Carnegie mellon university
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15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
15-319/15619: CLOUD COMPUTING COURSE DESCRIPTION & SYLLABUS CARNEGIE MELLON UNIVERSITY SPRING 2017
1. OVERVIEW Title: Cloud Computing Units: 15-319 is 9 units and 15-619 is 12 units. Pre-requisites for undergraduate students: A “C” or better in 15-213. Pre-requisites for graduate students: Knowledge of computer systems, programming and debugging, with a strong competency in at least one language (such as Java/Python), and the ability to pick up other languages as needed.
http://oli.cmu.edu (accessed through https://blackboard.andrew.cmu.edu ) The Project Zone: https://TheProject.Zone Piazza: http://piazza.com/cmu/spring2017/1531915619/home
msakr@cs.cmu.edu
GHC 7006, +1-412-268-1161 Office hours: Tuesday, 3-4pm (Pittsburgh)
TAs in Pittsburgh typically hold office hours in GHC 5 th Floor
Teaching Commons. The TA office hours are posted on Piazza:
Haokang An (Marshall)
Anthony Corletti
Cameron Dashti
Lu Jiang
Mrigesh Kalvani
Yibai Li
Zebing Lin
Jialing Liu
Yifang Liu
Ziyi Liu
Siyao Lyu
Shardul Mahadik
Yuhan Mao
Siyao Meng (Scott)
Imre Nagi
Joshua Ocero
Yiqun Ouyang
Quan Quan (Bill)
Prasoon Telang
Hochuen Wong
Tianhe Wu
Mengtao Yang
Yin Yi (Elizabeth)
Fengnan Yue
Yang Zhou (yangzhou)
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
2. COURSE DESCRIPTION This project-based on-line course focuses on skill building across various aspects of cloud computing. We cover conceptual topics and provide hands-on experience through projects utilizing public cloud infrastructures (Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP)). The adoption of cloud computing services continues to grow across a variety of organizations and in many domains. Simply, cloud computing is the delivery of computing as a service over a network, whereby distributed resources and services are rented, rather than owned, by an end user as a utility. Conceptually, the course will introduce this domain and cover the topics of cloud infrastructures, virtualization, software defined networks and storage, cloud storage, and programming models (analytics frameworks). As an introduction, we will discuss the motivating factors, benefits and challenges of the cloud, as well as service models, service level agreements (SLAs), security, example cloud service providers and use cases. Modern data centers enable many of the economic and technological benefits of the cloud paradigm; hence, we will describe several concepts behind data center design and management and software deployment. Next, we will focus on virtualization as a key cloud technique for offering software, computation and storage services. We will study how CPU, memory and I/O resources are virtualized, with examples from Xen and VMWare, and present real use cases such as Amazon EC2. Within the same theme of virtualization, students will also be introduced to Software Defined Networks and Storage (SDN and SDS). Subsequently, students will learn about different cloud storage concepts including data distribution, durability, consistency and redundancy. We will discuss distributed file systems, NoSQL databases and object storage. HDFS, CephFS, HBASE, MongoDB, Cassandra, DynamoDB, S3, Swift and Ceph Object Gateway will be presented as case studies. Finally, students will learn the details of the MapReduce programming model and gain a broad overview of the Spark, GraphLab programming models as well as message queues (Kafka) and stream processing (Samza). For the projects, students will work with Amazon Web Services, Microsoft Azure and Google Cloud Platform, use them to rent and provision compute resources and then program and deploy applications that run on these resources. Students will develop and evaluate virtual machine (VM) and container scaling, elasticity and load balancing solutions. In addition, students will work with cloud storage systems and learn to develop different applications using batch, iterative and stream processing frameworks. 15-619 students will have to complete an extra project which entails designing and implementing a complete web-service solution for querying big data. For the extra project, the student teams are evaluated based on the cost and performance of their web service. 3.
