Worst custom scrolling I've seen in a long time... Completely disabled the swipe-back feature on my MacBook.
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Machine learning driven AWS EC2 scheduler
11–20 of 25 posts
Re: Machine learning driven AWS EC2 scheduler
#12This seems interesting and sounds like a fun project - but... How many EC2 instances can just be turned off and on? If they can, they're probably part of an autoscaling group and already scale based on required capacity... Or they're workstations and can be turned off on a schedule (as mentioned in the article), ie: work hours only. As much as I want to, I can't think of a purpose for this. Would be fun as a Kubernet…
I don't know how to setup an autoscaler group on EC2. But if I used this wouldn't I not have to set one up? What if you don't have perfect information about your fleet and some instances are workstations?
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Re: Machine learning driven AWS EC2 scheduler
#13This seems interesting and sounds like a fun project - but... How many EC2 instances can just be turned off and on? If they can, they're probably part of an autoscaling group and already scale based on required capacity... Or they're workstations and can be turned off on a schedule (as mentioned in the article), ie: work hours only. As much as I want to, I can't think of a purpose for this. Would be fun as a Kubernet…
> How many EC2 instances can just be turned off and on?
I could actually imagine situations where you have loads that also follow a schedule -- for example, if you have a ton of data and customers can schedule summary generation/analyses, a lot of them will tend to schedule them on the first of the month (that is, after the last month has ended).
In a case like that with a predictable load, you could boot up a bunch of instances a bit before the end of the last day of a month, then turn them off (to go back to normal loads) a bit into the second day of the new month.
Machine learning could help you pick out other peaks, like say, every Monday or Sunday.
Re: Machine learning driven AWS EC2 scheduler
#14I don't understand how machine learning fits into literally just an option to power down a machine on a schedule?
I know when my business does not need a particular environment up because it's my business... why would I need something to train that and than apply it, probably incorrectly, for me?
Re: Machine learning driven AWS EC2 scheduler
#15This seems interesting and sounds like a fun project - but... How many EC2 instances can just be turned off and on? If they can, they're probably part of an autoscaling group and already scale based on required capacity... Or they're workstations and can be turned off on a schedule (as mentioned in the article), ie: work hours only. As much as I want to, I can't think of a purpose for this. Would be fun as a Kubernet…
> How many EC2 instances can just be turned off and on? I could actually imagine situations where you have loads that also follow a schedule -- for example, if you have a ton of data and customers can schedule summary generation/analyses, a lot of them will tend to schedule them on the first of the month (that is, after the last month has ended). In a case like that with a predictable load, you could boot up a bunch…
> In a case like that with a predictable load, you could boot up a bunch of instances a bit before the end of the last day of a month, then turn them off (to go back to normal loads) a bit into the second day of the new month.
If it's predictable, no need for machine learning at all.
> "Machine learning could help you pick out other peaks, like say, every Monday or Sunday."
Or human can script around it, like you just did.
Re: Machine learning driven AWS EC2 scheduler
#16Earlier quoted context omitted.
> How many EC2 instances can just be turned off and on? I could actually imagine situations where you have loads that also follow a schedule -- for example, if you have a ton of data and customers can schedule summary generation/analyses, a lot of them will tend to schedule them on the first of the month (that is, after the last month has ended). In a case like that with a predictable load, you could boot up a bunch…
> In a case like that with a predictable load, you could boot up a bunch of instances a bit before the end of the last day of a month, then turn them off (to go back to normal loads) a bit into the second day of the new month. If it's predictable, no need for machine learning at all. > "Machine learning could help you pick out other peaks, like say, every Monday or Sunday." Or human can script around it, like you jus…
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Re: Machine learning driven AWS EC2 scheduler
#17I don't understand how machine learning fits into literally just an option to power down a machine on a schedule? I know when my business does not need a particular environment up because it's my business... why would I need something to train that and than apply it, probably incorrectly, for me?
Regular reminder for readers: If a service exists that claims to meet a need but you do not have that need, it means you are not a customer for that service, not that the need does not exist. If the need is not large enough, or if not enough customers exist that have that need, then the service may fail as a business, but that does not mean that there was no need at all.
If you "know when my business does not need a particular environment up" then you are not a great customer, but other people might be.
Re: Machine learning driven AWS EC2 scheduler
#18Re: Machine learning driven AWS EC2 scheduler
#19This is a rather low content/high marketing post, but the idea is interesting. I'd be more interested in a description of how it actually works, but I guess that's FittedCloud's secret sauce.
You may find this blog interesting - https://www.fittedcloud.com/blog/machine-learning-cpu-utiliz...
Slightly more technical content but the description of how it works still doesn't go much beyond "machine learning techniques"
That's an extremely broad brush for what I imagine is a much more specific technique or set of techniques.
Re: Machine learning driven AWS EC2 scheduler
#20I don't understand how machine learning fits into literally just an option to power down a machine on a schedule? I know when my business does not need a particular environment up because it's my business... why would I need something to train that and than apply it, probably incorrectly, for me?
Regular reminder for readers: If a service exists that claims to meet a need but you do not have that need, it means you are not a customer for that service, not that the need does not exist. If the need is not large enough, or if not enough customers exist that have that need, then the service may fail as a business, but that does not mean that there was no need at all. If you "know when my business does not need a…
Did you look at the product? It gives you schedule suggestions. So you have to know your schedule to pick the right one, which means you could use something that allows you to build a schedule.
If the product is for people who think adding unnecessary machine learning to simple tasks then sure, I'm not the audience but a condescending "you don't get it" is not really adding any clarity. Do you get it?