In this article, we’ll deep into what cloud servers are, how they work, their types, benefits, and challenges. We’ll also cover factors to consider when choosing a cloud provider.
What Is a Cloud Server?
A cloud server is a pool of powerful virtual or dedicated physical computer resources. These resources are hosted in the public or private cloud infrastructure and can be accessed through the internet or a virtual private network (VPN.)
Despite their off-site locations, the servers in the cloud provide broadly the same functionality as the traditional on-premises physical servers. However, the cloud environment offers scalability, flexibility, and has a lower cost of entry, since it does not require upfront investments in physical infrastructure like an on-premises data center.
A typical cloud server combines high-performance components and systems in one or more locations via high-speed networks. These may be physically located in one or more machines or distributed across several networks. For example, a physical server cluster may provide the CPU and memory (RAM) while the storage is in another location or system, such as network-attached storage (NAS.)
While shared cloud server hosting is adequate for many organizations, it may not suit high-traffic applications or those dealing with sensitive data. So, those projects that require more resources or to be isolated from noisy neighbors find private cloud to be a better fit. Healthcare, banks, and other resource-intensive industries, such as gaming websites, video streaming, and research organizations, prefer private cloud servers where resources are physically isolated from all other users.
Types of Cloud Servers
Cloud servers can be categorized into three types, based on where they reside: public, private, and hybrid.
Public Cloud Server
Public cloud servers are computer resources hosted on a public cloud infrastructure, which means they are owned, managed, and maintained by a third-party service provider. The provider makes all resources available to the public via the internet. For example, Gcore offers bare metal servers, virtual machines, and Kubernetes containers.
Private Cloud Server
A private or dedicated cloud server is a cloud architecture where resources are only available to a single organization. The cloud provider installs the dedicated cloud server, such as Gcore’s Bare Metal, in a private region. Private servers provide better performance, control, and security, making them suitable for highly regulated industries, high-traffic websites, or running mission-critical or sensitive applications.
In-House Versus Third-Party Private Cloud Servers
Organizations can host private cloud servers either in-house or in a third-party cloud environment. In the latter case, the provider owns and manages the hardware and software resources, including the supporting infrastructure. The choice depends on objectives regarding control, convenience, cost, and flexibility. Third-party cloud hosting offers less control, but deploying and scaling are faster, simpler, and more cost-effective.
When the private cloud operates from the in-house data center, users access it over the local area network (LAN.) External users or remote offices can access the server over a private network such as a VPN or the organization’s intranet.
Hybrid Cloud Server
Hybrid cloud servers are a mix of public cloud and on-premises or private infrastructure. Hybrid cloud offers the best performance, flexibility, availability, and scalability. A hybrid cloud server enables businesses to take advantage of the best of these two environments to achieve optimal outcomes regarding compliance, availability, and security. A typical configuration could involve an off-site public cloud server combined with an in-house or third-party-hosted private cloud server. An example of a hybrid cloud server is Gcore’s Virtual Private Cloud.
Read: What is a bare metal server? | How does it work?
How Does a Cloud Server Work?
Public and private cloud servers work like traditional on-premises physical servers. They provide processing power, run applications, store data, and perform other functions like running databases, mail servers, etc. However, unlike the in-house servers that run locally from the in-house data center, most cloud servers are hosted and managed by third-party cloud computing providers. They are only accessible over the internet or a virtual private network (VPN.)
Most public cloud servers are software-defined using a virtualization process that creates multiple virtual machines on a single physical server. Providers may use a hypervisor or other virtualization technology to abstract the CPU, memory, storage, and other server resources.
For shared public cloud servers, the virtualization technology pools the configured resources to create multiple cloud servers that function like physical machines, but are only accessible via the Internet. This allows customers to share the hosting server’s compute, memory, storage, and other resources. However, each can access a separate instance with the individual operating system. Clients on the same physical server can run different applications and store data independently.
Cloud computing service providers usually invest in, manage, and maintain a high-capacity infrastructure, benefiting from the economies of scale. Consequently, they can pass on these benefits to customers and offer reliable, high-performance servers at lower costs than clients would have spent deploying equivalent systems in their data centers.
Most cloud computing providers have self-service portals that potential customers use to create and deploy cloud servers in less than ten minutes. Automated systems enable the customers to choose predefined servers or configure custom CPU, storage, memory, and other resources that align with their specific requirements and budget.
Who Should Use Cloud Servers, and Why?
The cloud computing delivery model is suitable for all types and sizes of workloads. Since it allows deployment of powerful virtual machines and bare metal servers without the high upfront costs associated with an on-premises system. The flexibility and affordability of cloud’s pay-as-you-go pricing plans enable everyone—including those with limited budgets—to deploy cloud servers that meet their specific needs.
