Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing.
Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more!
Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required.
The course also features interviews with leading researchers and managers, from both industry and academia.
This course builds on the material covered in the Cloud Computing Concepts, Part 1 course.
Este curso forma parte de Programa Especializado - Cloud Computing
Cloud Computing Concepts: Part 2
ofrecido por
Acerca de este Curso
4.6
190 calificaciones
Habilidades que obtendrás
Distributed ComputingC++Cloud ComputingNoSQL
Programa - Qué aprenderás en este curso
Semana
1Week 1: Course Orientation and Classical Distributed Algorithms Continued
Lesson 1: To coordinate machines in a distributed system, this module first looks at classical algorithms for electing a leader, including the Ring algorithm and Bully algorithm. We also cover how Google’s Chubby and Apache Zookeeper solve leader election. Lesson 2: This module covers solutions to the problem of mutual exclusion, which is important for correctness in distributed systems with shared resources. We cover classical algorithms, including Ricart-Agrawala’s algorithm and Maekawa’s algorithm. We also cover Google’s Chubby support for mutual exclusion.
11 videos (Total 122 minutos), 7 readings, 2 quizzes
11 videos
Orientation Towards Cloud Computing Concepts: Some Basic Computer Science Fundamentals23m
Week 1 Introduction1m
1.1. The Election Problem8m
1.2. Ring Leader Election14m
1.3. Election in Chubby and ZooKeeper9m
1.4. Bully Algorithm9m
2.1. Introduction and Basics12m
2.2. Distributed Mutual Exclusion10m
2.3. Ricart-Agrawala's Algorithm11m
2.4. Maekawa's Algorithm and Wrap-Up16m
7 lecturas
Orientation Overview10m
Syllabus10m
About the Discussion Forums10m
Course Learning Community and Social Media10m
Week 1 Overview10m
Homework 1 Instructions10m
Programming Assignment Instructions10m
2 ejercicios de práctica
Orientation Quiz10m
Homework 120m
Semana
2Week 2: Concurrency and Replication Control
Lesson 1: Transactions are an important component of many cloud systems today. This module presents building blocks to ensure transactions work as intended, from Remote Procedure Calls (RPCs), to serial equivalence for transactions, to optimistic and pessimistic approaches to concurrency control, to deadlock avoidance/prevention. Lesson 2: This module covers how replication – maintaining copies of the same data at different locations – is used to provide many nines of availability in distributed systems, as well as different techniques for replication and for ensuring transactions commit correctly in spite of replication.
8 videos (Total 95 minutos), 2 readings, 1 quiz
8 videos
1.1. RPCs17m
1.2. Transactions13m
1.3. Serial Equivalence12m
1.4. Pessimistic Concurrency13m
1.5. Optimistic Concurrency Control9m
2.1. Replication14m
2.2. Two-Phase Commit11m
2 lecturas
Week 2 Overview10m
Homework 2 Instructions10m
1 ejercicio de práctica
Homework 230m
Semana
3Week 3: Emerging Paradigms
Lesson 1: We study the emerging area of stream processing, touching on key design aspects of Apache Storm. Lesson 2: We study how enormous graphs can be processed in clouds. Lesson 3: We study various types of networks/graphs that are both natural and artificial, and their surprising commonalities. Lesson 4: This module presents classical scheduling algorithms that have been used in operating systems since the inception of computers. We then cover two popular scheduling algorithms for Hadoop.
9 videos (Total 110 minutos), 2 readings, 1 quiz
9 videos
Stream Processing in Storm15m
Distributed Graph Processing16m
Structure of Networks21m
4.1. Single-processor Scheduling11m
4.2. Hadoop Scheduling11m
4.3. Dominant-Resource Fair Scheduling8m
Storm Demo 9m
Apache Spark by Faria Kalim12m
2 lecturas
Week 3 Overview10m
Homework 3 Instructions10m
1 ejercicio de práctica
Homework 330m
Semana
4Week 4: Classical Systems
Lesson 1: When files and directories are stored/accessed over the network, it is called a distributed file system. This module covers the working of distributed file systems like NFS and AFS. Lesson 2: This module covers Distributed Shared Memory systems, their techniques, and pros/cons. Lesson 3: This module looks at the area of sensor networks, starting from what’s inside a sensor mote and how networks of them work.
6 videos (Total 109 minutos), 2 readings, 1 quiz
6 videos
1.1. File System Abstraction23m
1.2. NFS and AFS14m
Distributed Shared Memory17m
Sensor and Their Networks30m
Interview with Brighten Godfrey23m
2 lecturas
Week 4 Overview10m
Homework 4 Instructions10m
1 ejercicio de práctica
Homework 420m
Principales revisiones
por MR•Jul 16th 2017
Great course, I would recommend to everyone who wants to understand the basics of cloud computing. The course material is excellent, the instructor Indy is phenomenal and the exams are marvelous.
por AH•Jan 1st 2018
Very nice and informative course, and the programming assignment in the end of the course is so helpful to understand and apply the theoretical concepts that was introduced during the course
Instructor
Indranil Gupta
ProfessorDepartment of Computer Science
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Este curso es parte del Master in Computer Science completamente en línea de University of Illinois at Urbana-Champaign.
Si eres aceptado en el programa completo, tus cursos cuentan para tu título.
Acerca de University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Acerca del programa especializado Cloud Computing
The Cloud Computing Specialization takes you on a tour through cloud computing systems. We start in in the middle layer with Cloud Computing Concepts covering core distributed systems concepts used inside clouds, move to the upper layer of Cloud Applications and finally to the lower layer of Cloud Networking. We conclude with a project that allows you to apply the skills you've learned throughout the courses.
The first four courses in this Specialization form the lecture component of courses in our online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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