Google Recently Revealed Principle of Implementing Machine Learning on Data Center. But, What is Relationship With Software Defined Data Center or is Machine Learning and Data Center Automation inferior option? First thing is, one should understand the basics of Machine Learning. Another quite widely used method for simple automation on consumer electronics is Fuzzy Logic. Fuzzy Logic and human reasoning has a huge difference. Another related thing is Collective Intelligence.
Practical fact is that – Google uses Multi Agent System (MAS) for several years – all bots, algorithm are part of it. An interesting but a bit unrelated good read is about Robobees. The system presented by Google applies the modus operandi of the human brain to manage and monitor the data center. Software Defined Data Center serves some practical purposes. Automation in the Cloud is nothing quite new.
Machine Learning and Data Center Automation : Google Loves to Tell Sci-Fi Story
There are various reason behind Google’s whitepaper on Machine Learning and Data Center Automation. Everyone knows the possible relationship with Heartbleed and NSA’s Spyware Activities.
First, a disaster management for reputation is required by Google. Secondly, Google hugely loves to tell or package sci-fi stories – we talked about SixthSense. This is our practical logical view and now we are coming to the part what Google is saying. The next step in the management and monitoring of the data center, during the recent Data Centre meet in Europe, Google has indeed shown an unprecedented system to analyze the data received from various web farm through the use of machine learning and artificial neural network. These terminologies are enough to make most fearful.
In a nutshell, Mountain View has developed a computer system capable of knowing more detail of skilled technicians of the various data centers. The neural network, as it has been defined to the press, is able to implement the best practices.
Machine Learning and Data Center Automation : Google SEO Still is Backlinking
Jim Gao of Google has begin working with the simulation / processing, in his spare time, which is a research and innovation to which a Google employee dedicated a part of their workday. The skilled engineer was able to include in the model a large quantity of variables, the energy needs of the infrastructure up to the climatic conditions and workloads – the end result, after a difficult period of adjustment and trial and error, it allowed Jim to predict with accuracy PUE (Power Usage Effectiveness, energy used) of 99.66% correct of the structures. Joe Kawa Gao has done a specific course at the Stanford University related to AI.
As understandable from various other articles related to the theories, it is practically sophisticated automation – nothing more or less.