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Developers

What are zero-day attacks? Risks, prevention tips, and new trends

Zero-day attack is a term for any attack that targets a vulnerability in software or hardware that has yet to be discovered by the vendor or developer. The term “zero-day” stems from the idea that the developer has had zero days to address or patch the vulnerability before it is exploited.In a zero-day attack, an attacker finds a vulnerability before a developer discovers and patches itThe danger of zero-day attacks lies in their unknownness. Because the vulnerabilities they target are undiscovered, traditional defense mechanisms or firewalls may not detect them as no specific patch exists, making attack success rates higher than for known attack types. This makes proactive and innovative security measures, like AI-enabled WAAP, crucial for organizations to stay secure.Why are zero-day attacks a threat to businesses?Zero-day attacks pose a unique challenge for businesses due to their unpredictable nature. Since these exploits take advantage of previously unknown vulnerabilities, organizations have no warning or time to deploy a patch before they are targeted. This makes zero-day attacks exceptionally difficult to detect and mitigate, leaving businesses vulnerable to potentially severe consequences. As a result, zero-day attacks can have devastating consequences for organizations of all sizes. They pose financial, reputational, and regulatory risks that can be difficult to recover from, including the following:Financial and operational damage: Ransomware attacks leveraging zero-day vulnerabilities can cripple operations and lead to significant financial losses due to data breach fines. According to recent studies, the average cost of a data breach in 2025 has surpassed $5 million, with zero-day exploits contributing significantly to these figures.Reputation and trust erosion: Beyond monetary losses, zero-day attacks erode customer trust. A single breach can damage an organization’s reputation, leading to customer churn and lost opportunities.Regulatory implications: With strict regulations like GDPR in the EU and similar frameworks emerging globally, organizations face hefty fines for data breaches. Zero-day vulnerabilities, though difficult to predict, do not exempt businesses from compliance obligations.The threat is made clear by recent successful examples of zero-day attacks. The Log4j vulnerability (Log4Shell), discovered in 2021, affected millions of applications worldwide and was widely exploited. In 2023, the MOVEit Transfer exploit was used to compromise data from numerous government and corporate systems. These incidents demonstrate how zero-day attacks can have far-reaching consequences across different industries.New trends in zero-day attacksAs cybercriminals become more sophisticated, zero-day attacks continue to evolve. New methods and technologies are making it easier for attackers to exploit vulnerabilities before they are discovered. The latest trends in zero-day attacks include AI-powered attacks, expanding attack surfaces, and sophisticated multi-vendor attacks.AI-powered attacksAttackers are increasingly leveraging artificial intelligence to identify and exploit vulnerabilities faster than ever before. AI tools can analyze vast amounts of code and detect potential weaknesses in a fraction of the time it would take a human. Moreover, AI can automate the creation of malware, making attacks more frequent and harder to counter.For example, AI-driven malware can adapt in real time to avoid detection, making it particularly effective in targeting enterprise networks and cloud-based applications. Hypothetically, an attacker could use an AI algorithm to scan for weaknesses in widely used SaaS applications, launching an exploit before a patch is even possible.Expanding attack surfacesThe digital transformation continues to expand the attack surface for zero-day exploits. APIs, IoT devices, and cloud-based services are increasingly targeted, as they often rely on interconnected systems with complex dependencies. A single unpatched vulnerability in an API could provide attackers with access to critical data or applications.Sophisticated multi-vector attacksCybercriminals are combining zero-day exploits with other tactics, such as phishing or social engineering, to create multi-vector attacks. This approach increases the likelihood of success and makes defense efforts more challenging.Prevent zero-day attacks with AI-powered WAAPWAAP solutions are becoming a cornerstone of modern cybersecurity, particularly in addressing zero-day vulnerabilities. Here’s how they help:Behavioral analytics: WAAP solutions use behavioral models to detect unusual traffic patterns, blocking potential exploits before they can cause damage.Automated patching: By shielding applications with virtual patches, WAAP can provide immediate protection against vulnerabilities while a permanent fix is developed.API security: With APIs increasingly targeted, WAAP’s ability to secure API endpoints is critical. It ensures that only authorized requests are processed, reducing the risk of exploitation.How WAAP stops AI-driven zero-day attacksAI is not just a tool for attackers—it is also a powerful ally for defenders. Machine learning algorithms can analyze user behavior and network activity to identify anomalies in real time. These systems can detect and block suspicious activities that might indicate an attempted zero-day exploit.Threat intelligence platforms powered by AI can also predict emerging vulnerabilities by analyzing trends and known exploits. This enables organizations to prepare for potential attacks before they occur.At Gcore, our WAAP solution combines these features to provide comprehensive protection. By leveraging cutting-edge AI and machine learning, Gcore WAAP detects and mitigates threats in real time, keeping web applications and APIs secure even from zero-day attacks.More prevention techniquesBeyond WAAP, layering protection techniques can further enhance your business’ ability to ward off zero-day attacks. Consider the following measures:Implement a robust patch management system so that known vulnerabilities are addressed promptly.Conduct regular security assessments and penetration testing to help identify potential weaknesses before attackers can exploit them.Educate employees about phishing and other social engineering tactics to decease the likelihood of successful attacks.Protect your business against zero-day attacks with GcoreZero-day attacks pose a significant threat to businesses, with financial, reputational, and regulatory consequences. The rise of AI-powered cyberattacks and expanding digital attack surfaces make these threats even more pressing. Organizations must adopt proactive security measures, including AI-driven defense mechanisms like WAAP, to protect their critical applications and data. By leveraging behavioral analytics, automated patching, and advanced threat intelligence, businesses can minimize their risk and stay ahead of attackers.Gcore’s AI-powered WAAP provides the robust protection your business needs to defend against zero-day attacks. With real-time threat detection, virtual patching, and API security, Gcore WAAP ensures that your web applications remain protected against even the most advanced cyber threats, including zero-day threats. Don’t wait until it’s too late—secure your business today with Gcore’s cutting-edge security solutions.Discover how WAAP can help stop zero-day attacks

