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Running V2Ray VPN Service via WebSocket and Gcore Services

  • By Gcore
  • February 12, 2024
  • 5 min read
Running V2Ray VPN Service via WebSocket and Gcore Services

This guide will walk you through setting up the V2Ray proxy tool with the WebSocket protocol and Gcore services. A Gcore Basic VM instance will act as your V2Ray server, while Gcore DNS, CDN, and DDoS Protection will provide a fast and reliable connection via WebSocket. A significant advantage of this approach is the ability to run your own V2Ray server on a virtual machine for only €3.2 per month, while benefiting from additional Gcore services for free.

In short, the connection between a user and the internet will be organized as follows:

  • A V2Ray client is deployed on a user device.
  • The V2Ray client connects to a V2Ray server deployed on Gcore Basic VM using DDoS-protected CDN and DNS.
  • This connection gives the user secure access to a required website.

Once you’ve completed the installation process, here’s how your device will interact with websites you visit:

Figure 1: Data flow diagram for V2Ray connection based on Gcore services

There is one prerequisite you need to complete before beginning this guide. To use DNS and CDN, you need to have a domain already registered. The domain will be delegated to Gcore authoritative NS servers.

What Is V2Ray?

V2Ray is a free proxy tool that secures your internet connection and is widely used as a VPN service. It supports multiple protocols, customizable routing, obfuscation, and reverse proxy features. V2Ray is compatible with various platforms, including Windows, macOS, Linux, and mobile devices. The tool is maintained by the V2Fly open-source community, ensuring it stays up to date.

What Is WebSocket?

WebSocket is a computer communication protocol that enables full-duplex, two-way communication channels over a single Transmission Control Protocol (TCP) connection. This advanced technology supports real-time, event-driven communication between a server and a client (such as a web browser.)

Key benefits of WebSocket include:

  • Full-duplex communication: Allows both client and server to send and receive data simultaneously, a significant advantage over the traditional request-and-response model of HTTP.
  • Low latency: Designed for rapid communication, making it ideal for real-time applications.

By combining V2Ray with the WebSocket protocol, you achieve a faster and more stable internet connection.

How to Set Up V2Ray via WebSocket

Setting up V2Ray requires the following steps:

  1. Create a virtual machine (VM) in the Gcore Customer Portal under Edge Cloud.
  2. Connect to your VM and update the OS.
  3. Install the V2Ray server on your VM.
  4. Access the V2Ray server GUI (graphical user interface.)
  5. Configure DNS.
  6. Configure CDN.
  7. Set up a node in the V2Ray server.
  8. Connect to your server from your local machine.

Basic Gcore DDoS Protection is a built-in feature of Basic VM, so you don’t need to configure it.

Let’s go through all the steps in order.

Step 1: Create a Virtual Machine for a V2Ray Server

  1. Log into your Gcore Customer Portal and navigate to the Basic VM section. If you don’t yet have an account, sign up first.
  2. Choose a region for your VM. For this article, we’ll use Frankfurt.
Figure 2: Choose a region for the VM
  1. Configure the VM as follows:

Region: Frankfurt
OS: Ubuntu 23.04
Flavor: Basic VM, 1 vCPU / 1GB RAM / 25GB SSD
SSH Key: Select your public SSH key or generate a new one.
Name: v2rayserver (or whatever you want.)

  1. After configuration, click Create Basic VM. The VM will appear in the “Virtual Instances” list. Wait for the VM’s status to change from “Creating” to “Power on.”
Figure 3: The newly created VM with “Power on” status

Note the highlighted IP address in Figure 3: this is the public IP address we’ll use in Step 2 to connect to the VM.

  1. In the same Basic VM section, go to Networking > Firewall and click the default firewall. You can choose one of two options when adding inbound rules: a) set “All TCP” and “All UDP” for TCP and UDP ports, or b) add specific inbound rules:
  • TCP rule with port 3321
  • TCP rule with port 80
  • UDP rule with port 3321

This is what the inbound rules should look like:

Figure 4: The firewall settings

Step 2: Connect to Your Basic VM and Update OS

Before installing a V2Ray server, you need to update the Basic VM operating system.

