Distributed programming on the cloud
Learn about how complex computer programs must be architected for the cloud by using distributed programming.
In this learning path, you'll:
- Classify programs as sequential, concurrent, parallel, and distributed
- Indicate why programmers usually parallelize sequential programs
- Define distributed programming models
- Discuss the challenges with scalability, communication, heterogeneity, synchronization, fault tolerance, and scheduling that are encountered when building cloud programs
- Define heterogeneous and homogenous clouds, and identify the main reasons for heterogeneity in the cloud
- List the main challenges that heterogeneity poses on distributed programs, and outline some strategies for how to address such challenges
- State when and why synchronization is required in the cloud
- Identify the main technique that can be used to tolerate faults in clouds
- Outline the difference between task scheduling and job scheduling
- Explain how heterogeneity and locality can influence task schedulers
In partnership with Dr. Majd Sakr and Carnegie Mellon University.
Prerequisites
- Understand what cloud computing is, including cloud service models and common cloud providers
- Know the technologies that enable cloud computing
- Understand how cloud service providers pay for and bill for the cloud
- Know what datacenters are and why they exist
- Know how datacenters are set up, powered, and provisioned
- Understand how cloud resources are provisioned and metered
- Be familiar with the concept of virtualization
- Know the different types of virtualization
- Understand CPU virtualization
- Understand memory virtualization
- Understand I/O virtualization
- Know about the different types of data and how they're stored
- Be familiar with distributed file systems and how they work
- Be familiar with NoSQL databases and object storage, and how they work
Achievement Code
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Modules in this learning path
Learn about distributed programming and why it's useful for the cloud, including programming models, types of parallelism, and symmetrical vs. asymmetrical architecture.
MapReduce was a breakthrough in big data processing that has become mainstream and been improved upon significantly. Learn about how MapReduce works.
GraphLab is a big data tool developed by Carnegie Mellon University to help with data mining. Learn about how GraphLab works and why it's useful.
Spark is an open-source cluster-computing framework with different strengths than MapReduce has. Learn about how Spark works.
The increase of available data has led to the rise of continuous streams of real-time data to process. Learn about different systems and techniques for consuming and processing real-time data streams.
