I am new to cloud computing, but want to use it to host a website I am building. The website will be a data analytics site, and each user will interacting with a MySQL database and reading data from text files. I want to be able to accommodate about 500 users at a time. The site will likely have around 1000-5000 users fully scaled. I have chosen GCP, and am wondering if the e2-standard-2 VM instance would be enough to get started. I will also be using a GCP HA MySQL server, I am thinking that 2 vCPU's and 5GB memory will be enough, with 50GB high availability SDD storage. Any suggestions would be appreciated? Also, is there anything other service I will need? Thank you!!
Your question is irrelevant. On Google Cloud Platform you have real time monitoring for CPU and RAM usage so you if your website is gaining more users you can just upgrade or downgrade your CPU or RAM with 2 or 3 mouse clicks. Start small and upgrade later if you see CPU or RAM is getting close to 100% usage. Start with a N1 chip micro instance 600MB RAM.
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My website is taking too much time to load. I have hosted it in a VM instance created from Google compute engine.
My website is built on MERN stack and running with docker compose. I'm using docker scaling for backend application but no load balancing for the client side or the VM.
I would really appreciate if someone helps me out with this issue, I've been searching for days but still couldn't figure out what the issue is.
This is the site link:
https://www.mindschoolbd.com
VM type: e2-standard-2,
zone: nortamerica-norteast2-a
There are few things that we can do to make it load fast :
1)You can optimize your application code (including database queries if you have) - on this part, you can contact your developer as this is out of our scope as well.
Check there are a few situations that may cause the load issue in mobile apps:
i)The MERN Stack - A Practical guide app server may be down and that is causing the loading issue.
ii)Your wifi / mobile data connection is not working properly. Please check your data connection.
iii)Too many users are using the app at the same time. Please try after a few minutes.
Please go through the MERN Stack practical issues for more info.
2)You can upgrade your Machine type and this includes the CPU and RAM of your VM instance and server caching is also part of it.
Check over util of memory and cpu increase, if required change it into something higher than e2-standard-2, As recommended in the SO for more information.
3)Try to have your vm deployed in a region which is near to your location. Suggest to create snapshot and create vm using snapshot and change region.
4)Create a cloud CDN for caching to make it load fast, Cloud CDN lowers network latency & Content delivery best practices, offloads origins, and reduces serving costs. Recommending to use Optimize application latency with load balancing and also installing an Ops agent in your VM instance to have a better monitoring view.
Finally go through the Website Speed Optimization Tips: Techniques to Improve Performance and User Experience for more information.
I have one website that has simple one page that fetches trending videos from youtube with the use of youtube api and size of the website is just 100 kb. website is created by using of HTML,CSS,PHP. I want to host it on any good cloud hosting. Suppose i will get 10000 daily visitors to my website then 1gb ram and 1 coreCPU is sufficient for that?
Nobody can answer this question for you because every application is different, and much of it depends on the patterns of your particular user base.
The only way to know the requirements is to deploy the system and then simulate user traffic. Monitor the system to identify stress points, which could be RAM, CPU or Network. You can then adjust the size of the instance accordingly, and even change Instance Type to obtain a different mix of RAM and CPU.
Alternatively, just deploy something and monitor it closely. Then, adjust things based on usage patterns you see. This is "testing in production".
You could also consider using Amazon EC2 Auto Scaling, which can automatically launch new instances to handle an increased load. This way, the resources vary based on usage. However, this design would require a Load Balancer in front of the instances.
Then, if you want to get really fancy, you could simply host a static web page from Amazon S3 and have the page make API calls to a backend hosted in AWS Lambda. The Lambda function will automatically scale by running multiple functions in parallel. This way, you do not require any Amazon EC2 instances and you only pay for resources when somebody actually uses the website. It would be the cheapest architecture to run. However, you would need to rewrite your web page and back-end code to fit this architecture.
We have 2 Elastic VMs (Linux) (Currently DS2V2) behind an Azure Load Balancer. We are doing HTTP Posts from our local lan into the Load Balancer, but we seem to be getting throttled. We have tried: Changing the size of the VMs, no difference; adding additional premium SSDs, again no difference; running multiple threads on our end, again no differenece.