April 1, 2025 4 min read
Developers

What are virtual machines?

A virtual machine (VM), also called a virtual instance, is a software-based version of a physical computer. Instead of running directly on hardware, a VM operates inside a program that emulates a complete computer system, including a processor, memory, storage, and network connections. This allows multiple VMs to run on a single physical machine, each with its own operating system and applications, as if they were independent computers.VMS are useful because they provide flexibility, isolation, and scalability. Since each VM is self-contained, it can run different operating systems (like Windows, Linux, or macOS) on the same hardware without affecting other VMs or the host machine. This makes them ideal for testing software, running legacy applications, or efficiently using server resources in data centers. Because VMs exist as software, they can be easily copied, moved, or backed up, making them a powerful tool for both individuals and businesses.Read on to learn about types of VMs, their benefits, common use cases, and how to choose the right VM provider for your needs.How do VMs work?A virtual machine (VM) runs inside a program called a hypervisor, which acts as an intermediary between the VM and the actual computer hardware. Every time a VM needs to perform an action—such as running software, accessing storage, or using the processor—the hypervisor intercepts these requests and decides how to allocate resources like CPU power, memory, and disk space. You can think of a hypervisor as an operating system for VMs, managing multiple virtual machines on a single physical computer. Popular hypervisors like VirtualBox and VMware enable users to run multiple operating systems simultaneously while providing strong isolation.Modern hypervisors optimize performance by giving VMs direct access to certain hardware components when possible, reducing the need for constant intervention. However, some level of overhead remains because the hypervisor still needs to manage and coordinate resources efficiently. This means that while VMs can leverage most of the system’s hardware, they can’t use 100% of it, as some processing power is always reserved for managing virtualization itself. This small trade-off is often worth it, as hypervisors keep each VM isolated and secure, preventing one VM from interfering with another.VM layersFigure 1 illustrates the layers of a system virtual machine setup. The layer model can vary depending on the hypervisor. Some hypervisors include a built-in host operating system, while modern hardware offers native virtualization support. Many hypervisors can also manage multiple physical machines and VMs efficiently.VM snapshots are an essential feature in cloud computing, allowing users to quickly restore a virtual machine to a previous state.Figure 1: Layers of system virtual machinesHypervisors that emulate hardware architectures different from what the guest OS expects have a bigger overhead, as they can’t relay commands directly to the hardware without first translating them.VM snapshotsVM snapshots are an essential feature in cloud computing, allowing users to quickly restore a virtual machine to a previous state. The hypervisor can save the complete state of the VM and restore it at a later time to skip the boot process of the guest OS. The hypervisor can also move these snapshots between different physical machines, making the software running in the VM completely independent from the underlying hardware.What are the benefits of using VMs?Virtual machines offer benefits including resource efficiency, isolation, simplified operations, easy migration, faster deployment, cost savings, and security. Let’s look at these one by one.Multiple VMs can run on a single physical machine, making sharing resources between various guest operating systems easier. This is especially important when each guest OS needs to be isolated from the others, such as when they belong to different customers of a cloud service provider. Sharing resources through VMs makes running a server cheaper because you don’t have to buy or rent a whole physical machine, but only parts of it.Since VMs abstract the underlying hardware, they also improve resilience. If the physical machine fails, the hypervisor can perform a quick recovery by moving the snapshots to another machine without changing the guest OS installations to minimize downtime. This abstraction also allows operations teams to focus their deployment efforts on a standardized VM instead of considering different physical implementations.Migrations become easier with snapshots as you can simply move them to a faster machine without modifying the software running inside the VM.Faster deployments are possible because starting a VM is just a software execution instead of setting up a physical server in a data center. While you had to buy a server or rent it for months, with fast deployments, you can now rent a machine for hours, minutes, or even seconds, which allows for quite some savings.Modern CPUs have built-in virtualization features that enable easy resource sharing and enforce the isolation at the hardware layer. This prevents the services of one VM from accessing the resources of the others, improving security compared to running multiple apps inside one OS.Common use cases for VMsVMs have a range of use cases. Let’s look at the most popular ones.Cloud computingThe most popular use case is cloud computing, where VMs allow the secure sharing of the cloud provider’s resources, enabling their customers to rent only the resources they need for the period their workload will run.Software development and testingSoftware development often requires specific