By contrast, setting up an equivalent in-house system would mean installing physical servers and adequate storage in the organization’s data center. This capital-intensive exercise would require significant time and skill to install, configure, and manage, making it impractical and unaffordable for those with inadequate resources.
To understand the problem users of on-premises servers face is to imagine an organization with seasonal traffic spikes—a good example is an e-commerce platform with traffic spikes during the sales season, such as Black Friday.
The company may invest in large local infrastructures with enough capacity to handle traffic spikes, but those expensive resources remain idle most of the time. A cloud server can reduce deployment costs, time, and overprovisioning of resources while improving efficiencies and revenues.
In this article, we’ll discover what a cloud server is, the different types of cloud servers, how a cloud server works, its many benefits and limited challenges, and why cloud is essential for most individuals and organizations.
What Are the Benefits of Cloud Servers?
The specific benefits of a cloud server vary depending on the organization’s objectives, workload, and type of server and plan. However, numerous common advantages to individuals and businesses are worth a closer look.
Low Deployment and Maintenance Costs
Customers can rent virtual cloud servers and storage at a much lower deployment and maintenance cost. This results in savings in CapEx, space, and running costs, such as highly skilled in-house staff, electricity, cooling, and other requirements for maintaining an on-premises system.
Highly Scalable and Flexible Computing Resources
Servers in the cloud are highly scalable, with an almost unlimited capacity to process resource-intensive workloads and store vast quantities of data. Most providers offer flexible plans, enabling users to choose the appropriate configuration and operating system based on workloads. Customers can quickly and easily create or remove the servers or resources depending on current needs, demand, and budget.
Cloud computing providers often invest in vast capacities so that customers can add resources to meet temporary high-demand workloads and then remove them when demand is low. When a configured cloud server capacity cannot handle a particular workload, the customer organization can scale by quickly adding resources. Cloud servers scale up or down to match the current workload quickly and easily.
Easy to Deploy and Use
Provisioning a cloud server is almost instant. It usually takes less than ten minutes to configure and start using a virtual instance. Installation, physical upgrades, and maintenance associated with on-premises infrastructure are all taken care of by the cloud computing provider.
Global Access and Universal Workload Capability
Cloud servers provide the same functions as on-premises servers. However, they are accessible by more users regardless of their location. An on-site server is only accessible by the users on the company’s network. However, a server in the cloud is accessible through the internet, hence the ability to serve a broader user base.
Internet connectivity enables cloud servers to handle global workloads. However, unexpected disruptions, instabilities, and network latency can degrade performance for users, especially in locations with less reliable internet connectivity. To overcome this, cloud service providers can create duplicate instances to process workloads on servers in different locations, thus ensuring faster connectivity, continuity, and better user experience.
Affordable Pricing Plans
Cloud service providers offer a wide variety of pricing plans based on the specifications. Customers can opt for annual, monthly, or pay-as-you-go payment plans. There are usually different commitment plans available, commonly for three or six months or one or two years. In general, cloud services are charged at an hourly rate. Customers pay for the resources used at the end of the month.
Besides the annual and monthly rates, Gcore offers per-hour billing on production-grade instances designed for heavy workloads, complex calculations, and other high CPU-intensive applications. Per-hour billing is suitable for customers who do not want to sign up for monthly or annual contracts. Typical rates range from $0.03496/hr for a 2vCPU, with 2GB RAM, to $2.9/hr for a 32vCPU, 128GB RAM, and 4GPU.
The overall cost of an instance depends on the allocated computing resources, operating system, and usage time, which are calculated from the moment use begins until it is completely stopped. Storage and network usage are charged separately.
Flexible Cloud Server Specifications
Cloud servers allow client customization to suit unique business needs and use cases. While the providers offer some cloud servers with standard specifications, they also allow the customization of various resources.
Highly Available and Reliable
Cloud servers are highly available, and most providers guarantee 99% uptime. As such, it is rare to experience frequent and lengthy downtimes. Unlike a traditional on-premises server that may experience downtime and take time to fix, cloud servers have several redundant components and faster recovery features, which make them highly available and reliable.
Top-Notch Security
Cloud computing providers use highly effective security solutions to protect the infrastructure and data, and have skilled IT security teams to ensure the servers’ constant protection. They also observe security best practices, such as updating the software and tools.
Read: What is Latency? | How to Reduce Latency
What Are the Challenges of Using Cloud Servers?
Before migrating to the cloud, it is essential to evaluate the challenges you are likely to experience. Issues to consider include performance, security reliability, availability, and more. These may differ from one deployment and cloud server type to the other. However, some common issues include cloud server performance issues, meeting regulations, cloud outages, a lack of direct server control, and incompatibility. Let’s review each one in depth.
