance, improving resources utilization  Aims to create illusion of a simple, yet powerful computer out of a large number of heterogeneous systems  Tasks are submitted and distributed on nodes in the grid

8. ISWeb - Information Systems & Semantic Web Oberseminar 8 of 23 Cloud Computing “The interesting thing about cloud computing is that we’ve redefined cloud computing to include everything that we already do. “ Larry Ellison during Oracle’s Analyst Day

9. ISWeb - Information Systems & Semantic Web Oberseminar 9 of 23 Cloud Computing  Pay-as-you-go  No initial investments  Reduced operation costs  Scalability  Availability

10. ISWeb - Information Systems & Semantic Web Oberseminar 10 of 23 Grid vs. Cloud Computing AreaGridCloud Motivation Performance, CapacityFlexibility, scalability Infrastructure Owner by participantsProvided by third party Business Model Share costsPay-as-you-go Virtualization In some casesPrevalent Typical Applications Research, batch jobsOn-demand infrastructure, web applications Advantages Mature TechnologiesLow entry barrier, flexible Disadvantages Initial investments, less flexibility Third party dependence, costs, open issues

11. ISWeb - Information Systems & Semantic Web Oberseminar 11 of 23 Cloud Computing - Open Issues  Bandwidth and latency  Lack of standard and portability  „Black-box“ implementations  Security and lack of control  Immature tools and framework support  Legal issues (ownership, auditing, etc)  Limited Service Level of Agreements (SLAs)

12. ISWeb - Information Systems & Semantic Web Oberseminar 12 of 23 Data Grid vs. Computing Grid  Data Grid:  distributed data storage  controlled sharing and management of large amounts of distributed data.  Computing Grid:  Parallel execution  divide pieces of a program among several computers Data Grid + Computing Grid Grid Computing

13. ISWeb - Information Systems & Semantic Web Oberseminar 13 of 23 Data Grid (Hadoop FS - Overview)  Caching of Data Namenode (master node) Metadata (Name,..,..) … Index: Datanodes (Slave node) Block ops Client Ask specific text Replication

14. ISWeb - Information Systems & Semantic Web Oberseminar 14 of 23 Data Grid (HDFS - Replication Data)

15. ISWeb - Information Systems & Semantic Web Oberseminar 15 of 23 Counting Words in Text Files 1320 0518 7235 … Split-Operation countWords(File) Map-Operation w1:w1: w2:w2: w4:w4: w3:w3: w5:w5: … … 6234 0100 w 1 : 6 w 2 : 14 w 3 : 15 w 4 : 17 w 5 : 1 Reduce-Operation

16. ISWeb - Information Systems & Semantic Web Oberseminar 16 of 23 Advantages of Hadoop  Purely written in Java, requires installation of Cygwin under Windows  Available under LGPL and Apache 2.0 license  Usually offers only one implementation for the different features of a grid framework  May also use other file systems than Hadoop FS  Very flexible implementation of MapReduce  For split operation only supports FileSplit out of the box  Better suited for computations where …  … large data collections should be handled  … if reduce-operation is more than a simple aggregation of the map‘s output

17. ISWeb - Information Systems & Semantic Web Oberseminar 17 of 23 Danke! Questions?