While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads.
In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the public cloud best suits your organization, and what the future holds for operations and infrastructure engineers in a post-container world. Is a serverless world inevitable?| By Omar Sultan | Article Rating: |
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| November 3, 2008 10:00 PM EST | Reads: |
40,455 |
Omar Sultan's Blog
I was chatting with a customer the other day who was struggling with some of the implications of cloud computing. The analogy that finally made sense to them is what I will call 'cloud
dining.' I am the cook in the house and I am tasked with feeding the family. If my 10-year old is lobbying for Italian, I am cook at home or order out. The decision may also vary from day to day. For instance, I might not have all the ingredients and have to order out, or, like this weekend, it may be 103 outside and cooking at home is not all that appealing. Now, the same can be said for supporting a given application in a cloud computing environment.
In a fully implemented Data Center 3.0 environment, you can decide if an app is run locally (cook at home), in someone else’s data center (take-out) and you can change your mind on the fly in case you are short on data center resources (pantry is empty) or you having environmental/facilities issues (too hot to cook). In fact, with automation, a lot of this can can be done with policy and real-time triggers. For example, during month end processing, you might always shift non-critical apps offsite, or if you pass a certain cooling threshold, you might ship certain processing offsite.
James Gardner had an interesting post about this, which got me thinking. What if you could start comparing the cost of running a workload and handle it wherever it is most cost-effective: energy cost spiking in California today because of a heatwave, ship the workload somewhere cooler. James talks about a futures market for MIPS. I think he might be on to something.
Somewhere, in this data center arbitrage model, there is also a business opportunity, since someone is going to have to help customers find the find the best cost for data center resources and intermediate the transaction. Hmmm....
[This post appeared orignally here and is republished in full with the kind permission of the author, who retains copyright.]
Published November 3, 2008 Reads 40,455
Copyright © 2008 SYS-CON Media, Inc. — All Rights Reserved.
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More Stories By Omar Sultan
Omar Sultan is a regular contributor to Cisco's Data Center Blog.
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads.
In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the public cloud best suits your organization, and what the future holds for operations and infrastructure engineers in a post-container world. Is a serverless world inevitable?Apr. 14, 2019 05:15 PM EDT Reads: 4,265 |
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By Pat Romanski In his session at 23rd International CloudEXPO, Raju Shreewastava, founder of Big Data Trunk, will provide a fun and simple way to introduce Machine Leaning to anyone and everyone. Together we will solve a machine learning problem and find an easy way to be able to do machine learning without even coding. Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Big Data teams at Autodesk. He is a contributing author of book on Azure and Big Data published by SAMS.Apr. 14, 2019 04:30 PM EDT Reads: 2,840 |
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