tools and libraries that aren’t available on a production machine, so having a development VM with all these tools preinstalled can be helpful. An example is cloud IDEs, which look and feel like regular IDEs but run on a cloud VM. A developer can have one for each project with the required dev tools installed.VMs also allow a developer to set up a machine for software testing that looks exactly like the production environment. Here, the opposite of the development VM is required; it should not have any development tools installed because they would also be missing from production.Cross-platform developmentA special case of the software development use case is cross-platform development. When you implement an app for Android or iOS, for example, you usually don’t do this on a mobile device but on your computer. With VMs, developers can simulate different hardware environments, enabling cross-platform testing without requiring physical devices.Legacy system supportIf the hardware your application requires is no longer in production, a VM might be the only way to keep running your software without reimplementing it. This is similar to the cross-platform development use case, as the VM emulates different hardware, but the difference is that the hardware no longer exists.How to choose the right VM providerTo find the right provider for your workload, the most important factor to assess is your own workload requirements. Ask the following questions and compare the answers to what providers offer.Is your workload compute or I/O-bound?Many workloads, like web servers, are I/O-bound. They don’t make complex calculations but rather simply load data and send it over the network. If you need a VM for an I/O-bound workload, you care more about disk and memory size, as well as network speed.However, compute-heavy workloads, such as AI inference or Kubernetes clusters, require careful resource allocation. If you’re evaluating whether to run Kubernetes on bare metal or VMs, check out our white paper on Bare Metal vs. VM-based Kubernetes Clusters for an in-depth comparison.If your workload is compute-bound instead, you need a high-performance CPU or a GPU and loads of memory. An AI inference engine, for example, only sends a bit of text to a client, but it does many calculations to generate this text.How long will your workload run?Web servers usually run indefinitely, but some workloads only run a few hours or minutes. If you’re doing AI training, you don’t want to pay for your huge VM cluster 24/7 if it only runs a few hours or days a week. In such cases, looking for a provider that allows renting your desired VM type hourly on a pay-as-you-go model might be worthwhile.Certain cloud providers offer cost-effective spot instances, which provide lower prices for non-critical workloads that can tolerate interruptions. These cheap VMs can get shut down at any time with minimal notice, but if your calculations aren’t time-critical, you might save quite a bit of money here.How does your workload scale?Scaling in the cloud is usually done horizontally. That is, by adding more VMs and distributing the work between them. Workloads can have different requirements for when and how fast they must be added and removed.In the AI training example, you might know in advance that one training takes more resources than the other, so you can provision enough VMs when starting. However, a web server workload might change its requirements constantly. Hence, you need a load balancer that automatically scales the instances up and down depending on the number of clients that want to access your service.Do you handle sensitive data?You might have to comply with specific laws and regulations depending on your jurisdiction(s) and industry. This means you must check whether the cloud provider also complies. How secure are their data centers? Where are they located? Do they support encryption in transit, at rest, and in process?What are your reliability requirements?Reliability is a question of costs and, again, of compliance. You might get into financial or regulatory troubles if your workload can’t run. Cloud providers often boast about their guaranteed uptimes, but remember that 99% uptime a year still means over three days of potential downtime. Check your needs and then seek a provider that can meet them reliably.Do you need customer support?If your organization doesn’t have the know-how for operating VMs in the cloud, you might need technical support from the provider. Most cloud providers are self-service, offering you a GUI and an API to manage resources. If your business lacks the resources to operate VMs, seek out a provider that can manage VMs on your behalf.SummaryVMs are a core technology for cloud computing and software development alike. They enable efficient resource sharing, improve security with hardware-enforced guest isolation, and simplify migration and disaster recovery. Choosing the right VM provider starts with understanding your workload requirements, from resource allocation to security and scalability.Maximize cloud efficiency with Gcore Virtual Machines—engineered for high performance, seamless scalability, and enterprise-grade security at competitive pricing. Whether you need to run workloads at scale or deploy applications in seconds, our VMs provide enterprise-grade security, built-in resilience, and optimized resource allocation, all powered by cutting-edge infrastructure. With global reach, fast provisioning, egress traffic included, and pay-as-you-go pricing, you get the scalability and reliability your business needs without overspending. Start your journey with Gcore VMs today and experience cloud computing that’s built for speed, security, and savings.Discover Gcore VMs

