Smart Machine and Smart Insight are the terms coined by DataRPM – a cognitive and self-service Business Intelligence company. They call it smart machine because the machine learns the behavior of customer using artificial intelligence machine learning algorithms. yes, it is smart machine – smart machine on cloud.
Cloud computing has cluster of machines which stores and computes petabyte of data. But as it suggests all the data has to come to cloud for computing, which has its own pros and cons. With big data technology and virtualization, cloud was natural choice.
Then comes Fog Computing ; I guess a term coined by Cisco, where calculation and computing is done at router, end point or last mile level. Argument was, fog is a layer far below cloud and it is far thinner than cloud. So router, end points and last mile works as fog computers and primary has smart algorithms for distribution and load balancing of data. Fog is homogeneous too. As wifi getting ubiquitous, fog computing is natural choice.
As IoT (Internet of Things) is gaining acceptance, billions of devices and sensor will be in fields talking to each other. They will need very real time smartness – and I call it Dew Computing.
” Like Dew it will be at ground zero, condensed and all over the place. Dew Computing will make each machines , smart machine to solve the problems of IoT scale.”
Characteristics of Dew Computing :
a.) Smart software of in size of KBs : These smart machines will work in very low RAM – in the range of 512 KB. So the images of the software will be in low KBs so that it can be loaded into RAM of these probes, devices or sensors.
b.) Compression logic optimized for time series : Today’s big data compression logic is optimized for full data compression. Smart machines will not have storage to have complete data set.They will have compression logic optimized for time series, where only delta is stored and algorithm will know how to rebuild itself, if required.
c.) Self Regulatory : Smart machines will have their threshold preset or will have algorithms to adjust. If values reaches threshold, smart machine will exception itself and give reason for error. It will save time as otherwise data analytic has to find outlier and then mechanic to find out reason for failure.
I think future smart machines will look like above as dew spread over field. It is likeDew Computing.
Your thoughts !!
Author : Vivek Singh is contributor to world’s first open source data quality tool and data preparation tool http://sourceforge.net/projects/dataquality/ .