Businesses are moving to clouds.
According to Gartner, the global cloud services market reached $257.5 billion in 2020 and continues to grow.
Cloud providers typically offer three service models: IaaS, PaaS, and SaaS. Let’s figure out what these abbreviations mean and how to choose the right solution.
IaaS
IaaS stands for Infrastructure as a Service.
Almost every company now needs its own IT infrastructure (computers and servers where data is stored and processed) and the tools necessary for connecting that infrastructure to a single network. You can purchase equipment and service it yourself. Or you can rent computing power through a cloud.
The second option is called IaaS. Instead of setting up your infrastructure, monitoring its state, and constantly updating it, you take resources on lease and use them for your own purposes virtually.
IaaS can be compared to virtual “computers”. You get computing power and can use it however you want. In this case, you pay only for the resources consumed, and the provider monitors the state of the equipment.
IaaS features
- You rent a virtual or physical dedicated server and can install any programs on it or perform any operations.
- The provider provides special settings for managing your resources in the cloud. For example, you can divide resources into projects within an account and define access rules for different users.
- The load between servers can be evenly distributed using balancers.
- Files and backups can be placed in the cloud storage. You will have instant access to your data. You can also store an almost unlimited amount of information.
- Disaster recovery is available. Providers provide services that create backups of your infrastructure and automatically transfer the load there in case of failure. Thus, even in the case of a major disaster, your data won’t be lost, and access will be restored in a few minutes.
IaaS pros
1. Significant savings. If you’re setting up your own infrastructure, you will need to upgrade the equipment, hire employees to maintain it, and pay for rented premises and licensed software. This can all get quite expensive. But at the same time, for some tasks you don’t need the entire infrastructure, and some of the equipment will be idle from time to time.
If you choose IaaS, the provider takes care of the equipment maintenance and upgrades. You pay only for the resources consumed.
2. Easy scalability. With IaaS, you can increase your computing power in just a few clicks. This is especially useful when there is a surge in traffic, for example, during sales, promotions, or explosive business growth.
3. Flexibility. You can use the processing power however you want with almost no limits. Deploy and test any application, store data, and manage resources as you see fit. How exactly the infrastructure will function is up to you.
IaaS cons
IaaS doesn’t have many disadvantages:
- It’s a rather complex model. To use it, you need special knowledge and skills.
- The quality of your infrastructure depends on the reliability and conscientiousness of the provider: how they maintain the equipment and how often they update it. Choose a cloud provider very carefully.
What tasks is IaaS suitable for?
IaaS is an alternative to buying your own hardware. Therefore, this model is suitable for any type of business.
When is IaaS the perfect solution?
- Big surges in demand at certain times. The cloud saves you the trouble of buying additional hardware that will be idle most of the time. You’ll be able to increase the consumption of virtual resources during peak loads and use less power the rest of the time, allowing you to save on costs.
- The development and testing of applications. In a cloud infrastructure, you can instantly deploy separate environments to test software performance. This way, testing won’t affect the functioning of your main resources in any way. And after the tests are complete, you can minimize unnecessary environments.
IaaS with Gcore Cloud
Our cloud offers a variety of IaaS solutions to help you grow your business faster and cheaper:
- Rent virtual machines and bare metal servers.
- Distribute the load among resources using a user-friendly load balancer.
- Take snapshots for disaster recovery.
- Set up private clusters in cloud networks for secure computing or isolating applications.
- Store any data on our servers in Europe, Asia, and the USA.
- Accelerate the loading of web applications, online services, and media with the integrated CDN.
Learn more about cloud services
PaaS
PaaS stands for Platform as a Service.
It’s a pre-configured tool set that a provider provides to customers to build their applications.
You also rent computing power. But if, in the case of IaaS, you only have virtual machines or bare-metal servers, and you install and configure the working tools yourself, then PaaS already offers everything you need for development.
Unlike IaaS, you don’t have access to the operating system of the servers, so you can’t set their configuration and balance the load. The provider takes care of all that. And you, in turn, can use the platform tools, upload your data, and install application software.
Examples of PaaS include database processing systems, AI platforms, and Kubernetes containers. Basically, PaaS refers to any platform that can be used as a basis for developing applications.
What tasks is PaaS suitable for?
PaaS provides developers with a framework for building and testing software. How exactly the platform works depends on the purpose for which it’s intended and on what tools it contains.
For example, if it’s an AI platform, you can leverage the power of artificial intelligence in your applications.