In this on-line course we plan to give students an overview of the field of Cloud Computing, and an in-depth study into its enabling technologies and main building blocks. Students will gain hands-on experience solving relevant problems through projects that will utilize existing public cloud tools. It is our objective that students will develop the skills needed to become a practitioner or carry out research projects in this domain. Specifically, the course has the following objectives: Students will learn 1)
benefits, as well as current and future challenges; 2)
the basic ideas and principles in data center design; cloud management techniques and cloud software deployment considerations; 3)
and storage services on the cloud; Software Defined Networks (SDN) and Software Defined Storage (SDS);
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
4) cloud storage technologies and relevant distributed file systems, NoSQL databases and object storage; 5)
4.
The primary learning outcomes of this course are five-fold. Students will be able to: 1)
Explain the core concepts of the cloud computing paradigm: how and why this paradigm shift came about, the characteristics, advantages and challenges brought about by the various models and services in cloud computing. 2)
Apply fundamental concepts in cloud infrastructures to understand the tradeoffs in power, efficiency and cost, and then study how to leverage and manage single and multiple datacenters to build and deploy cloud applications that are resilient, elastic and cost-efficient. 3)
Discuss system, network and storage virtualization and outline their role in enabling the cloud computing system model. 4)
such as Amazon S3 and HDFS. 5)
Analyze various cloud programming models and apply them to solve problems on the cloud. 4.1.
BASIC CONCEPTS This module will provide a broad overview of cloud computing, its history, technology overview, benefits, risks and the economic motivation for it. Upon completion of this module, students will be able to: 4.1.1. Explain the concept of “cloud computing”. 4.1.2. Briefly recall the recent history of cloud computing, illustrating its motivation and evolution. 4.1.3. List some of the enabling technologies in cloud computing and discuss their significance. 4.1.4. Discuss some of the advantages and disadvantages of the cloud paradigm. 4.1.5. Articulate the economic benefits as well as issues/risks of the cloud paradigm for businesses as well as cloud providers. 4.1.6. Associate the various layers in the cloud building blocks and differentiate cloud service models. 4.1.7. Define SLAs and SLOs and illustrate their importance in Cloud Computing. 4.1.8. Enumerate and explain various threats in cloud security. 4.1.9. List some of the common cloud providers and their associated cloud stacks and recall popular cloud use case scenarios. 4.2.
CLOUD INFRASTRUCTURE This module will provide a historical overview of data centers, along with design considerations. Students will learn to apply methods to evaluate data centers, cloud management techniques and software deployment considerations. Upon completion of this module, students will be able to:
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
Understand the implications of building a multi-tier cloud application to achieve resiliency and elasticity, and the latency implications of such applications. 4.2.9. Recall various cloud pricing models and their applicability to various business use cases. 4.2.10. Recall and describe cloud management techniques such as middleware, resource provisioning, metering, and orchestration. 4.2.11. Describe and evaluate different cloud software deployment considerations such as scaling strategies, load balancing, fault tolerance, accounting for tail latencies and optimizing for cost. 4.3.
CLOUD RESOURCE MANAGEMENT Students will learn how virtualization can allow software and hardware images (e.g., virtual machines) to run side-by-side on a single cloud data center while provided security, resource and failure isolations. They will understand how virtualization enables clouds to offer software, computation, and storage as services as well as attain agility and elasticity properties. We will discuss resource virtualization in detail and present multiple examples from Xen and VMware. Finally, we will present a real use case such as Amazon EC2. After finishing this unit students will be able to:
4.4.
CLOUD STORAGE This module will provide a broad overview of storage technologies and concepts of cloud storage. It will also provide a detailed study of Amazon S3, EBS and distributed file systems and databases. Students will be able to:
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
Compare and contrast different types of file systems and discuss their design considerations. Compare and contrast Hadoop Distributed File System (HDFS) with Ceph File System (CephFS). 4.4.5. Compare and contrast different types of databases and discuss their design tradeoffs. 4.4.6. Discuss the concepts of cloud object storage. Enumerate the different types of block devices used in data storage. 4.5.
