I have several VMs that are configured and powered off for both operational and DR purposes.
How can I achieve the equivalent of a Azure VM + configuration + resources stored only on (blob) storage and turn it on as needed (with a preferred set of name names, or name variants?
I'm familiar with Azure Automation, but just want to recognize the pieces, and if necessary automation that needs to occur to start/stop this "snapshot" of a base OS VM + lots of customization + software addins.
well, its kinda hard to figure out what you mean exactly, but your best bet is just creating vms and configuring them automatically or using a golden image. if you choose to use ephemeral OS disk - you are not paying for storage, only for compute, but you cant shut it down (all the changes are lost). Also, ephemeral OS disk might not be supported for custom images.
Apart from that, just deallocating a VM means you are not paying for it - only for the storage.
Related
I am data analyst. My company is moving all data science to a cloud provider (it could be Azure, GCP,AWS). All the data science programming tools like Jupyter notebook will be installed on the cloud environment (there will be no local installations of Python, or Jupyter Notebooks on the laptop).
For most of my work, I will be reading/ingesting relational database tables directly from an on-premise Database. Also most of my data analysis work does not require any GPU instances for data processing. Sometimes, I also do simple research or experimentation data analysis programming such as data cleaning using Jupyter notebooks without the need for usage of GPU instances.
I would like to find out if it would be possible to do such activities without incurring any pay-per-use costs or unnecessary expenses for my company on their data science cloud computing platform given that none of my tasks utilize GPUs? Please advise, thank you.
EDIT Note: It is difficult to work & develop locally with Jupyter on my company PC because I do not have full permissions to install Python packages(usually this has to be requested for approval, which is very painful and takes a very long time).
Jupyter Notebook can be installed in the cloud, but also on prem and on your workstation. You pay either resource in the cloud, on prem, or your worstation.
Of course, if you add large disk, GPUs, CPUs, memory, it costs more! The problem isn't the cost, it is more where do you want to run your notebook?
I think, there is a bad alternative. With Colab you have free Jupyter Notebook instance. But, AFAIK, it's not private, it's public instances and if you work for your company, you can have data leakage. (Not sure, to validate, but it's not a recommended solution in any case)
EDIT 1
Considering your latest comment, I wondering if you need a jupyter notebook to run your code.
Indeed, Jupyter is simply and IDE: you could create your script, even this one that need GPU locally, and to run it on production data on Compute Engine that you provision only for the process. At the end of the script destroy the VM. No Jupyter notebook environment for that, no?
EDIT 2
Thanks to your note, I understand that developing locally isn't an option. In this case, I recommend you to use a managed Jupyter Notebook solution. You can provision this VM on Google Cloud if you want, you can also have different VM, with or without GPU.
The principle is the same: when you stop to work with your instance, stop it. You will only pay for the storage (the disk) when the instance is down.
And the dev principle can be the same: use a small CPU/GPU for your dev, and when you have to process big data, run your script on a powerful VM. Because you pay only when the VM is running, you can optimize cost like that.
In addition to Guillaume's answer, if you want to keep track or to plan ahead if there are cost that will occur while using instances. You can use Google Cloud Platform's Pricing calculator:
https://cloud.google.com/products/calculator?hl=en
With this, you can can choose what product do you're interested to, what kind of components do want in your set-up (e.g. how many RAM, capacity of your storage space, CPU)in case you choose to use GCP Compute Engine, choose what location you are and check if that location price suits your company's budget.
If you want to have more information regarding Google Cloud Platform pricing, you can check out this link:
https://cloud.google.com/compute/all-pricing#compute-optimized_machine_types
So, I really like the idea of server-less. I came across Google Cloud Functions and Google Cloud Run.
So google cloud functions are individual functions, which is a broad perspective, I assume google must be securely running on a huge nodejs server. And it contains all the functions of all the google consumers and fulfils the request using unique URLs. Now, Google takes care of the cost of this one big server and charges users for every hit their function gets. So its pay to use. And makes sense.
But when it comes to Cloud Run. I fail to understand how does it work. Obviously the container must not always be running because then they will simply charge a monthly basis instead of a per-hit basis, just like a normal VM where docker image is deployed. But no, in reality, they charge on per hit basis, that means they spin up the container when a request arrives. So, I don't understand how does it spin it up so fast? The users have the flexibility of running any sort of environment, that means the docker container could contain literally anything. Maybe a full-fledged Linux OS. How does it load up the environment OS so quickly and fulfils the request? Well, maybe it maintains the state of the machine and shut it down when not in use, but even then, it will require a decent amount of time to restore the state.
