DistOS 2015W Session 9

BONIC

• Public Resource Computing Platform
• Gives scientists the ability to use large amounts of computation resources.
• The clients do not connect directly with each other but instead they talk to a central server located at Berkley
• The goals of Bonic are:

• 1) reduce the barriers of entry
• 2) Share resources among autonomous projects
• 3) Support diverse applications
• 4) Reward participants.

A BONIC application can be identified by a single master URL, 
which serves as the homepage as well as the directory of the servers.

SETI@Home

• Uses public resource computing to analyze radio signals to find extraterrestrial intelligence
• Need good quality telescope to search for radio signals, and lots of computational power, which was unavailable locally
• It has not yet found extraterrestrial intelligence, but its has established credibility of public resource computing projects which are given by the public
• Uses BONIC as a backbone for the project
• Uses relational database to store information on a large scale, further it uses a multi-threaded server to distribute work to clients

MapReduce

• A programming model presented by Google to do large scale parallel computations
• Uses the Map() and Reduce() functions from functional style programming languages

• Map (Filtering)

• Takes a function and applies it to all elements of the given data set

• Reduce (Summary)

• Accumulates results from the data set using a given function

Naiad

• A programming model similar to MapReduce but with streaming capabilities so that data results are almost instantaneous
• A distributed system for executing data parallel cyclic dataflow programs offering high throughput and low latency
• Aims to provide a general purpose system which will fulfill the requirements and the will also support wide variety of high level programming models.
• Real Time Applications:

• Batch iterative Machine Learning:

VW, an open source distributed machine learning performs iteration in 3 phases: each process updates local state; processes independently training on local data; and process jointly performed global average which is All Reduce.