February 26, 2025 6 min read
Developers

Why do bad actors carry out Minecraft DDoS attacks?

One of the most played video games in the world, Minecraft, relies on servers that are frequently a target of distributed denial-of-service (DDoS) attacks. But why would malicious actors target Minecraft servers? In this article, we’ll look at why these servers are so prone to DDoS attacks and uncover the impact such attacks have on the gaming community and broader cybersecurity landscape. For a comprehensive analysis and expert tips, read our ultimate guide to preventing DDoS attacks on Minecraft servers.Disruption for financial gainFinancial exploitation is a typical motivator for DDoS attacks in Minecraft. Cybercriminals frequently demand ransom to stop their attacks. Server owners, especially those with lucrative private or public servers, may feel pressured to pay to restore normalcy. In some cases, bad actors intentionally disrupt competitors to draw players to their own servers, leveraging downtime for monetary advantage.Services that offer DDoS attacks for hire make these attacks more accessible and widespread. These malicious services target Minecraft servers because the game is so popular, making it an attractive and easy option for attackers.Player and server rivalriesRivalries within the Minecraft ecosystem often escalate to DDoS attacks, driven by competition among players, servers, hosts, and businesses. Players may target opponents during tournaments to disrupt their gaming experience, hoping to secure prize money for themselves. Similarly, players on one server may initiate attacks to draw members to their server and harm the reputation of other servers. Beyond individual players, server hosts also engage in DDoS attacks to disrupt and induce outages for their rivals, subsequently attempting to poach their customers. On a bigger scale, local pirate servers may target gaming service providers entering new markets to harm their brand and hold onto market share. These rivalries highlight the competitive and occasionally antagonistic character of the Minecraft community, where the stakes frequently surpass in-game achievements.Personal vendettas and retaliationPersonal conflicts can occasionally be the source of DDoS attacks in Minecraft. In these situations, servers are targeted in retribution by individual gamers or disgruntled former employees. These attacks are frequently the result of complaints about unsolved conflicts, bans, or disagreements over in-game behavior. Retaliation-driven DDoS events can cause significant disruption, although lower in scope than attacks with financial motivations.Displaying technical masterySome attackers carry out DDoS attacks to showcase their abilities. Minecraft is a perfect testing ground because of its large player base and community-driven server infrastructure. Successful strikes that demonstrate their skills enhance reputations within some underground communities. Instead of being a means to an end, the act itself becomes a badge of honor for those involved.HacktivismHacktivists—people who employ hacking as a form of protest—occasionally target Minecraft servers to further their political or social goals. These attacks are meant to raise awareness of a subject rather than be driven by personal grievances or material gain. To promote their message, they might, for instance, assault servers that are thought to support unfair policies or practices. This would be an example of digital activism. Even though they are less frequent, these instances highlight the various reasons why DDoS attacks occur.Data theftMinecraft servers often hold significant user data, including email addresses, usernames, and sometimes even payment information. Malicious actors sometimes launch DDoS attacks as a smokescreen to divert server administrators’ attention from their attempts to breach the server and steal confidential information. This dual-purpose approach disrupts gameplay and poses significant risks to user privacy and security, making data theft one of the more insidious motives behind such attacks.Securing the Minecraft ecosystemDDoS attacks against Minecraft are motivated by various factors, including personal grudges, data theft, and financial gain. Every attack reveals wider cybersecurity threats, interferes with gameplay, and damages community trust. Understanding these motivations can help server owners take informed steps to secure their servers, but often, investing in reliable DDoS protection is the simplest and most effective way to guarantee that Minecraft remains a safe and enjoyable experience for players worldwide. By addressing the root causes and improving server resilience, stakeholders can mitigate the impact of such attacks and protect the integrity of the game.Gcore offers robust, multi-layered security solutions designed to shield gaming communities from the ever-growing threat of DDoS attacks. Founded by gamers for gamers, Gcore understands the industry’s unique challenges. Our tools enable smooth gameplay and peace of mind for both server owners and players.Want an in-depth look at how to secure your Minecraft servers?Download our ultimate guide

February 3, 2025 3 min read
Developers

How to deploy DeepSeek 70B with Ollama and a Web UI on Gcore Everywhere Inference

