Close to the Edge: No Data Center Needed Computing

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Written by Douglas Eadline

Published: 22 May 2019

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Introduction

Welcome to a new series on ClusterMonkey! While the news and articles have been a bit sparse lately, it is not because the head monkey has been idle. Indeed, there is so much to write about and so little time. Another issue we ran into was how to present all the recent projects that may seem rather disparate with an easy-to-understand overriding theme. Welcome to edge computing.

Defining edge computing has become tricky because it now has a marketing buzz associated with it. Thus, like many over-hyped technology topics, it may take on several forms and have some core aspects that allow it to be treated as a "thing."

In this series, the definition of edge is going to be as specific as possible. In general, edge computing is that which does not take place in the data center or the cloud (hint: the cloud is a data center). Such a definition is too broad, however, since computing is everywhere (from cell phones to actual desktop workstations). A more precise definition of edge computing can be written as:

Data center level computing that happens outside of the physical data center or cloud.

That definition seems to eliminate many smaller forms of computing but still is a little gray in terms of "data center level computing." This category of computing usually operates 24/7 and provides a significantly higher level of performance and storage than mass-marketed personal systems.

Sledgehammer HPC

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Written by Douglas Eadline

Published: 05 August 2017

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HPC without coding in MPI is possible, but only if your problem fits into one of several high level frameworks.

[Note: The following updated article was originally published in Linux Magazine in June 2009. The background presented in this article has recently become relevant due to the resurgence of things like genetic algorithms and the rapid growth of MapReduce (Hadoop) . It does not cover deep learning.]

Not all HPC applications are created in the same way. There are applications like Gromacs, Amber, OpenFoam, etc. that allow domain specialist to input their problem into an HPC framework. Although there is some work required to "get the problem into the application", these are really application specific solutions that do not require the end user to write a program. At the other end of the spectrum are the user written applications. The starting points for these problems include a compiler (C/C++ or Fortran), an MPI library, and other programming tools. The work involved can range form small to large as the user must concern themselves with the "parallel aspects of the problem". Note: all application software started out at this point some time in the past.

Big Data In Little Spaces: Hadoop And Spark At The Edge

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Written by Administrator

Published: 25 April 2019

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Ever wonder what Edge computing is all about? Data happens and information takes work. Estimates are that by 2020, 1.7 megabytes of new data will be created every second for every person in the world. That is a lot of raw data.

Two questions come to mind. What are we going to do with it and where we going to keep it. Big Data is often described by the three Vs – Volume, Velocity, and Variability – and note not all three need apply. What is missing is the letter “U” which stands for Usability. A Data Scientist will first ask, how much of my data is usable? Data usability can take several forms and include things like quality (is it noisy, incomplete, accurate) and pertinence (is there any extraneous information that will not make a difference to my analysis). There is also the issue of timeliness. Is there a “use by” date for the analysis or might the data be needed in the future for some as of yet unknown reason. The usability component is hugely important and often determines the size of any scalable analytics solution. Usable data is not the same as raw data.

Get the full article at The Next Platform. You may recognize the author.

Answering The Nagging Apache Hadoop/Spark Question

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Written by Douglas Eadline

Published: 30 November 2016

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(or How to Avoid the Trough of Disillusionment)

A recent blog post, Why not so Hadoop?, is worth reading if you are interested in big data analytics, Hadoop, Spark, and all that. The article contains the 2015 Gartner Hype Cycle. The 2016 version is worth examining as well. Some points similar to the blog can be made here:

1. Big data was at the "Trough of Disillusionment" stage in 2014, but is not seen in the 2015/16 Hype cycle.
2. The "Internet of Things" (a technology that is expected to fill the big data pipeline) was on the peak for two years and now has been given "platform status."