Explore your network in a new way with Wireshark, a user-friendly, open-source packet analyzer. This comprehensive guide will help you understand what Wireshark is and how to install it on Ubuntu 22.04.
What is Wireshark?
Wireshark, a well-known open-source packet analyzer, is often referred to as a network protocol analyzer. It serves multiple functions such as network troubleshooting, analysis, software and protocol development, and even educational purposes. Wireshark is a multi-platform tool, compatible with Windows, Linux, macOS, and more.
With Wireshark, you can decode live data from a variety of sources, including Ethernet, IEEE 802.11, PPP/HDLC, ATM, Bluetooth, USB, Token Ring, Frame Relay, FDDI, and beyond. Moreover, it has the ability to interpret captured data from files saved in an array of formats, such as tcpdump (PCAP), NAI’s Sniffer (both compressed and uncompressed), Sniffer Pro, NetXray, Network Instruments Observer, NetScreen snoop, Novell LANalyzer, RADCOM WAN/LAN Analyzer, among others.
To put it simply, Wireshark functions as a diagnostic tool for your network, offering you a granular view of your network activities. It has gained significant popularity across industries for network troubleshooting and learning about network protocols.
Install Wireshark on Ubuntu 22.04
1. Establish a connection to your server running Ubuntu.
2. Update the repository’s list of packages that are available for installation on Ubuntu:
sudo apt update3. Proceed to install Wireshark from the repositories of Ubuntu:
sudo apt install wireshark
4. During the installation, you’ll be asked whether non-superusers should be permitted to capture packets. If you want to capture packets without needing to run Wireshark as root, you should select ‘Yes’.
5. If ‘No’ was your choice in the previous step or you wish to assign packet capture permissions to a specific user, you can add the user to the Wireshark group by typing in the command line:
sudo usermod -a -G wireshark usernameRemember to replace ‘username’ with the actual username of the person you want to extend permissions to.
You can launch Wireshark by simply typing ‘wireshark’ in the terminal. As an alternative, you can also look for Wireshark in your applications list and initiate it from the GUI.
Conclusion
Want to run Ubuntu in a virtual environment? With Gcore Cloud, you can choose from Basic VM, Virtual Instances, or VPS/VDS suitable for Ubuntu:
- Gcore Basic VM offers shared virtual machines from €3.2 per month
- Virtual Instances are virtual machines with a variety of configurations and an application marketplace
- Virtual Dedicated Servers provide outstanding speed of 200+ Mbps in 20+ global locations
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Inefficient Code, Libraries, and FrameworksPoor-quality code and inefficient frameworks can increase the size of web pages, consume too many resources, and slow down page load times. Code quality is often affected by syntax, semantics, and logic errors. Correct these issues by writing clean and simple code.Errors or inefficiencies introduced by developers can impact site performance, such as excessive API calls or memory overuse. Prevent these issues by using TypeScript, console.log, or built-in browser debuggers during development. For bugs in already shipped code, utilize logging and debugging tools like the GNU debugger or WinDbg to identify and resolve problems.Improving code quality also involves minimizing the use of large libraries and frameworks. While frontend frameworks like React, Vue, and Angular.js are popular for accelerating development, they often include extensive JavaScript and prebuilt components that can bloat your website’s codebase. 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Tools for minification include UglifyJS for JavaScript, CSSNano for CSS, and HTMLminifier for HTML.Bundling groups multiple files into one, reducing the number of HTTP requests and speeding up site load times. Popular bundling tools include Rollup, Webpack, and Parcel.File compression using GZIP or Brotli can also reduce the weight of HTTP requests and responses before they reach users’ browsers. Enable your chosen compression technique on your server only after checking that your server provider supports it.4. Unoptimized Images and VideosSome websites are slowed down by large media files. Upload only essential media files to your site. For images, compress or resize them using tools like TinyPNG and Compressor.io. Convert images from JPEG, PNG, and GIF to WebP and AVIF formats to maintain quality while reducing file size. This is especially beneficial in industries like e-commerce and travel, where multiple images boost conversion rates. Use dynamic image optimization services like Gcore Image Stack for efficient processing and delivery. For pages with multiple images, use CSS sprites to group them, reducing the number of HTTP requests and speeding up load times.When adding video files, use lite embeds for external links. Standard embed code, like YouTube’s, is heavy and can slow down your pages. Lite embeds load only thumbnail images initially, and the full video loads when users click the thumbnail, improving page speed.5. No Lazy LoadingLazy loading delays the rendering of heavy content like images and JavaScript files until the user needs it, contrasting with “eager” loading, which loads everything at once and slows down site load times. Even with optimized images and code, lazy loading can further enhance site