Large language models (LLMs) like DeepSeek 70B are revolutionizing industries by enabling more advanced and dynamic conversational AI solutions. Whether you’re looking to build intelligent customer support systems, enhance content generation, or create data-driven applications, deploying and interacting with LLMs has never been more accessible.In this tutorial, we’ll show you exactly how to set up DeepSeek 70B using Ollama and a Web UI on Gcore Everywhere Inference. By the end, you’ll have a fully functional environment where you can easily interact with your custom LLM via a user-friendly interface. This process involves three simple steps: deploying Ollama, deploying the web UI, and configuring the web UI and connecting to Ollama.Let’s get started!Step 1: Deploy OllamaLog in to Gcore Everywhere Inference and select Deploy Custom Model.In the model image field, enter ollama/ollama.Set the Port to 11434.Under Pod Configuration, configure the following:Select GPU-Optimized.Choose a GPU type, such as 1×A100 or 1×H100.Choose a region (e.g., Luxembourg-3).Set an autoscaling policy or use the default settings.Name your deployment (e.g., ollama).Click Deploy model on the right side of the screen.Once deployed, you’ll have an Ollama endpoint ready to serve your model.Step 2: Deploy the Web UI for OllamaGo back to the Gcore Everywhere Inference console and select Deploy Custom Model again.In the Model Image field, enter ghcr.io/open-webui/open-webui:main.Set the Port to 8080.Under Pod Configuration, set:CPU-Optimized.Choose 4 vCPU / 16 GiB RAM.Select the same region as before (e.g., Luxembourg-3).Configure an autoscaling policy or use the default settings.Name your deployment (e.g., webui).Click Deploy model on the right side of the screen.Once deployed, navigate to the Web UI endpoint from the Gcore Customer Portal.Step 3: Configure the Web UIFrom the Web UI endpoint and set up a username and password when prompted.Log in and navigate to the admin panel.Go to Settings → Connections → Disable the OpenAI API integration.In the Ollama API field, enter the endpoint for your Ollama deployment. You can find this in the Gcore Customer Portal. It will look similar to this: https://<your-ollama-deployment>.ai.gcore.dev/.Click Save to confirm your changes.Step 4: Pull and Use DeepSeek 70BOpen the chat section in the Web UI.In the Select a model field, type deepseek-r1:70b.Click Pull to download the model.Wait for the download to complete.Once downloaded, select the model and start chatting!Your AI environment is ready to exploreBy following these steps, you’ve successfully deployed DeepSeek 70B on Gcore Everywhere Inference with Ollama. This setup provides a powerful and user-friendly environment for experimenting with LLMs, prototyping AI-driven features, or integrating advanced conversational AI into your applications.Ready to unlock the full potential of AI? Gcore Everywhere Inference offers outstanding scalability, performance, and support, making it the perfect solution for developers and businesses working with advanced AI models. Dive deeper into our powerful tools and resources by exploring our AI blog and docs.Discover Gcore Everywhere Inference

January 28, 2025 2 min read
Developers

How do CDNs work?