So how does google really does it? How is it able to spin up a customer's container in literally no time?
The idea of fast spinning-up sandboxes containers (that run on their own kernel for security reasons) have been around for a pretty long time. For example, Intel Clear Linux Containers and Firecracker provide fast startup through various optimizations.
As you can imagine, implementing something like this would require optimizations at many layers (scheduling, traffic serving, autoscaling, image caching...).
Without giving away Google’s secrets, we can probably talk about image storage and caching: Just like how VMs use initramfs to pre-cache the state of the VM, instead of reading all the files from harddisk and following the boot sequence, we can do similar tricks with containers.
Google uses a similar solution for Cloud Run, called gVisor. It's a user-space virtualization technique (not an actual VMM or hypervisor). To run containers on a Linux-like environment, gVisor doesn't need to boot a Linux kernel from scratch (because gVisor reimplements the linux kernel in go!).
You’ll find many optimizations on other serverless platforms across most cloud providers (such as how to keep a container instance around, should you be predictively scheduling inactive containers before the load arrives). I recommend reading the Peeking Behind the Curtains of Serverless Platforms paper to get an idea about what are the problems in this space and what are cloud providers trying to optimize for speed and cost.
You have to decouple the containers to the VMs. The second link of Dustin is great because if you understand the principles of Kubernetes (and more if you have a look to Knative), it's easy to translate this to Cloud Run.
You have a pool of resources (Nodes in Kubernetes, the VM in fact with CPU and memory) and on these resources, you can run container: 1, 2, 1000 per VM, maybe, you don't know and you don't care. The power of the container, is the ability to be packaged with all the dependency that it needs. Yes, I talked about package because your container isn't an OS, it contains the dependencies for interacting with the host OS.
For preventing any problem between container from different project/customer, the container run into a sandbox (GVisor, first link of Dustin).
So, there is no VM to start and to stop, no VM to create when you deploy a Cloud Run services,... It's only a start of your container on existing resources. It's also for this reason that you need to have a stateless container, without disks attached to it.
Do you want 3 "secrets"?
It's exactly the same things with Cloud Functions! Your code is packaged into a container and deploy exactly as it's done with Cloud Run.
The underlying platform that manages Cloud Functions and Cloud Run is the same. That's why the behavior and the feature are very similar! Cloud Functions is longer to deploy because Google need to build the container for you. With Cloud Run the container is already built.
Your Compute Engine instance is also managed as a container on the Google infrastructure! More generally, all is container at Google!
You'll have to excuse my ignorance on this one...but honestly, I've had a hard time finding clarity on this. That being said, I'm looking for a non technical answer...something in layman's terms!
Anyways, I've been playing around building a web app (first time obviously) and I'm getting to the point where I've started looking into hosting services. A quick google search and a few blogs later, I thought AWS would be a good place to start, since they give a free-year trial. I don't care about speedy upstarts or other hosting serves, so save your key strokes on offering other services.
My question is based on the fact that AWS charges "Linux Usage per hour" and they also use this term "instance". Yeah...an "instance" is an "object", which is also above my head (probably the real source of the problem), but that was the extent I was able to learn via a google search. That being said, I don't know how to translate the cost into a ball park cost. Yes, I can probably use the trial to help monitor predictable costs, but I don't want to go through the effort of learning one hosting companies system just to find out it's not going to work in the end.
OK...so hopefully by now you see where I'm coming from. What is an "instance" and how do I use the "Linux Usage per hour" to estimate cost? Is an instance a server? For example if I start NGINX is that in instance? Is it just one instance running NGINX or does every VPN represent an instance? If I have 100 people calling the server at once, can they fit on one instance? If I start another server say, Apache or Node, does that become another instance? If I connect to a database, is that an instance? Do instances start as needed? Yes, I know, that's more than one question...I'm just trying to express my confusion.
If I'm suppose to choose a pricing model from this list, "Linux Usage per hour", I need to know what them mean by "Linux Usage". If it's based on an "instance", I need to know what that is. So please, in layman's terms, help clear this up. Maybe some examples or analogies, but no deep technical stuff.
This is more a side note, but I was reading this article and it said
For a client needing to run 800 virtual instances, the annual cost of
a private cloud came to below $400,000 vs. somewhere between $800,000
and $1.2 million for public cloud services.
Considering I don't know what an instance is, that kinda made me a bit nervous...WAAAAAAyyyyyy outta my price range! Yes, it's obviously a big company, but can you imagine "hitting the lottery" with an app everyone loves then before you know it, AWS hits you with a bill of $1,000,000. Or even worse, your security sucks and someone spawns millions of these "instances"...help alive my paranoia!!