What we did do though, was to having the Elastic Engine suck in all of the log files from the Linux boxes and the index rate jump pretty high while it was ingesting them. So we are assuming that it's not really the Linux Elastic boxes that are throttling us.
We do have Kibana installed on the boxes, and as a base line, we're just using the "Cluster Indexing Rate" for both our local posts to the box, and the local ingestion of the log files.
We do understand that yes, there is going to be some latency and overhead since we are now involving the internet, but not the rates we are currently getting. (We have a 1G pipe to the internet, it's nowhere near capacity, so we can rule out at least getting out of our company).
The question is, where else can we look to determine where we might be getting throttled?
For the performance "MUCH slower", it is a bit subjective question and hard to identify. I just provide some information that may impact it.
Azure Compute requests may be throttled at a subscription and on a per-region basis. If you have an API throttling error, you could refer to this document to troubleshoot throttling issues, and best practices to avoid being throttled.
Some factors CPU and storage limits that differ on Azure VM sizes may impact the Azure VM to process incoming data. You may change the size to a higher CPU and premium SSD disk. You could also change Azure resources to another region which is close to your location. You could refer to this article.
We're setting up a Moodle for our LMS and we're designing it to autoscale.
Here are the current stack specifications:
-Moodle Application (App + Data) baked into an image and launched into a Managed Instance Group
-Cloud SQL for database (MySQL 5.7 connected through Cloud SQL Proxy)
-Cloud Load Balancer - HTTPS load balancing with the managed instance group as backend + session affinity turned on
Questions:
Do I still need Redis/Memcached for my session? Or is the load balancer session affinity enough?
I'm thinking of using Cloud Filestore for the Data folder. Is this recommendable vs another Compute Engine?
I'm more concerned of the session cache and content cache for future user increase. What would you recommend adding into the mix? Any advise on the CI/CD would also be helpful.
So, I can't properly answer these questions without more information about your use case. Anyway, here's my best :)
How bad do you consider to be forcing the some users to re-login when a machine is taken down from the managed instance group? Related to this, how spiky you foresee your traffic will be? How many users will can a machine serve before forcing the autoscaler to kick in and more machines will be added or removed to/from the pool (ie, how dynamic do you think your app will need to be)? By answering these questions you should get an idea. Also, why not using Datastore/Firestore for user sessions? The few 10s of millisecond of latency shouldn't compromise the snappy feeling of your app.
Cloud Filestore uses NFS and you might hit some of the NFS idiosyncrasies. Will you be ok hitting and dealing with that? Also, what is an acceptable latency? How big is the blobs of data you will be saving? If they are small enough, you are very latency sensitive, and you want atomicity in the read/write operations you can go for Cloud BigTable. If latency is not that critical Google Cloud Storage can do it for you, but you also lose atomicity.
Google Cloud CDN seems what you want, granted that you can set up headers correctly. It is a managed service so it has all the goodies without you lifting a finger and it's cheap compared to serving stuff from your application/Google Cloud Storage/...
Cloud Builder for seems the easy option, unless you want to support more advanced stuff that are not yet supported.
Please provide more details so I can edit and focus my answer.
there is study for the autoscaling, using redis memory store show large network bandwidth from cache server, compare than compute engine with redis installed.
moodle autoscaling on google cloud platform
regarding moodle data, it show compute engine with NFS should have enough performance compare than filestore, much more expensive, as the speed also depend on the disk size.
I use this topology for the implementation
Autoscale Topology Moodle on GCP
I've a website that can have on an average half million views per month. Due to some limitations in shared hosting, I need to consider EC2 or some some VPS. However, among VPS, I'm feeling EC2 is reliable as I have to use it along with a Amazon SES (Will go more performance because of same location). I'm trying to find if I can host such a website with reasonable response time on EC2 t1.micro instance. Please let me know. If not, please suggest some alternatives
EC2 is in many ways like a VPS, with some tools that you probably wont find at most VPS providers.
You will probably not get half a million views on a micro unless they were very spread out during the day. Micros use a burstable architecture, you get 2 ECU worth of computing power, then you get locked to way less than 1 ECU for a while afterwards.