Picture this: A visitor lands on your website excited to watch a video, buy an item, or explore your content. If your page loads too slowly, they may leave before it even loads completely. Every second matters when it comes to customer retention, engagement, and purchasing patterns.This is where a content delivery network (CDN) comes in, operating in the background to help end users access digital content quickly, securely, and without interruption. In this article, we’ll explain how a CDN works to optimize the delivery of websites, applications, media, and other online content, even during high-traffic spikes and cyberattacks. If you’re new to CDNs, you might want to check out our introductory article first.Key components of a CDNA CDN is a network of interconnected servers that work together to optimize content delivery. These servers communicate to guarantee that data reaches users as quickly and efficiently as possible. The core of a CDN consists of globally distributed edge servers, also known as points of presence (PoPs):Origin server: The central server where website data is stored. Content is distributed from the origin to other servers in the CDN to improve availability and performance.Points of presence (PoPs): A globally distributed network of edge servers. PoPs store cached content—pre-saved copies of web pages, images, videos, and other assets. By serving cached content from the nearest PoP to the user, the CDN reduces the distance data needs to travel, improving load times and minimizing strain on the origin server. The more PoPs a network has, the faster content is served globally.How a CDN delivers contentCDNs rely on edge servers to store content in a cache, enabling faster delivery to end users. The delivery process differs depending on whether the content is already cached or needs to be fetched from the origin server.A cache hit occurs when the requested content is already stored on a CDN’s edge server. Here’s the process:User requests content: When a user visits a website, their device sends a request to load the necessary content.Closest edge server responds: The CDN routes the request to the nearest edge server to the user, minimizing travel time.Content delivered: The edge server delivers the cached content directly to the user. This is faster because:The distance between the user and the server is shorter.The edge server has already optimized the content for delivery.What happens during a cache miss?A cache miss occurs when the requested content is not yet stored on the edge server. In this case, the CDN fetches the content from the origin server and then updates its cache:User requests content: The process begins when a user’s device sends a request to load website content.The closest server responds: As usual, the CDN routes the request to the nearest edge server.Request to the origin server: If the content isn’t cached, the CDN fetches it from the origin server, which houses the original website data. The edge server then delivers it to the user.Content cached on edge servers: After retrieving the content, the edge server stores a copy in its cache. This ensures that future requests for the same content can be delivered quickly without returning to the origin server.Do you need a CDN?Behind every fast, reliable website is a series of split-second processes working to optimize content delivery. A CDN caches content closer to users, balances traffic across multiple servers, and intelligently routes requests to deliver smooth performance. This reduces latency, prevents downtime, and strengthens security—all critical for businesses serving global audiences.Whether you’re running an e-commerce platform, a streaming service, or a high-traffic website, a CDN ensures your content is delivered quickly, securely, and without interruption, no matter where your users are or how much demand your site experiences.Take your website’s performance to the next level with Gcore CDN. Powered by a global network of over 180+ points of presence, our CDN enables lightning-fast content delivery, robust security, and unparalleled reliability. Don’t let slow load times or security risks hold you back. Contact our team today to learn how Gcore can elevate your online presence.Discover Gcore CDN