Basically, an instance is a virtual machine, which looks very much like a server. As such it's running an operating system - e.g. linux - which is capable of running many programs (aka 'processes' or sometimes, 'services') at the same time.
To go through your questions (some of the explanations below are not technically accurate, but are hopefully more explanatory for it - if anything is obvious or already known, apologies - trying not to assume any knowledge)
An instance is an object
This definition is coming up in your searches because 'instance' has many definitions in different situations. If you see the definition of 'instance' as an object, it's from the topic of object oriented programming languages - you define a class in your code (kind of like a 'template'), and then create instances of the class - kind of like real copies of the template.
Amazon borrowed the term to be analogous - because in the 'cloud' world, you can create an AMI (Amazon Machine Image - the template) and then create lots of instances that are copies or clones of that template.
Is an instance a server?
In terms of what you can do with it, yes, it's a server.
(Technically it's a virtual server - Amazon runs multiple virtual servers on each physical server.)
how do I use the "Linux Usage per hour" to estimate cost?
Estimate how long you will have your instance running for in hours per month, multiply it by cost per hour and you will have your estimated cost per instance per month.
e.g. - one instance always turned on would be - 24 hrs * 31 days = 744 hours. At $0.013/hr (for a t2.micro) that would be 744 * $0.013 = $9.672/mth.
(And that's the reason the free tier gives you 750 hours of instance time per month.)
Instances come in different types and sizes and each size costs a different amount. If you are not sure what size you need, I'd start with the smallest until you discover you need more - which would be when your program starts running too slowly.
For example if I start NGINX is that in instance?
Nginx is a program that runs as a daemon in linux terms - a program that runs in the background so it's always on. It will be one of the many programs running on the server (aka the instance)
If I have 100 people calling the server at once, can they fit on one instance?
It depends - on how big your instance is, and how efficient the program is that is responding to their requests. If you are just getting started learning to program websites, I wouldn't worry about handling 100 people issuing requests to the server all at once just yet - walk before you run :) (also, even when there are 100 people visiting your website, the odds that all of them issue a request at exactly the same time is low - usually they load a page and read it - while they're reading it, some of the other people are loading other pages, and it all spreads out so you might only have ~10 page requests actively being processed by your server at the same time.)
However, if you have 2,000 people on your site at the same time, you might be processing 200 page requests at once, so by then you do need to have put some thought into performance and scalability.
(Note: these numbers are arbitrary and depend entirely on the type of site and it's traffic patterns.)
Generally, most websites pick a mid-level instance size, and then to handle more requests they 'scale out' - create lots of copies of that instance, and allow each instance to handle a portion of the traffic.
If I start another server say, Apache or Node, does that become another instance
The language to use here would be 'start another service say, Apache or Node' - they are other programs, and your instance will be perfectly fine running nginx, apache and node all at the same time. Although each will consume some of the resources (e.g. memory and cpu) and the more activity they are doing, the faster you will run out of resources and need to get a bigger instance size
So - no, they don't automatically become another instance. The language is confusing because sometimes people don't distinguish between the 'server' (aka the instance) and the service (aka the program) and will say the 'apache server' and the 'apache service' interchangably.
If I connect to a database, is that an instance?
Your instance, as a fully capable server, could run a database service on it at the same time as the other services - e.g. you could install and run mysql on your instance.
There is another option, though - if you use the AWS RDS product, then you will be starting an RDS instance. An RDS instance is different from an EC2 instance (what we've been talking about so far) in that RDS instances are specialised to just run the database service and nothing else, but EC2 instances are general servers that you can do pretty much anything on.
It's usually recommended to use RDS, but if you are trying to save money and aren't serving many users, there's nothing particularly wrong with installing mysql on your instance yourself (especially while you're learning how it works) and then moving your data to an RDS instance when you want to support more load or traffic.
Do instances start as needed?
Not by default, no - you have to manually start and stop them.
However, there are options other than manually starting and stopping. Amazon provides a lot of APIs, so you could write a program that would connect to the API and automatically start and stop your instance(s) based on rules you build into your program..
Also, Amazon offers a product called "AutoScalingGroups" which allows you to have a related group of instances and for Amazon to automatically start and stop them according to rules that you configure into that product. These rules can be 'scheduled actions' - start/stop at certain times of day - or they can be reactive - e.g. when the average CPU usage is > 50% for more than 5 minutes, start another instance.
This is more a side note, but I was reading this article and it said
For a client needing to run 800 virtual instances, the annual cost of
a private cloud came to below $400,000 vs. somewhere between $800,000
and $1.2 million for public cloud services.