speed through a process called “timing.”Image timing uses the HTML loading attribute in an image tag or frameworks like Angular or React to load images in response to user actions. The browser only requests images when the user interacts with specific features, triggering the download.JavaScript timing controls when certain code loads. If JavaScript doesn’t need to run until the entire page has rendered, use the defer attribute to delay its execution. If JavaScript can load at any time without affecting functionality, load it asynchronously with the async attribute.6. Heavy or Redundant External Widgets and PluginsWidgets and plugins are placed in designated frontend and backend locations to extend website functionality. Examples include Google review widgets that publish product reviews on your website and Facebook plugins that connect your website to your Facebook Page. As your website evolves, more plugins are typically installed, and sometimes website admins forget to remove those that are no longer required.Over time, heavy and unused plugins can consume substantial resources, slowing down your website unnecessarily. Widgets may also contain heavy HTML, CSS, or JavaScript files that hinder web performance.Remove unnecessary plugins and widgets, particularly those that make cURL calls, HTTP requests, or generate excessive database queries. Avoid plugins that load heavy scripts and styles or come from unreliable sources, as they may contain malicious code and degrade website performance.7. Network IssuesYour server’s physical location significantly impacts site speed for end users. For example, if your server is in the UK and your users are in China, they’ll experience high latency due to the distance and DNS resolution time. The greater the distance between the server and the user, the more network hops are required, increasing latency and slowing down site load times.DNS resolution plays a crucial role in this process. Your authoritative DNS provider resolves your domain name to your IP address. If the provider’s server is too far from the user, DNS resolution will be slow, giving visitors a poor first impression.To optimize content delivery and reduce latency, consider integrating a content delivery network (CDN) with your server-side code. A CDN stores copies of your static assets (e.g., container images, JavaScript, CSS, and HTML files) on geographically distributed servers. This distribution ensures that users can access your content from a server closer to their location, significantly improving site speed and performance.8. No CachingWithout caching, your website has to fetch data from the origin server every time a user requests. This increases the load time because the origin server is another physical hop that data has to travel.Caching helps solve this problem by serving pre-saved copies of your website. Copies of your web files are stored on distributed CDN servers, meaning they’re available physically closer to website viewers, resulting in quicker load times.An additional type of caching, DNS caching, temporarily stores DNS records in DNS resolvers. This allows for faster domain name resolution and accelerates the initial connection to a website.9. Excessive RedirectsWebsite redirects send users from one URL to another, often resulting in increased HTTP requests to servers. These additional requests can potentially crash servers or cause resource consumption issues. To prevent this, use tools like Screaming Frog to scan your website for redirects and reduce them to only those that are absolutely necessary. Additionally, limit each redirect to making no more than one request for a .css file and one for a .js file.10. Lack of Mobile OptimizationForgetting to optimize for mobile can harm your website’s performance. Mobile-first websites optimize for speed and UX. Better UX leads to happier customers and increased sales.Optimizing for mobile starts with understanding the CPU, bandwidth, and memory limitations of mobile devices compared to desktops. Sites with excessively heavy files will load slowly on mobiles. Writing mobile-first code, using mobile devices or emulators for building and testing, and enhancing UX for various mobile device types—such as those with larger screens or higher capacity—can go a long way to optimizing for mobile.How Can Gcore Help Prevent These Web Performance Mistakes?If you’re unsure where to start in correcting or preventing web performance mistakes, don’t worry—you don’t have to do it alone. Gcore offers a comprehensive suite of solutions designed to enhance your web performance and deliver the best user experience for your visitors:Powerful VMs: Fast web hosting with a wide range of virtual machines.Managed DNS: Hosting your DNS zones and ensuring quick DNS resolution with our fast Managed DNS.CDN: Accelerate both static and dynamic components of your website for global audiences.With robust infrastructure from Gcore, you can ensure optimal performance and a seamless experience for all your web visitors. Keep your website infrastructure in one place for a simplified website management experience.Need help getting started? Contact us for a personalized consultation and discover how Gcore can supercharge your website performance.Get in touch to boost your website
How to Choose Between Bare Metal GPUs and Virtual GPUs for AI Workloads