January 28, 2025 3 min read
Developers

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

January 17, 2025 3 min read
Developers

What is AI inference and how does it work?

Artificial intelligence (AI) inference is what happens when a trained AI model is used to predict outcomes from new, unseen data. While training focuses on learning from historical datasets, inference is about putting that learned knowledge into action—such as identifying production bottlenecks before they happen, converting speech to text, or guiding self-driving cars in real time. This article walks you through the basics of AI inference and shows how to get started.What is AI inference?AI inference is the application phase of artificial intelligence. Once a model has been trained on large datasets, it shifts from “learning mode” to “doing mode”—providing predictions or decisions from new data inputs.For example, an e-commerce platform with a model trained on purchasing behavior uses inference to personalize recommendations for each site visitor. Without re-training from scratch, the model quickly adapts to new browsing patterns and purchasing signals, offering instant, relevant suggestions.By enabling actionable insights, inference is transforming how businesses and technologies function, empowering relevance and instant responsiveness in an increasingly data-driven world.How does AI inference work? A practical guideAI inference has four steps: data preparation, model loading, processing and prediction, and output generation.#1 Data preparationThe first step involves transforming raw input—such as text, images, or numerical data—into a format that the AI model can process. For instance, customer feedback might be converted into numerical representations of words and patterns, or an image could be resized and normalized. Proper data preparation ensures that the AI model can effectively understand and analyze the input. For businesses, this means making sure that input data is clean, well-structured, and formatted according to the model’s requirements.#2 Model loadingOnce the input data is ready, the trained AI model is loaded into memory. This model, equipped with patterns and relationships learned during training, acts as the foundation for predictions and decisions.Businesses must make sure that their infrastructure is capable of quickly loading and deploying AI models, especially during high-demand periods. We simplify this process by providing a high-performance platform with global scalability. Your models are loaded and operational in seconds, whether you’re using a custom model or an open-source one.#3 Processing and predictionIn this step, the prepared data is passed through the model’s neural networks, which apply learned patterns to generate insights or predictions. For example, a customer service AI might analyze incoming messages to determine if they express satisfaction or frustration.The speed and accuracy of this stage depend on access to low-latency infrastructure capable of handling complex calculations. Our edge inference solution means data processing happens close to the source, reducing latency and enabling real-time decision making.#4 Output generationThe final stage translates the model’s mathematical outputs into meaningful insights, such as predictions, labels, or recommendations. These outputs must be integrated into business workflows or customer-facing applications in a way that’s easy to understand and actionable.We help streamline this step by offering APIs and integration tools that allow businesses to seamlessly incorporate inference results into their operations, so outputs are accessible and actionable in real time.A real-life exampleLet’s look at how this works in practice. Consider a retail business implementing AI for inventory management. The system continuously:Receives data from point-of-sale systems and warehouse scannersProcesses this information through trained AI modelsGenerates predictions about future inventory needsAdjusts order quantities and timing automaticallyAll of this happens in milliseconds, making real-time decisions possible. However, the speed and efficiency depend on choosing the right infrastructure for your needs.The technology stack behind inferenceTo make this process work smoothly, specialized computing infrastructure and software need to work together.Computing infrastructureModern AI inference relies on specialized hardware designed to process mathematical operations quickly. While training AI models often requires expensive, high-powered graphics processors (GPUs), inference can run on more cost-effective hardware options:CPUs: Suitable for smaller-scale applicationsEdge devices: For processing data locally on smartphones or IoT devices or other hardware closer to the data source, resulting in low latency and better privacy.Cloud-based inference servers: Designed for handling large-scale operations, enabling centralized processing and flexible scaling.When evaluating computing infrastructure for AI, businesses should