Let’s say you want to create a model that can diagnose patients based on their CT scans. The platform already has ready-made tools for the loading and processing data, and support for full-cycle machine learning. You don’t need to teach the model to analyze snapshots from scratch. Instead, you use ready-made platform tools. The development process in this case is several times faster.
In addition, PaaS can be used for various other tasks depending on what features the platform offers. For example, you can manage and analyze databases.
PaaS pros
- The simplification, acceleration, and consequently, cost reduction of developing programs and applications.
- It’s easier to use than IaaS. The provider takes over the management of services, configuration, and load balancing.
- Development tools are ready to use, there’s no need to install or configure anything.
- You can share the project with multiple users, for example, your team members or contractors. Everyone will have their own access level and privileges.
PaaS cons
- Like IaaS, PaaS requires specialized knowledge to use.
- Less control over the computing infrastructure compared to IaaS.
- Development capabilities are limited by the features offered by the provider.
- As with IaaS, you need to be very careful when choosing your provider. Contact companies with reliable data centers. Be sure to find out if the provider can guarantee the security of your data.
PaaS with Gcore Cloud
We offer a user-friendly AI Platform to help accelerate the machine learning process of your software.
A catalog of ready-made templates and models is built into the platform, and tools for development and working with data are integrated.
We also plan to launch support for Kubernetes containers and database management in the near future.
SaaS
SaaS stands for Software as a Service.
These are completely ready-to-use applications configured to solve your problems.
Any application that you access over the internet is SaaS: email services, website builders, CRM systems, video call services, and so on.
You also don’t need to develop anything yourself. You take a ready-made solution and start using it.
What tasks is SaaS suitable for?
The company’s own applications are usually developed for end users. To solve internal problems, the development of new software is sometimes also required. But in most cases, there’s no need to reinvent the wheel.
For example, you need an email service. Why develop your own email client when you can use a ready-made solution? Or maybe you need a CRM system. There’s e a huge number of offers on the market. Surely at least one of them will fully suit your tasks.
SaaS is also used for short-term projects when you need the fastest and simplest solution and there’s no time to develop something on your own.
SaaS pros
- It’s a ready-made solution without the need to waste time on development or hire staff.
- You can focus more on reaching your business goals.
- To access a SaaS, you usually only need a browser and an internet connection.
SaaS cons
- Your capabilities are limited by the software features.
- The SaaS is controlled by the provider.
- Limited opportunities for integration with other applications and services.
SaaS with Gcore Cloud
Choose a ready-made SaaS solution from our Marketplace.
Applications we already have:
- A video call service for browsers
- Repository management systems
- Popular game servers
- An alert management system
The list is constantly growing.
Gcore Cloud is powerful and reliable. It will help your projects grow faster at a lower cost around the world.
We guarantee:
- Tier IV data centers
- 99.95% SLA with financial guarantees
- Protection against DDoS attacks
- Compliance with security standards (GDPR, PCI DSS)
Move to our cloud quickly and safely.
Or request a free consultation to get started. We’ll help you find the best solution for your project!
Related articles
Optimize your workload: a guide to selecting the best virtual machine configuration
Virtual machines (VMs) offer the flexibility, scalability, and cost-efficiency that businesses need to optimize workloads. However, choosing the wrong setup can lead to poor performance, wasted resources, and unnecessary costs.In this guide, we’ll walk you through the essential factors to consider when selecting the best virtual machine configuration for your specific workload needs.﹟1 Understand your workload requirementsThe first step in choosing the right virtual machine configuration is understanding the nature of your workload. Workloads can range from light, everyday tasks to resource-intensive applications. When making your decision, consider the following:Compute-intensive workloads: Applications like video rendering, scientific simulations, and data analysis require a higher number of CPU cores. Opt for VMs with multiple processors or CPUs for smoother performance.Memory-intensive workloads: Databases, big data analytics, and high-performance computing (HPC) jobs often need more RAM. Choose a VM configuration that provides sufficient memory to avoid memory bottlenecks.Storage-intensive workloads: If your workload relies heavily on storage, such as file servers or applications requiring frequent read/write operations, prioritize VM configurations that offer high-speed storage options, such as SSDs or NVMe.I/O-intensive workloads: Applications that require frequent network or disk I/O, such as cloud services and distributed applications, benefit from VMs with high-bandwidth and low-latency network interfaces.