Cloud Server Performance Issues
Shared or “logical” cloud servers reside in multi-tenant environments. You don’t have direct control over the VM, its location, or other cloud servers on the same physical machine. As such, excessive usage of resources such as network and storage by other tenants may degrade the performance of your server.
One way of overcoming performance degradation is to migrate your workloads to regions with fewer activities. But the ultimate solution is to move to a server with more capacity. The cost of such a server depends on the type and configuration, with dedicated servers being costlier than virtual servers.
For example, Gcore virtual servers that support Linux and Windows operating systems start at $4.99. The entry-level servers have 1vCPU, 512MB RAM, and 20GB disk, and you can adjust the specifications to match your workload. A dedicated server with an E-2236 Intel Xeon CPU, 32 GB RAM, and 2x 480 SSD disks starts at $206 per month and guarantees outstanding performance without exception, even for highly demanding workloads. A user who needs the full capacity of Gcore’s top-end cloud server could experience performance issues if they tried to put that same workload through an entry-level server, which is why it’s important to pick the option that matches your use case. Gcore’s experts can help with that.
Regulations, Compliance, and Governance
Data privacy rules, regulations, and other industry standards mean that cloud users can come up against challenging differences between geographical locations, which limit options for cloud servers. For example, if regulations or industry standards demand that the organization must store its data within its political and geographic boundaries, this means a limited choice of cloud servers.
Unexpected Cloud Server Outages
Cloud servers may experience unexpected outages due to a problem within the service provider’s infrastructure or as a result of internet issues. Since the servers are hosted at a physical distance, customers have no way to remedy outages and are reliant on the cloud provider for both information and a fix.
Such problems may include network issues or slow performance due to spikes in demand. Additionally, unexpected network issues could be beyond even the provider’s control. To safeguard against such a challenge, organizations may opt to keep the critical workloads in on-premises servers. Choosing a reliable cloud provider, such as Gcore, can help to limit such outages.
Limited Direct Control of the Server
The cloud provider manages the cloud infrastructure, including the type of hardware components and location. This leaves you unable to optimize the systems for the best performance, since you have no direct control of the server.
Incompatibility
If the cloud servers only support some of your operating systems, workloads, and applications, you may experience compatibility issues. Selecting a cloud provider and planning carefully upfront can help mitigate this problem but does not always account for future additional or changed needs.
Factors to Consider When Looking for a Cloud Server
The choice of a cloud server depends on a combination of factors, such as your workload, budget, server technology, and providers’ reputations. Below are the key issues to consider.
Type of Cloud Server to Deploy
Virtual machines are budget friendly and easier to manage. However, performance issues are always a threat, especially when other tenants have a high demand for resources on the same physical machine. As such, they are suitable for less resource-intensive and non-critical workloads. If you want a third-party vendor-controlled virtualized layer, this is the right choice.
If you need more resources and your workload requires an entire system built from the ground up, then you need a bare metal server. Bare metal offers superior resources compared to VM, and other clients’ demands do not affect bare metal. However, bare metal is costlier than VM. You can host the bare metal server on-premises or on a third-party provider’s infrastructure.
Type of Server Virtualization Technology
Providers may use hardware or software virtualization technologies to subdivide a physical server into several virtual machines. In a logical cloud server, virtualization abstracts resources such as the CPU, storage, memory, and network from the physical hardware, so that services and applications can function without depending so much on the hardware.
Besides hypervisor-based virtualization, providers can use other technologies such as OS-level virtualization, hardware virtualization, hardware-assisted virtualization, etc. It’s important to check whether the virtualization technology is suitable for your operating system, applications, and workloads.
Cloud Server Security
Securing your users, workloads, and data on the cloud server should be a priority. This prevents or reduces risks of cyberattacks, DDoS attacks, ransomware, and other security issues that could compromise your systems and lead to performance degradation and even financial and reputation losses for your organization.
Unlike an on-premises data center, where you can protect your digital assets using perimeter security and other technologies, you have less control over the cloud-based server. Instead, you have to rely more on the security measures of the cloud service provider.
It is, therefore, vital to evaluate their technologies and ensure the provider can adequately secure your cloud server. However, cloud security is a shared responsibility between the provider and customers. As such, you still need to deploy various security measures, policies, and practices to protect your users, applications, and data.
Cost-Effective Cloud Services
Good value doesn’t always mean picking the cheapest option available. Select a cloud service that delivers the best features at a fair price. Customizing and optimizing the server comes with added costs, but ensures better performance and security of your workloads and data. It’s also worth paying attention to the available pricing plans, their flexibility, and their compensation policy if you decide to move away before the contract ends.