PROGRAMMING MODELS Students will be given an overview on a variety of cloud-based programming models. Students will understand the benefits and limitations of each so that they can assess applicability based on the problem domain. Students will gain working experience in one of these programming models. Upon completion of this module students will be able to: 4.5.1. Explain the fundamental aspects of parallel and distributed programming models. 4.5.2. Explain the main execution flow, scheduling and fault tolerance concepts in the MapReduce programming model. 4.5.3. Recall and contrast different cloud programming models (MapReduce, Spark, GraphLab, Spark Streaming and Samza). 5.
COURSE ORGANIZATION Your participation in the course will involve several forms of activity: 1.
2.
Completing the unscored inline activities for each unit (Review activities on OLI). 3.
Completing the graded checkpoint weekly quizzes after each unit. 4.
Complete projects which are performed on the cloud and submitted through TheProject.Zone. 5.
AssessMe AssessMents, unscored short quizzes to unlock subsequent project sections. 6.
Complete a team project on building a complete web service. Students should regularly check OLI to see when new content or checkpoint quizzes are made available. Projects and Checkpoint quizzes must be completed by the due dates posted on TheProject.Zone. 6.
Students are encouraged to ask questions about content and projects through Piazza, where an online class portal has been created for this course. The course link for Piazza is:
http://
piazza.com/cmu/spring2017/1531915619/home . There is video recording of a weekly recitation in Pittsburgh which is made available to all students. The teaching staff will discuss any major questions that have been posted to Piazza or by email. For urgent communication with the teaching staff, it is best to post on Piazza and then send email. We will use the course website as the basic portal for the class. The course content is entirely on OLI. The project write-ups, submission, scoreboard and grades are on TheProject.Zone. The checkpoint quizzes are on OLI. OLI can be reached through Blackboard. Announcements, discussions and questions are posted on Piazza.
7.
POLICIES WORKING ALONE ON PROJECTS 15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
Projects that are assigned to single students should be performed individually. HANDING IN PROJECTS All assessments are due at 11:59 PM EST (one minute before midnight) on the due dates specified on OLI or TheProject.Zone. All hand-ins are electronic, and use the OLI Checkpoint system and TheProject.Zone. APPEALING GRADES After each project module is graded, you have seven calendar days to appeal your grade. All your appeals should be provided by email to Prof. Sakr. 8.
Inline activities (“Learn by Doing” and “Did I Get This”), which are available in most pages in the OLI course, are simple, non-graded activities to assess your comprehension of the material as you read through the course material. You are advised to complete all of the inline activities before proceeding through to the next page or module. If you missed many of the activities, it is recommended that you review the material again. There are five units consisting of modules of content on OLI, each week has a Checkpoint Quiz that you must complete before the deadline posted on OLI. Each weekly Checkpoint Quiz will be worth ~2% of your total grade. It is your responsibility to ensure that the quiz is submitted prior to the deadline. You will have only a single attempt to complete each Checkpoint Quiz on OLI. This course includes four individual projects. Each individual project consists of several project modules. Every week, a project module has to be completed based on the deadlines posted on TheProject.Zone. The write-up required to complete each project module is available on TheProject.Zone. Each module has a submission process that is specific to the project module that is due. It is the students’ responsibility to make sure that all project work is completed and that the project module is submitted prior to the deadline. Students typically have multiple attempts to submit the project module on TheProject.Zone. 15-619 students have to complete a team-based multi-week project in parallel to the weekly Project Modules.
Content Checkpoint Quizzes
12
20% Projects
4 (15-319) 5 (15-619) 80%
Total Grade 100% 9.
CHEATING We urge each student to carefully read the university policy on academic integrity , which outlines the policy on cheating, plagiarism or unauthorized assistance. It is the responsibility of each student to produce her/his own original academic work. Collaboration or assistance on academic work to be graded is not permitted unless explicitly authorized by the course instructor. Each unit checkpoint quiz or project module submitted must be the sole work of the student turning it in. Student work on the cloud is logged, submitted work will be closely monitored by automatic cheat checkers, and students may be asked to explain any suspicious similarities with any piece of code available. The following are guidelines on what collaboration is authorized and what is not: WHAT IS CHEATING?