  1. To connect to your VM, open your CLI and use the following command, inserting the public IP address of your VM:
ssh ubuntu@<V2Ray server public IP address>

Example:

ssh ubuntu@176.119.203.30
  1. After you’re connected, update your OS using the following command:
sudo suroot@v2rayserver:/home/ubuntu# apt update

Note: From this step on, instead of “v2rayserver”, you should see/use your server’s name.

Step 3: Install the V2Ray Server on Your VM

The easiest way to install the V2Ray server is by using the official V2Ray installation script. To do that, run the following command:

root@v2rayserver:/home/ubuntu# bash &lt;(curl -Ls https://raw.githubusercontent.com/vaxilu/x-ui/master/install.sh)

The V2Ray GUI and CLI tools only have a Chinese version. If you don’t know Chinese, don’t worry. Just follow our instructions.

During the installation, you’ll need to configure the server’s account name, password, and panel access port. This is how it looks in our example:

出于安全考虑,安装/更新 完成后需要强制修改端口与账户密码
确 认是否继 续? (Confirm to continue?) [y/n]:y
请设置您的账户名 (Please set your account name):v2ray
您的账户名将设定为:v2ray
请设置您的账户密码 (Please set your account password):v2raypassword
您的账户密码将设定为:v2raypassword
请设置面板访问端口 (Please set the panel access port):5000
您的面板访问端口将设定为:5000

Step 4: Connect to the V2Ray Server GUI

  1. To access the V2Ray server GUI, open a web browser and enter the URL of your V2Ray server:

http://<V2Ray server public IP address>:5000

Here’s an example:

http://176.119.203.30:5000

You’ll see the login window:

Figure 5: The V2Ray Server GUI login window
  1. Enter the username and password that you chose in Step 3. You will see this dashboard:
Figure 6: The V2Ray Server GUI

Step 5: Configure DNS

We will use Gcore free public DNS with a delegated domain to create a new record for the V2Ray server.

  1. Send us a request for DNS activation. You’ll receive an email notification when Gcore DNS is activated.
  2. Create a new zone for your domain. The newly-created DNS zone will be displayed in the All zones section, and its status should be “Delegated”.
Figure 7: The delegated zone
  1. Add a new A record with the following configuration:

Type (Required): A
Name: <whatever name you want>.<your domain>
IPv4 address (Required): <V2Ray server public IP address>

Note: For the name, you may want to use the particular purpose for which this VM will be used or the region chosen.

Click Add.

Configuration example:

Figure 8: The new A record configuration

Step 6: Configure CDN

  1. Navigate to the CDN section of the Gcore Customer Portal and click Create CDN resource.
  1. Choose the option “Accelerate and protect entire site” and click Confirm.
Figure 9: Choose an acceleration type
  1. Enter the name you chose in step 5.3 above, and click Confirm.
Figure 10: Type the site name
  1. Review your DNS records and click Confirm.
Figure 11: The DNS records list
  1. If all the steps have been completed successfully, the CDN resource and DNS zone will be automatically created for the site you specified in the step “Enter site name” above.
Figure 12: The CDN resource and DNS zone are created
  1. Click Confirm and then Open resource settings. You’ll see this window:
Figure 13: Configure the CDN resource settings
  1. Go to the settings for “Origin” and set the “Origin Source” port as 3321.
Figure 14: Set an Origin Source port
  1. In the “Origin shielding” section, enable SSL and select the Let’s Encrypt option.
Figure 15: Enable SSL with the Let’s Encrypt option

WebSocket is now configured. It will take up to twenty minutes for SSL to apply to all CDN nodes.

Step 7: Configure Your Node in the V2Ray Server

The next step is to configure your node.