﹟2 Consider VM size and scalabilityOnce you understand your workload’s requirements, the next step is to choose the right VM size. VM sizes are typically categorized by the amount of CPU, memory, and storage they offer.Start with a baseline: Select a VM configuration that offers a balanced ratio of CPU, RAM, and storage based on your workload type.Scalability: Choose a VM size that allows you to easily scale up or down as your needs change. Many cloud providers offer auto-scaling capabilities that adjust your VM’s resources based on real-time demand, providing flexibility and cost savings.Overprovisioning vs. underprovisioning: Avoid overprovisioning (allocating excessive resources) unless your workload demands peak capacity at all times, as this can lead to unnecessary costs. Similarly, underprovisioning can affect performance, so finding the right balance is essential.﹟3 Evaluate CPU and memory considerationsThe central processing unit (CPU) and memory (RAM) are the heart of a virtual machine. The configuration of both plays a significant role in performance. Workloads that need high processing power, such as video encoding, machine learning, or simulations, will benefit from VMs with multiple CPU cores. However, be mindful of CPU architecture—look for VMs that offer the latest processors (e.g., Intel Xeon, AMD EPYC) for better performance per core.It’s also important that the VM has enough memory to avoid paging, which occurs when the system uses disk space as virtual memory, significantly slowing down performance. Consider a configuration with more RAM and support for faster memory types like DDR4 for memory-heavy applications.﹟4 Assess storage performance and capacityStorage performance and capacity can significantly impact the performance of your virtual machine, especially for applications requiring large data volumes. Key considerations include:Disk type: For faster read/write operations, opt for solid-state drives (SSDs) over traditional hard disk drives (HDDs). Some cloud providers also offer NVMe storage, which can provide even greater speed for highly demanding workloads.Disk size: Choose the right size based on the amount of data you need to store and process. Over-allocating storage space might seem like a safe bet, but it can also increase costs unnecessarily. You can always resize disks later, so avoid over-allocating them upfront.IOPS and throughput: Some workloads require high input/output operations per second (IOPS). If this is a priority for your workload (e.g., databases), make sure that your VM configuration includes high IOPS storage options.﹟5 Weigh up your network requirementsWhen working with cloud-based VMs, network performance is a critical consideration. High-speed and low-latency networking can make a difference for applications such as online gaming, video conferencing, and real-time analytics.Bandwidth: Check whether the VM configuration offers the necessary bandwidth for your workload. 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
10 Common Web Performance Mistakes and How to Overcome Them
Web performance mistakes can carry a high price, resulting in websites that yield low conversion rates, high bounce rates, and poor sales. In this article, we dig into the top 10 mistakes you should avoid to boost your website performance.1. Slow or Unreliable Web HostYour site speed begins with your web host, which provides the server infrastructure and resources for your website. This includes the VMs and other infrastructure where your code and media files reside. Three common host-related problems are as follows:Server location: The further away your server is from your users, the slower the site speed and the poorer the experience for your website visitors. (More on this under point 7.)Shared hosting: Shared hosting solutions share server resources among multiple websites, leading to slow load times and spotty connections during peak times due to heavy usage. Shared VMs can also impact your website’s performance due to increased network traffic and resource contention.VPS hosting: Bandwidth limitations can be a significant issue with VPS hosting. A limited bandwidth package can cause your site speed to decrease during high-traffic periods, resulting in a sluggish user experience.Correct for server and VM hosting issues by choosing a provider with servers located closer to your user base and provisioning sufficient computational resources, like Gcore CDN. Use virtual dedicated servers (VDS/VPS) rather than shared hosting to avoid network traffic from other websites affecting your site’s performance. If you already use a VPS, consider upgrading your hosting plan to increase server resources and improve UX. For enterprises, dedicated servers may be more suitable.2. 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. To optimize for speed, carefully analyze your use case to determine if a framework is necessary. If a static page suffices, avoid using a framework altogether. If a framework is needed, select libraries that allow you to link only the required components.3. Unoptimized Code Files and FontsEven high-quality code needs optimization before shipping. Unoptimized JavaScript, HTML, and CSS files can increase page weight and necessitate multiple HTTP requests, especially if JavaScript files are executed individually.To optimize code, two effective techniques are minification and bundling.Minification removes redundant libraries, code, comments, unnecessary characters (e.g., commas and dots), and formatting to reduce your source code’s size. It also shortens variable and function names, further decreasing file size. 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