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
Sharing code or other electronic files either by copying, retyping, looking at, or supplying a copy of any file. Copying any code from the internet (stackoverflow.com or github or others).
Copying answers to any checkpoint quiz from another individual, published or unpublished written sources, and electronic sources.
Collaborating with another student or another individual on checkpoint quizzes or project modules. 4. Sharing written work, looking at, copying, or supplying work from another individual, published or unpublished written sources, and electronic sources.
Collaboration in team projects is strictly limited to the members of the team. WHAT IS NOT CHEATING?
Clarifying ambiguities or vague points in class handouts. 2. Helping others use computer systems, networks, compilers, debuggers, profilers, or system facilities. 3. Helping others with high-level design issues. 4. Guiding others through code debugging but not debugging for them. Cheating in projects will also be strictly monitored and penalized. Be aware of what constitutes cheating (and what does not) while interacting with students. You cannot share or use written code, and other electronic files from students. If you are unsure, ask the teaching staff. Be sure to store your work in protected directories. The penalty for cheating is severe, and might jeopardize your career – cheating is simply not worth the trouble. By cheating in the course, you are cheating yourself; the worst outcome of cheating is missing an opportunity to learn. In addition, you will be removed from the course with a failing grade. We also place a record of the incident in the student’s permanent record. 10.
CONCEPTUAL TOPICS The course content will be structured into the following units: Unit # Title Modules and Description 1
Definition and evolution of Cloud Computing Enabling Technologies, Service and Deployment Models Popular Cloud Stacks and Use Cases Benefits, Risks, and Challenges of Cloud Computing Economic Models and SLAs Topics in Cloud Security 2
Historical Perspective of Data Centers Datacenter Components: IT Equipment and Facilities Design Considerations: Requirements, Power, Efficiency, & Redundancy Power Calculations, PUE and Challenges in Cloud Data Centers Cloud Management and Cloud Software Deployment Considerations 3
Virtualization (CPU, Memory, I/O), Case Study: Amazon EC2 Software Defined Networks (SDN) Software Defined Storage (SDS) 4
Introduction to Storage Systems Cloud Storage Concepts Distributed File Systems (HDFS, Ceph FS) Cloud Databases (HBase, MongoDB, Cassandra, DynamoDB) Cloud Object Storage (Amazon S3, OpenStack Swift, Ceph) 6
Distributed Programming for the Cloud Data-Parallel Analytics with Hadoop MapReduce (YARN); Iterative Data- Parallel Iterative Analytics (Spark); Graph-Parallel Analytics with GraphLab 2.0 (PowerGraph); Stream Processing (Samza)
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
11. PROJECTS The programming projects in this course will be geared towards providing hands-on experience with various cloud technologies. Students will learn to develop all projects using various public cloud services (primarily AWS and some work on Azure and GCP). Students will be given a budget for cloud resources for each project and are expected to work within the budget otherwise, they risk being penalized. 11.1.
PROJECT 1: BIG DATA ANALYSIS Students will work with Amazon AWS and provision their first compute resources. Students will setup AWS accounts, work with provisioning management software and launch instances on Amazon EC2. Students will learn the benefits and tradeoffs of running programs in parallel, using AWS EMR or Azure HDInsight, versus sequential on a large dataset. Students will have to solve a problem using resources provisioned in AWS and Azure within particular cost constraints. 11.2.
PROJECT 2: CLOUD ELASTICITY In this project, students will learn about cloud elasticity through virtual machines and containers. Students will be first tasked with developing their own elastic services for a dynamically changing load scenario using AWS, Azure and GCP APIs. Students will then work with the Load Balancing and Auto Scaling services on AWS to mitigate varying loads on the server. Furthermore, students will build a hands-on in-browser programming web service using Docker Containers and Kubernetes on the AWS, Azure and GCP cloud platforms. 11.3.