  1. Go to the V2Ray Server GUI, open the second tab in the menu on the left, and click the blue “+” to open the node settings.
Figure 16: Open the node settings
  1. Set the following parameters as they’re filled in the image below, except “id” which is filled in automatically. Then, press the blue button at the bottom-right corner.
Figure 17: Configure the node

Step 8: Connect to Your Server From Your Local Machine

  1. Install the V2Ray client on your device using the appropriate guide.

We’ll use the macOS client V2Box as an example; the details are also applicable to other clients.

  1. Next, copy the server configuration as a QR code. Open the V2Ray node section (step 7.1,) click the first option on the left, and choose the first item in the drop-down list.
Figure 18: The V2Ray server list

Click Copy.

Figure 19: Copy the server configuration
  1. Open the V2Ray client, click “+”, and choose the first option in the list to import the configuration.
Figure 20: Import the server configuration

The configuration profile will then appear in the Configs list.

Figure 21: The Configs list with the imported configuration
  1. Click the pencil icon to edit the profile. Change the Address field to your CDN/DNS name, set port 443, enable TLS, and leave the other fields as they are.
Figure 22: Edit the Config profile

Scroll down and click the blue button to save changes.

Figure 23: Save changes

Go to the home page and swipe the “Slide to connect” toggle. You’ll see this window:

Figure 24: The connection is established

Congratulations! You have successfully configured V2Ray on your local machine.

You can now check your new IP address and location using www.whatismyip.com:

Figure 25: Check your new IP via www.whatismyip.com

Conclusion

Using V2Ray in combination with Gcore Basic VM, DNS, CDN, and DDoS protection enhances your internet connection’s security, speed, and resilience, offering a superior alternative to configurations that rely on a single virtual machine to run a V2Ray server.

To learn more about the products we’ve used and their features, please visit their respective Gcore pages:

Try Gcore Basic VM

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For applications that handle large data transfers, such as cloud backup or file servers, make sure that the network interface provides high throughput.Network latency: Low latency is crucial for applications where real-time performance is key (e.g., trading systems, gaming). Choose VMs with low-latency networking options to minimize delays and improve the user experience.Network isolation and security: Check if your VM configuration provides the necessary network isolation and security features, especially when handling sensitive data or operating in multi-tenant environments.﹟6 Factor in cost considerationsWhile it’s essential that your VM has the right configuration, cost is always an important factor to consider. Cloud providers typically charge based on the resources allocated, so optimizing for cost efficiency can significantly impact your budget.Consider whether a pay-as-you-go or reserved model (which offers discounted rates in exchange for a long-term commitment) fits your usage pattern. The reserved option can provide significant savings if your workload runs continuously. You can also use monitoring tools to track your VM’s performance and resource usage over time. This data will help you make informed decisions about scaling up or down so you’re not paying for unused resources.﹟7 Evaluate security featuresSecurity is a primary concern when selecting a VM configuration, especially for workloads handling sensitive data. Consider the following:Built-in security: Look for VMs that offer integrated security features such as DDoS protection, web application firewall (WAF), and encryption.Compliance: Check that the VM configuration meets industry standards and regulations, such as GDPR, ISO 27001, and PCI DSS.Network security: Evaluate the VM's network isolation capabilities and the availability of cloud firewalls to manage incoming and outgoing traffic.﹟8 Consider geographic locationThe geographic location of your VM can impact latency and compliance. Therefore, it’s a good idea to choose VM locations that are geographically close to your end users to minimize latency and improve performance. In addition, it’s essential to select VM locations that comply with local data sovereignty laws and regulations.﹟9 Assess backup and recovery optionsBackup and recovery are critical for maintaining data integrity and availability. Look for VMs that offer automated backup solutions so that data is regularly saved. You should also evaluate disaster recovery capabilities, including the ability to quickly restore data and applications in case of failure.﹟10 Test and iterateFinally, once you've chosen a VM configuration, testing its performance under real-world conditions is essential. Most cloud providers offer performance monitoring tools that allow you to assess how well your VM is meeting your workload requirements.If you notice any performance bottlenecks, be prepared to adjust the configuration. This could involve increasing CPU cores, adding more memory, or upgrading storage. Regular testing and fine-tuning means that your VM is always optimized.Choosing a virtual machine that suits your requirementsSelecting the best virtual machine configuration is a key step toward optimizing your workloads efficiently, cost-effectively, and without unnecessary performance bottlenecks. By understanding your workload’s needs, considering factors like CPU, memory, storage, and network performance, and continuously monitoring resource usage, you can make informed decisions that lead to better outcomes and savings.Whether you're running a small application or large-scale enterprise software, the right VM configuration can significantly improve performance and cost. Gcore offers a wide range of virtual machine options that can meet your unique requirements. Our virtual machines are designed to meet diverse workload requirements, providing dedicated vCPUs, high-speed storage, and low-latency networking across 30+ global regions. You can scale compute resources on demand, benefit from free egress traffic, and enjoy flexible pricing models by paying only for the resources in use, maximizing the value of your cloud investments.Contact us to discuss your VM needs

