1. Cloud Computing

2. Cloud Computing Overview Scott@scottstreit.com Course Content https://content.scottstreit.com

3. Rules Address me as “Scott” Being able to do something is more important than memorizing. I will not ask you to memorize. My tests ask you to think and explain. I ask you to take a position. Your grade on a test (mid-term, final) is not the final grade. You must successfully complete all homeworks to pass the course. You pick your grade – I'll explain.

4. Goals Einstein said, As simple as possible, but no simpler. If you cannot explain it simply, you do not understand it well enough. Any fool can make things more complex it takes genius to find the simplicity. Great science is simple.

5. How did we get here? Turing Machine P-V Semaphore – Unix – Flat Files 1972, Dr. E. F. Codd invented Relational Database, Linear Algebra → Data Storage. RDBMS – Transactions – Bob Epstein 1988 --- 1995 --- Databases fault tolerant and load balanced. They were tightly coupled. Startup and you want to do load balancing... Larger than anyone ever has..... What do you do.?

6. Cloud Computing What is Cloud Computing? Coupling Fault Tolerance Load Balancing Loosely coupled systems participating load balancing and fault tolerance such that all computers minus one may fail and processing continues.

7. Cloud Computing What is Cloud Computing? The application software shall not change irrespective of the number of nodes.

8. 5 Rules of Software Development 1.W3C specifications ahead of JSR specifications. 2.JSR ahead of defacto standards. 3.Defacto standards ahead of custom development. 4.Compositional patterns to create software systems. 5.Use design patterns when creating custom code.

9. One Definition Radically load balanced and fault tolerant such that all systems but one may fail and processing continues. What do we mean by this? Map/Reduce, Solr, etc. What was fault tolerance in 1990? Any one system may fail and processing continues.

10. NIST Definition 1. Software as a Service 2. Platform as a Service 3. Infrastructure as a Service 4. Also note, Software as a Service plus Software as a Service. 5. Implied Elasticity - not seen.

11. Homework Assignment #1 Singleton 1. Find the Singleton pattern. 2. Explain it. 3. Give one use of it. Adapter/Adaptee 4. Find the Adapter/Adaptee pattern 5. Explain it 6. Give one use of it. 7. Consider Mark Grand.. but there are others.

12. Some Examples of Software as a Service bankofamerica.com google.com amazon.com many other.com

13. Software as a Service All runs in a browser. May extend application via the browser. Salesforce.com Google sites.

14. Software as a Service with Software Plus Services Amazon.com Anyone may sell a book, wrapped in an application. Contingent on Web Services. REST vs. SOAP

15. Platform as a Service Constrain the Solution... Commit to an environment. Pick a Technology. Choices - LAMP, War,.net. Dictates skill sets, tool, etc. Elastic.

16. LAMP vs. WAR Where is LAMP best. Linux, Apache, MySQL, Php 1) Your views closely model your database design. 2) Security requirements are not excessive. Where is War best. 1) You views do not closely model your database Design. In fact there probably is not RDBMS. Elastic. 2) Serious Security Requirements (Underwriting).

17. Infrastructure as a Service Bare bones systems. Elastic. Drives, OS, Database. Close to Hardware. Elastic.

18. Public Clouds SAAS, PAAS, IAAS available to everyone. Offerings from Amazon, Google, Microsoft Full service. Little administration for Client. Little control of resources.

19. Private Clouds Cloud running on dedicated hardware. May or may not be your computers. Hardware is not shared. Constrains virtualization.

20. Hybrid Clouds Public plus Private Community Finding common uses.

21. Solutions Network Layer – sticky sessions Asynchronous federation. Web Layer – cache Enterprise Layer – cache Database Layer – XA, Replication, RAC Data Layer – non-relational – Map/Reduce Data Layer – non-relational - Lucene/Solr

22. How Did We End Up Here? Google RAC expensive Loosely Coupled. Hardware, OS Independent.

23. Big Data Processing Search Asynchronous Non-Relational Paradigm

24. Map/Reduce is a paradigm for deterministic processing, highly replicated and a formal paradigm Lucene is a dependent jar file, allowing solutions around it. Built for text processing initially. Add fault tolerance through Solr. Solution is inverted sparse index Same author different problem Comparison

25. Hadoop: An implementation of Map/Reduce from apache. Pure Java, in production, server based, HDFS, etc. Lucene: A dependent jar file for sparse index processing. Significant Focus – Non Relational

26. Setup an Apache Server that runs php. Create a simple page that creates a session. Go to a second page and see the session variable. Homework Assignment #2