Using AWS resources, students will work on cloud storage technologies to evaluate their capabilities and limitations, each week with a new workload and a storage system. Students begin by exploring the limitations of traditional filesystems, and then compare them to relational databases (MySQL) and NoSQL databases (HBase). Next, students will build a social network timeline using heterogeneous back-end storage systems. The project will cover several storage systems, including low-latency KV stores, NoSQL databases, and in-memory databases (examples include Apache HBase, Amazon RDS, MongoDB and others). Finally, students explore sharding and replication of a simple key-value store while implementing strong consistency for geo-replicated key-value stores. 11.4.
In this project, students will work on developing applications using the MapReduce, Spark, and Samza frameworks to experience batch, iterative, graph and stream processing. Students will write their own MapReduce code using Apache Hadoop and provision instances on Amazon EC2 to run them in order to build their own input text predictor, similar to Google Instant . Students will build the input text predictor from a large text corpus by generating a list of n-grams, building a statistical language model using the n- grams, and creating a user interface. Students will also be introduced to iterative programming models by implementing a social graph analysis algorithm on Apache Spark. Finally, students will learn to deal with streaming data to perform real-time processing of multiple data streams using Apache Kafka/Samza.
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
11.5. 15-619 TEAM PROJECT: TWITTER ANALYTICS WEB SERVICE Students will work in teams to design and implement a complete web-service that uses the REST interface to respond to queries that require running an analytics job on a large (1.2TB) Twitter data set which is stored in a database (MySQL, HBASE, etc.). In this team project, student teams are expected to use different tools and services to achieve build a performing web-service that meets the requirements. The students' web-services are evaluated through a load generator for a fixed time period (several hours) by measuring the cost of cloud resources used and their system’s performance (throughput). There is an upper bound on the budget which could cause students to be disqualified. Students are evaluated based on how their service performs compared to a baseline. 12.
The tentative schedule is as follows (specific deadlines are posted on OLI and TheProject.Zone): Week Monday OLI Content Individual Projects Team Project Quizzes 1 1/16/2017 Unit 1, Module 1, 2 Primers/P0 (Jan 22) Q0 (Ac. Integ.) 2 1/23/2017 Unit 1, Module 1, 2 P1.1 (Jan 29)
Q1 (Jan 27) 3 1/30/2017 Unit 2, Module 3, 4 P1.2 (Feb 5)
Q2 (Feb 3) 4 2/6/2017 Unit 2, Module 5, 6 P2.1 (Feb 12)
Q3 (Feb 10) 5 2/13/2017 Unit 3, Module 7, 8, 9 P2.2 (Feb 19)
Q4 (Feb 17) 6 2/20/2017 Unit 3, Module 10, 11, 12 P3.1 (Feb 26)
Q5 (Feb 24) 7 2/27/2017 Unit 3, Module 13 P3.2 (Mar 5) Project Out (Feb 27) Q6 (Mar 3) 8 3/6/2017 Unit 4, Module 14
Q7 (Mar 9) 9 3/13/2017 Spring Break
10 3/20/2017 Unit 4, Module 15 P3.3 (Mar 26)
Q8 (Mar 24) 11 3/27/2017 Unit 4, Module 16, 17
Phase 1 Due (Apr 2) Q9 (Mar 31) 12
4/3/2017 Unit 5, Module 18 P4.1 (Apr 9)
Q10 (Apr 7) 13 4/10/2017 Unit 5, Module 19, 20
Q11 (Apr 14)
14
4/17/2017 Unit 5, Module 21, 22 P4.2 (Apr 23)
4/24/2017
Phase 3 Due (Apr 30) Q12 (Apr 28) 16 5/1/2017 P4.3 (May 5)
15-319/15-619 Syllabus, v68, MFS, 22 Jan2017
13.
TAKE CARE OF YOURSELF Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful. If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/ . Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
CaPS: 412-268-2922 Re:solve Crisis Network: 888-796-8226 If the situation is life threatening, call the police:
If you have questions about this or your coursework, please let me know. Yüklə 119,99 Kb. Dostları ilə paylaş:
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