How to get the size of a directory in Linux

Understanding how to check directory size in Linux is critical for managing storage space efficiently. Understanding this process is essential whether you’re assessing specific folder space or preventing storage issues.This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.What is a Linux directory?A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.#1 Get Linux directory size using the du commandUsing the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).Check the size of a specific directory in LinuxTo get the size of a specific directory, open your terminal and type the following command:du -sh /path/to/directoryIn this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.Check the size of all directories in LinuxTo get the size of all files and directories within the current directory, use the following command:sudo du -h /path/to/directoryExample: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.#2 Get Linux directory size using ncduIf you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.For Debian or Ubuntu, use this command:sudo apt-get install ncduOnce installed, run ncdu followed by the path to the directory you want to analyze:ncdu /path/to/directoryThis will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:#3 Get Linux directory size using 1s -1aYou can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:-l (long listing format)Displays the detailed information for each file and directoryShows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name-a (all files)Instructs ls to include all files, including hidden files and directoriesIncludes hidden files on Linux that typically have names beginning with a . (dot)ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.Example: When you enter ls -la command and press Enter, you will see an output similar to this:Each line includes:File type and permissions (e.g., drwxr-xr-x):The first character indicates the file type- for a regular filed for a directoryl for a symbolic linkThe next nine characters are permissions in groups of three (rwx):r = readw = writex = executePermissions are shown for three classes of users: owner, group, and others.Number of links (e.g., 2):For regular files, this usually indicates the number of hard linksFor directories, it often reflects subdirectory links (e.g., the . and .. entries)Owner and group (e.g., user group)File size (e.g., 4096 or 1045 bytes)Modification date and time (e.g., Jan 7 09:34)File name (e.g., .bashrc, notes.txt, Documents):Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)ConclusionThat’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:Affordable shared compute resources starting from €3.2 per monthDeploy across 50+ cloud regions with dedicated servers for low-latency applicationsSecure apps and data with DDoS protection, WAF, and encryption at no additional costGet started today

How to Run Hugging Face Spaces on Gcore Inference at the Edge

Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization.  Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. Step 1: Log In to the Gcore Customer PortalGo to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. Step 2: Go to Inference at the EdgeIn the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. Step 3: Choose a Hugging Face ModelOpen huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. Click on Files in the Space and locate the Docker option. Copy the Docker image link and startup command from Hugging Face Space. Step 4: Deploy the Model on GcoreReturn to the Gcore Customer Portal deployment page and enter the following details: Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest Startup command: python app.py Container port: 7860 Configure the pod as follows: GPU-optimized: 1x L40S vCPUs: 16 RAM: 232GiB For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.Step 5: Interact with Your ModelOnce the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.Powerful, Simple AI Deployment with GcoreGcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. Explore Gcore Inference at the Edge

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