How to Configure Grafana for Visualizing Kubernetes (K8s) Cluster Monitoring
Kubernetes monitoring allows you to observe your workloads and cluster resources, spot issues and failures, and efficiently manage pods and other resources. Cluster admins should prioritize tracking the performance and stability of clusters in these environments. One popular tool that can help you visualize Kubernetes monitoring is Grafana. This monitoring solution lets you display K8s metrics through interactive dashboards and real-time alerts. It seamlessly integrates with Prometheus and other data sources, providing valuable insights.Gcore Managed Kubernetes simplifies the Grafana setup process by providing a managed service that includes tools like Grafana. In this article, we’ll explain how to set up and configure Grafana to monitor Kubernetes, its key metrics, and dashboards.Setting Up Grafana for Effective Kubernetes MonitoringTo begin monitoring Kubernetes with Grafana, first, check that you have all the requirements in place: a functioning Kubernetes cluster, the Helm package manager installed, and kubectl set up to communicate with your cluster.Install Grafana in a Kubernetes Cluster. Start by adding the Grafana Helm repository.helm repo add grafana https://grafana.github.io/helm-chartshelm repo updateNext, install Grafana using Helm. This command deploys Grafana into your Kubernetes cluster:helm install grafana grafana/grafanaNow it’s time to configure Grafana for the Kubernetes environment. After installation, retrieve the admin password by using the command below:kubectl get secret --namespace default grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echoThen access the Grafana UI by port-forwarding:kubectl port-forward svc/grafana 3000:80Open your web browser and navigate to http://localhost:3000. Log in using the default username admin and the password you retrieved. Once logged in, you can configure Grafana to monitor your Kubernetes environment by adding data sources such as Prometheus and creating custom dashboards.You’ve now successfully set up Grafana for Kubernetes monitoring!Key Metrics for Kubernetes MonitoringUnderstanding metrics for Kubernetes monitoring allows you to visualize your cluster’s reliability. Key metrics are the following:Node resources. Track CPU and memory usage, disk utilization, and network bandwidth to understand resource consumption and identify bottlenecks.Cluster metrics. Monitor the number of nodes to understand resource billing and overall cluster usage, and track running pods to determine node capacity and identify failures.Pod metrics. Measure how pods are managed and deployed, including instances and deployment status, and monitor container metrics like CPU, memory, and network usage.State metrics. Keep an eye on persistent volumes, disk pressure, crash loops, and job success rates to ensure proper resource management and application stability.Container metrics. Track container CPU and memory usage relative to pod limits, and monitor network data to detect bandwidth issues.Application metrics. Measure application availability, performance, and business-specific metrics to maintain optimal user experience and operational health.Setting Up Grafana DashboardsYou can opt to design and tailor Grafana dashboards to monitor your Kubernetes cluster. This will help you better understand your systems’ performance and overall well-being at a glance.Log into Grafana. Open your web browser, go to http://localhost:3000/, and log in with the default credentials (admin for both username and password), then change your password if/when prompted.Grafana—Log In to Start MonitoringAdd data source. Navigate to Configuration and select Data Sources. Click on Add Data Source and choose the appropriate data source, such as Prometheus.Create a dashboard. Go to Create > Dashboard, click Add New Panel, choose the panel type (e.g., Time series chart, Gauge, Table), and configure it with a PromQL query and visualization settings.Adding a New Panel in Grafana DashboardOrganize and save the dashboard. Arrange panels by clicking Add Panel > Add Row and dragging panels into the desired rows. To save the dashboard, click the save icon, name it, and confirm the save.Gcore Managed Kubernetes for Kubernetes MonitoringWhether you’re getting started with monitoring Kubernetes or you’re a seasoned pro, Gcore Managed Kubernetes offers significant advantages for businesses seeking efficient and reliable Kubernetes cluster monitoring and container management:Ease of integrating Grafana: The service seamlessly integrates with Grafana, enabling effortless visualization and monitoring of performance metrics via dashboards.Automated control: Gcore Managed Kubernetes simplifies the setup and monitoring process by using automation. This service conducts health checks on your nodes, automatically updating and restarting them when needed to keep performance at its best.Enhanced security and reliability: Gcore Managed Kubernetes guarantees the management of nodes by integrating features like automatic scaling and self-repairing systems to maintain optimal performance.Discover Gcore Managed Kubernetes, including automated scaling, one-click provisioning, and Grafana integration.
Subscribe to our newsletter
Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.