Choosing the right GPU type for your AI project can make a huge difference in cost and business outcomes. The first consideration is often whether you need a bare metal or virtual GPU. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server, whereas a virtual GPU means you share GPU resources with other virtual machines.Read on to discover the key differences between bare metal GPUs and virtual GPUs, including performance and scalability, to help you make an informed decision.The Difference Between Bare Metal and Virtual GPUsThe main difference between bare metal GPUs and virtual GPUs is how they use physical GPU resources. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server. There is no hypervisor layer between the operating system (OS) and the hardware, so applications use the GPU resources directly.With a virtual GPU, you get a virtual machine (VM) and uses one of two types of GPU virtualization, depending on your or a cloud provider’s capabilities:An entire, dedicated GPU used by a VM, also known as a passthrough GPUA shared GPU used by multiple VMs, also known as a vGPUAlthough a passthrough GPU VM gets the entire GPU, applications access it through the layers of a guest OS and hypervisor. Also, unlike a bare metal GPU instance, other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.The difference between running applications with bare metal and virtual GPUsThese architectural features affect the following key aspects:Performance and latency: Applications running on a VM with a virtual GPU, especially vGPU, will have lower processing power and higher latency for the same GPU characteristics than those running on bare metal with a physical GPU.Cost: As a result of the above, bare metal GPUs are more expensive than virtual GPUs.Scalability: Virtual GPUs are easier to scale than bare metal GPUs because scaling the latter requires a new physical server. In contrast, a new GPU instance can be provisioned in the cloud in minutes or even seconds.Control over GPU hardware: This can be critical for certain configurations and optimizations. For example, when training massive deep learning models with a billion parameters, total control means the ability to optimize performance optimization—and that can have a big impact on training efficiency for massive datasets.Resource utilization: GPU virtualization can lead to underutilization if the tasks being performed don’t need the full power of the GPU, resulting in wasted resources.Below is a table summarizing the benefits and drawbacks of each approach: Bare metal GPUVirtual GPUPassthrough GPUvGPUBenefitsDedicated GPU resourcesHigh performance for demanding AI workloadsLower costSimple scalabilitySuitable for occasional or variable workloadsLowest costSimple scalabilitySuitable for occasional or variable workloadsDrawbacksHigh cost compared to virtual GPUsLess flexible and scalable than virtual GPUsLow performanceNot suitable for demanding AI workloadsLowest performanceNot suitable for demanding AI workloadsShould You Use Bare Metal or Virtual GPUs?Bare metal GPUs and virtual GPUs are typically used for different types of workloads. Your choice will depend on what AI tasks you’re looking to perform.Bare metal GPUs are better suited for compute-intensive AI workloads that require maximum performance and speed, such as training large language models. They are also a good choice for workloads that must run 24/7 without interruption, such as some production AI inference services. Finally, bare metal GPUs are preferred for real-time AI tasks, such as robotic surgery or high-frequency trading analytics.Virtual GPUs are a more suitable choice for the early stages of AI/ML and iteration on AI models, where flexibility and cost-effectiveness are more important than top performance. Workloads with variable or unpredictable resource requirements can also run on this type of GPU, such as training and fine-tuning small models or AI inference tasks that are not sensitive to latency and performance. Virtual GPUs are also great for occasional, short-term, and collaborative AI/ML projects that don’t require dedicated hardware—for example, an academic collaboration that includes multiple institutions.To choose the right type of GPU, consider these three factors:Performance requirements. Is the raw GPU speed critical for your AI workloads? If so, bare metal GPUs are a superior choice.Scalability and flexibility. Do you need GPUs that can easily scale up and down to handle dynamic workloads? If yes, opt for virtual GPUs.Budget. Depending on the cloud provider, bare metal GPU servers can be more expensive than virtual GPU instances. Virtual GPUs typically offer more flexible pricing, which may be appropriate for occasional or variable workloads.Your final choice between bare metal GPUs and virtual GPUs depends on the specific requirements of the AI/ML project, including performance needs, scalability requirements, workload types, and budget constraints. Evaluating these factors can help determine the most appropriate GPU option.Choose Gcore for Best-in-Class AI GPUsGcore offers bare metal servers with NVIDIA H100, A100, and L40S GPUs. Using the 3.2 Tbps InfiniBand interface, you can combine H100 or A100 servers into scalable GPU clusters for training and tuning massive ML models or for high-performance computing (HPC).If you are looking for a scalable and low-latency solution for global AI inference, explore Gcore Inference at the Edge. It especially benefits latency-sensitive, real-time applications, such as generative AI and object recognition.Discover Gcore bare metal GPUs
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