The 'free tier' gives you a t2.micro sized instance (1 vCPU, 1 GiB RAM) which you could leave turned on permanently for free during that free year.
Even after your free tier expires, that same instance would cost you $9.67/mth, and you have the option to go downgrade to a t2.nano (0.5 GiB RAM) which would only cost ~$4/mth - but 0.5GiB RAM isn't much these days, so may not be enough for you.
A t2.micro should be more than enough to learn how to build websites on. If you are fortunate enough to build a site that is popular enough that you are getting more requests than that server can handle, then you will have to decide if you can generate revenue from that popularity sufficient to cover the cost, but by then you'll have more of a sense of how efficient your program is, and what instance size (and/or how many instances) you'll need.
Yes, it's obviously a big company, but can you imagine "hitting the
lottery" with an app everyone loves then before you know it, AWS hits
you with a bill of $1,000,000
AWS protects you from yourself here a bit - they have limits which generally restrict you from running more than 20 instances at a time - unless you ask for permission. So, by default, your instance won't go multiplying like rabbits on it's own - unless you set it up to. And even if you have set it up to, it won't be able to grow beyond 20 instances unless you have asked amazon to let you. So, worst case is 20 x $9.67/mth - $197/mth.
But - that's just the instance cost. Amazon charges you for lots of things including data traffic in and out, RDS instance costs, and if you start using other service such as S3 buckets and/or elastic load balancers, they all attract their own costs.
But hopefully, if you hit the lottery with an app everyone loves, you've worked out how to convert that love into dollars and cents so you can pay for all those instances you're going to need :)
We have an application deployed across AWS with using EC2, EBS services.
The infrastructure dropped by layers (independent instances):
application (with load balancer)
database (master-slave standard schema)
media server (streaming)
background processing (redis, delayed_job)
Application and Database instance use number of EBS block storage devices (root, data), which help us to attach/detach them and do EBS snapshots to S3. It's pretty default way how AWS works.
But EBS should be located in a specific zone and can be attached to one instance only in the same time.
Media server is one of bottlenecks, so we'd like to scale them with master/slave schema. So for the media server storage we'd like to try distributed file systems can be attached to multiple servers. What do you advice?
If you're not Facebook or Amazon, then you have no real reason to use something as elaborate as Hadoop or Cassandra. When you reach that level of growth, you'll be able to afford engineers who can choose/design the perfect solution to your problems.
In the meantime, I would strongly recommend GlusterFS for distributed storage. It's extremely easy to install, configure and get up and running. Also, if you're currently streaming files from local storage, you'll appreciate that GlusterFS also acts as local storage while remaining accessible by multiple servers. In other words, no changes to your application are required.
I can't tell you the exact configuration options for your specific application, but there are many available such as distributed, replicated, striped data. You can also play with cache settings to avoid hitting disks on every request, etc.
One thing to note, since GlusterFS is a layer above the other storage layers (particularly with Amazon), you might not get impressive disk performance. Actually it might be much worst than what you have now, for the sake of scalability... basically you could be better-off designing your application to serve streaming media from a CDN who already has the correct infrastructure for your type of application. It's something to think about.
HBase/Hadoop
Cassandra
MogileFS
Good same question (if I understand correctly):
Lustre, Gluster or MogileFS?? for video storage, encoding and streaming
There are many distributed file systems, just find the one you need.
The above are just part which I personally know (haven't tested them).
I put our application on EC2 (Windows 2003 x64 server) and attached up to 7 EBS volumes. The app is very I/O intensive to storage -- typically we use DAS with NTFS mount points (usually around 32 mount points, each to 1TB drives) so i tried to replicate that using EBS but the I/O rates are bad as in 22MB/s tops. We suspect the NIC card to the EBS (which are dymanic SANs if i read correctly) is limiting the pipeline. Our app uses mostly streaming for disk access (not random) so for us it works better when very little gets in the way of our talking to the disk controllers and handling IO directly.
Also when I create a volume and attach it, I see it appear in the instance (fine) and then i make it into a dymamic disk pointing to my mount point, then quick format it -- when I do this does all the data on the volume get wiped? Because it certainly seems so when i attach it to another AMI. I must be missing something.
I'm curious if anyone has any experience putting IO intensive apps up on the EC2 cloud and if so what's the best way to setup the volumes?
Thanks!
I've had limited experience, but I have noticed one small thing:
The initial write is generally slower than subsequent writes.
So if you're streaming a lot of data to disk, like writing logs, this will likely bite you. But if you make a big file fill it with data, and do a lot of random access I/O to it, it gets better on the second time writing to any specific location.