Support for Private Cloud Server
The public cloud servers offer some level of cost-savings and convenience. However, compliance and data governance requirements may require deploying a private cloud server with resources fenced off from other users. In such a case, you need a provider who supports the private or hybrid cloud server system while offering favorable customization options.
Numerous and Diverse Cloud Features
It is vital to look for a provider who offers a service that meets your current and future needs. Choose a provider offering features such as automatic scaling, load balancing, easy server upgrades, backups, and disaster recovery so that you can feel confident in your efficiency, performance, and continuity.
Reliable and Prompt Support
A good provider must offer reliable support to minimize downtimes. Ideally, look for a provider that provides 24/7 support. Check the reviews to see what other customers have experienced when it comes to support from your potential provider.
Compliance with Regulatory Standards
If you are in an industry that requires compliance with regulations such as GDPR, CCPA, ISO/EIC 27 001, HIPAA, PCI-DSS, and others, look for a compliant provider.
Seamless Migration to the Cloud
Whether switching from an on-premises data center or another cloud provider, your new cloud service provider should assist you in the migration process. Such support ensures a smooth transition and minimal interruption or downtime.
Cloud Server Customization Options
If you anticipate that your business will grow rapidly or experience temporary demand spikes, look for a scalable and customizable service where you can add or remove resources to meet current and future needs. Auto-scaling features enable the automatic addition or removal of server resources, ensuring that you can easily and quickly process any workload while only paying for the computing resources that you actually use.
Should You Switch to a Cloud Server?
Most individuals and organizations benefit from switching to the cloud because it offers cost-effective, scalable, and reliable on-demand computing services without an upfront cost.
However, public cloud is not always the best choice, especially for use cases involving unusually sensitive data (such as healthcare or finance companies) or extreme performance computing requirements. For sensitive workloads, Gcore offers highly secure virtual cloud servers with Intel SGX support. The technology provides extra security such that only authorized users can access the encrypted data.
An assessment of your specific needs and circumstances is important before deciding to switch to cloud, but in almost all cases, it’s a move worth making.
Examples of Cloud Servers
Most organizations and individuals are using cloud servers in one way or another. Some use cases include email, file storage, and web applications. The cloud servers power major platforms and diverse application types, such as Software as a Service (SaaS,) Infrastructure as a Service (IaaS,) and Platform as a Service (PaaS.) Let’s take a look at two examples: Dropbox and Gcore.
Dropbox is a popular web-based file storage, sharing, and synchronization platform. It allows users to store files from their computers and access them from anywhere over the internet and supports real-time collaboration.
Gcore provides customizable cloud computing, CDN, infrastructure, security, and other services to meet computing needs across all industries. These include:
Conclusion
Cloud servers provide customers with a cost-effective solution to meet varying workloads on demand. We have learned about the cloud-based server, how it works, its benefits, and the type to deploy. The next step is to look for a reliable provider with the capacity and ability to meet your needs, which means balancing a number of complex factors.
Choosing the right cloud service provider is critical to achieving your organization’s objectives and success. It’s vital to opt for a service that meets both your current and future needs. At Gcore, we offer high-performance, scalable, affordable cloud servers to support customers and industries with different needs and workloads.
To learn more about Gcore, check out our cloud server products or talk to one of our experts for a free consultation on deploying your cloud servers and improving your operations.
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Choosing the right type of server infrastructure is critical to how your application performs, scales, and fits your budget. For most workloads, the decision comes down to two core options: bare metal servers and virtual machines (VMs). Both can be deployed in the cloud, but they differ significantly in terms of performance, control, scalability, and cost.In this article, we break down the core differences between bare metal and virtual servers, highlight when to choose each, and explain how Gcore can help you deploy the right infrastructure for your needs. If you want to learn about either BM or VMs in detail, we’ve got articles for those: here’s the one for bare metal, and here’s a deep dive into virtual machines.Bare metal vs. virtual machines at a glanceWhen evaluating whether bare metal or virtual machines are right for your company, consider your specific workload requirements, performance priorities, and business objectives. 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You get exclusive access and control over the hardware via the cloud provider, which offers the stability and security needed for high-demand applications.The benefits of bare metal serversHere are some of the business advantages of opting for a bare metal server:Maximized performance: Because they are dedicated resources, bare metal servers provide top-tier performance without sharing processing power, memory, or storage with other users. This makes them ideal for resource-intensive applications like high-performance computing (HPC), big data processing, and game hosting.Greater control: Since you have direct access to the hardware, you can customize the server to meet your specific requirements. This is especially important for businesses with complex, specialized needs that require fine-tuned configurations.High security: Bare metal servers offer a higher level of security than their alternatives due to the absence of virtualization. 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How to get the size of a directory in Linux
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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
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