Often Google Compute recommends upgrading cpu/memory of the vms I am using. I can see the cpu graph of the instance so I can imagine where it gets the idea I should upgrade the CPU, but there is no such thing for ram so How does it know when to upgrade the ram?
You can use the companion Google Stackdriver app which by default collects more metrics for all your instances. The URL for the metrics for a single instance is
https://app.google.stackdriver.com/instances/<INSTANCE_ID>?project=<PROJECT_ID>
The hypervisor gives us some idea of the number of RAM pages that have been used, and we can make a recommendation based on that. However, since in any case Google does not inspect the RAM, we don't actually know what the purpose of pages are, merely how many are backed by physical RAM. Installing the StackDriver agent allows us to get better information from the guest OS about what is considered to be in use by the OS, and we can actually recommend better discounts in that case. The docs page [1] talks about this a little, although it could probably go into more detail about the memory usage signals.
[1] https://cloud.google.com/compute/docs/instances/viewing-sizing-recommendations-for-instances
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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
The title pretty much says it all, but just to clarify:
Redis afaik has a performance metric mem_fragmentation_ratio, giving the ratio of memory used as seen by the OS (used_memory_rss) to memory allocated by Redis (used_memory).
Is it possible to find this metric (or calculate it using other existing metrics) in Cloud Monitoring?
Unfortunately the answer is no. Only the GCP support team is able to verify this metric with their internal tools :(, nevertheless, I found this Public Issue tracker that is requesting to add this metric to monitor the instance. I suggest you do a comment and start the issue so you can get notifications about this :)
on the other hand, about the calculation, I found this link. It seems that the formula is:
MemoryFragmentationRatio= Used_Memory_RSS / Used_Memory
With the command "info all" you can get these values.
I really hope that the aforementioned information helps you.
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!
The company which I work right now planning to use AWS to host a new website for a client. Their old website had roughly 75,000 sessions and 250,000 page views per year. We haven't used AWS before and I need to give a rough cost estimate to my project manager.
This new website is going to be mostly content-driven with a cms backend (probably WordPress) + a cost calculator for their services. Can anyone give me a rough idea about the cost to host such kind of a website in aws?
I have used simple monthly calculator with a single Linux t2.small 3 Year upfront which gave me around 470$.
(forgive my English)
The only way to know the cost is to know the actual services you will consume (Amazon EC2, Amazon EBS, database, etc). It is not possible to give an accurate "guess" of these requirements because it really does depend upon the application and usage patterns.
It is normally recommended that you implement the system and run it for a while before committing to Reserved Instances so that you have a chance to measure performance and test a few different instance types.
Be careful using T2 instances for production workloads. They are very powerful instances, but if the CPU Credits run out, the amount of CPU is limited.
Bottom line: Implement, measure, test. Then you'll know what is right for your needs.
Take Note
When you are new in AWS you have a 1 year free tier on a single t2.micro
Just pulled it out, looking into your requirement you may not need this
One load balancer and App server should be fine (Just use route53 to serve some static pages from s3 while upgrading or scalling )
Use of email subscription and processing of Some document can be handled with AWS Lambda, SNS and SWQ which may further reduce the cost ( you may reduce the server size and do all the hevay lifting from Lambda)
A simple webpage with 3000 request/monthly can be handled by T2 micro which is almost free for one year as mentioned above in the note
You don't have a lot of details in your question. AWS has a wide variety of services that you could be using in that scenario. To accurately estimate costs, you should gather these details:
What will the AWS storage be used for? A database, applications, file storage?
How big will the objects be? Each type of storage has different limits on individual file size, estimate your largest object size.
How long will you store these objects? This will help you determine static, persistent or container storage.
What is the total size of the storage you need? Again, different products have different limits.
How often do you need to do backup snapshots? Where will you store them?
Every cloud vendor has a detailed calculator to help you determine costs. However, to use them effectively you need to have all of these questions answered and you need to understand what each product is used for. If you would like to get a quick estimate of costs, you can use this calculator by NetApp.
I'm using cloud VPS instances to host very small private game servers. On Amazon EC2, I get good performance on their micro instance (1 vCPU [single hyperthread on a 2.5GHz Intel Xeon], 1GB memory).
I want to use Google Compute Engine though, because I'm more comfortable with their UX and billing. I'm testing out their small instance (1 vCPU [single hyperthread on a 2.6GHz Intel Xeon], 1.7GB memory).
The issue is that even when I configure near-identical instances with the same game using the same settings, the AWS EC2 instances perform much better than the GCE ones. To give you an idea, while the game isn't Minecraft I'll use that as an example. On the AWS EC2 instances, succeeding world chunks would load perfectly fine as players approach the edge of a chunk. On the GCE instances, even on more powerful machine types, chunks fail to load after players travel a certain distance; and they must disconnect from and re-login to the server to continue playing.
I can provide more information if necessary, but I'm not sure what is relevant. Any advice would be appreciated.
Diagnostic protocols to evaluate this scenario may be more complex than you want to deal with. My first thought is that this shared core machine type might have some limitations in consistency. Here are a couple of strategies:
1) Try backing into the smaller instance. Since you only pay for 10 minutes, you could see if the performance is better on higher level machines. If you have consistent performance problems no matter what the size of the box, then I'm guessing it's something to do with the nature of your application and the nature of their virtualization technology.
2) Try measuring the consistency of the performance. I get that it is unacceptable, but is it unacceptable based on how long it's been running? The nature of the workload? Time of day? If the performance is sometimes good, but sometimes bad, then it's probably once again related to the type of your work load and their virtualization strategy.
Something Amazon is famous for is consistency. They work very had to manage the consistency of the performance. it shouldn't spike up or down.
My best guess here without all the details is you are using a very small disk. GCE throttles disk performance based on the size. You have two options ... attach a larger disk or use PD-SSD.
See here for details on GCE Disk Performance - https://cloud.google.com/compute/docs/disks
Please post back if this helps.
Anthony F. Voellm (aka Tony the #p3rfguy)
Google Cloud Performance Team