Cloud Computing. Summary

Transcription

1 Cloud Computing Lecture Summary Teaching Staff. Rooms and Schedule. Goals. Context. Syllabus. Reading Material. Assessment and Grading. Important Dates.

2 João Garcia Teaching Staff Assistant Professor at DEI: OS, SDIA, Advanced Arquitectures, Cloud Computing. Researcher at the Distributed Systems Groups of INESC ID Lisboa: Large Scale Computing Platforms. Context Aware Computing, Hoarding. Fault Replication. Rooms and Schedule Lectures: Tuesdays, from 9:30to 11:00 in room Wednesdays, from 14:00to 15:30 in room Labs: Tuesdays, from11:00to 12:30 in room Wednesdays, from15:30to 17:00 in room You can start enrolling. Office hours: . Tuesdays, from 13:30to 14:30in room 2-N3.13. Wednesdays, from 11:00 to 13:00 in room 2-N first if you plan to attend especially on Tuesday.

3 Goals Understand the cloud computing paradigm and surrounding issues. Learn about the technologies that originated cloud computing: clusters, grid computing, utility computing, virtualization, middleware and autonomic computing. Learn about design and development techniques for cloud computing. Related Courses Computer Networks Network & Serv. Management Operating Systems Distributed Systems Internet Platforms Cloud Computing Parallel & Distr. Computing

4 Historical Context Computing Multiprocessing Distributed Processing Parallel Processing DSM Cluster Grid Edge Cycle-sharing Cloud Networking No network Ethernet/Internet Large bandwidth LANs Large bandwidth WANs What is cloud computing? Solution for really large scale issues. Good vapourware: more HW, larger data sets. Examples: Web specific problems: crawling, indexing, search. Data mining. Genome datamining. High energy physics. Astronomy.

5 What is cloud computing? Highly scalable architectures. Virtualization and open API Web Services very important. Examples: Google, 15k to 450k (probably ~250k) PCs. Microsoft, Illinois container data center (~450k servers). A lot of data Internet Archive: 2 PB + 20 TB/month. Google handles 20 PB/day (2008). CERN s LHC generates PB/year.

6 The cloud for the end-user Utility computing: I just want to rent CPU cycles. Example: Amazon EC2. Platform as a Service (PaaS): Give me an API and I ll handle the implementation. Example: Google App Engine. Software as a Service(SaaS): I want a finished application available off-site. Example: Gmail. Syllabus (1) Introduction to Cloud Computing (CC): context, concepts, examples, challenges. Grid Computing: concepts, case studies and standards. Security and federation in Grid computing. Edge computing, e.g. Akamai. Cyclesharing, e.g. BOINC. Utility computing, e.g. Amazon Elastic Clouds, Google Apps. Programming models, e.g. Map/Reduce, Hadoop. Storage Mechanisms, e.g. Simple DB, BigTable.

7 Syllabus (2) Virtualization: concepts, system level virtual machines and case studies, e.g Xen. Security in CC. Resource management in CC: scheduling, migration, payment models, SLAs, market mechanisms. Autonomic Computing (self-*): adaptability (self-configuration), fault tolerance (self-healing). Security, economic efficiency, and environmental and energy sustainability. Case studies: cloud computing choices and design. Lab Assignments 1. Condor. 2. Map Reduce. 3. Google App Engine. 4. Microsoft Azure.

8 Some papers. Reading Material Slides from class (Unfortunately...). Chapters from: Grid 2: Blueprint for a new computing generation, Elsevier. Programming AWS, O Reilly. Hadoop, O Reilly. Assessment Exam: 50% Class material + Papers (some can be taken to the exam). Minimum grade: 9 out of 20. Project: 50% Minimum grade: 10 out of 20. Late submissions are penalized by 1 to 1,5 points/day. Project checkpoint: 15% of the project grade if higher than final grade. I cannot guarantee that the project grade will be kept for next year. Student-workers: the same. Época Especial : 60% exam + 40% adapted project

9 Project Probably a Cloud platform app accessing a Map Reduce app. You can present your own proposal! Important Dates Oct. 4 : Project handout. Nov. 11 : Project checkpoint. Dec. 9 : Project submission. Dec : Project discussion. Jan 12 : 1st Exam. Jan 30 : 2nd Exam.