prioritize solutions that address latency, scalability, and ease of use. Edge inference capabilities are essential for deploying models closer to end users, which optimizes performance globally even during peak demand. Flexible access to diverse hardware options like GPUs, CPUs, and advanced accelerators ensures adaptability, while user-friendly tools and automated scaling enable seamless management and consistent performance.Software optimizationThe efficiency of inference depends heavily on software optimization. When done right, software optimization ensures that AI applications are fast, responsive, and scalable, making them practical for real-world use.Look for the following to identify a solution that reduces inference processing time and supports optimized results:Model compression and optimization: The computational load is reduced and inference occurs faster—without sacrificing accuracy.Workload distribution and automation: This means that resources are allocated efficiently and cost-effectively.Integration: Look for APIs and tools that connect seamlessly with existing business systems.The future of AI inferenceWe anticipate three major trends for the future of AI inference.First, we’re seeing a dramatic shift toward specialized AI accelerators and custom silicon. New chips are being developed and existing ones optimized specifically for inference workloads. These purpose-built processors are delivering significant improvements in both performance and energy efficiency compared to traditional GPUs. This specialization is making AI inference more cost-effective and environmentally sustainable, particularly for companies running large-scale operations.The second major trend is the emergence of lightweight, efficient models designed specifically for inference. While large language models like GPT-4 showcase the potential of AI, many businesses are finding that smaller, task-specific models can deliver comparable or better results for their particular needs. These “small language models” (SLMs) and domain-adapted models are trained on focused datasets and optimized for specific tasks, making them more practical for real-world deployment. This approach is particularly valuable for edge computing scenarios where computing resources are limited.Finally, the infrastructure for AI inference is becoming more sophisticated and accessible. Advanced orchestration tools are automating the complex process of model deployment, scaling, and monitoring. These platforms can automatically optimize model performance based on factors like latency requirements, cost constraints, and traffic patterns. This automation is making it possible for companies to deploy AI solutions without maintaining large specialized teams of ML engineers.Dive into more of our predictions for AI inference in 2025 and beyond in our dedicated article.Accelerate inference adoption for your businessAI inference is rapidly becoming a differentiator for businesses. By applying trained AI models to new data, companies can make instant predictions, automate decision-making, and optimize operations across industries. However, achieving these benefits depends on having the right infrastructure and expertise behind the scenes. This is where the choice of inference provider plays a critical role. The provider’s infrastructure determines latency, scalability, and overall efficiency, which directly affect business outcomes. A well-equipped provider allows businesses to maximize the value of their AI investments.At Gcore, we are uniquely positioned to meet these needs with our edge inference solution. Leveraging a secure, global network of over 180 points of presence equipped with NVIDIA GPUs, we deliver ultra-fast, low-latency inference capabilities. Intuitively deploy and scale open-source or custom models on our powerful platform that accelerates AI adoption for a competitive edge in an increasingly AI-driven world.Get a complimentary consultation about your AI inference needs

January 14, 2025 4 min read
Comparisons

AI model selection simplified: your guide to Gcore-supported model selection

2024 has been an exceptional year for advancements in artificial intelligence (AI). The variety of models has grown significantly, with impressive strides in performance across domains. Whether it’s text or image classification, text and image generation, speech models, or multimodal capabilities, businesses now face the challenge of navigating an ever-expanding catalog of open-source models. Understanding the differences in tasks and metrics targeted by these models is crucial to making informed decisions.At Gcore, we’ve been expanding our model catalog to simplify AI model testing and deployment. As businesses scale their AI applications across various units, identifying the best model for specific tasks becomes critical. For example, some applications, like cancer screening, prioritize accuracy over latency. On the other hand, time-sensitive use cases like fraud detection demand rapid processing, while cost may drive decisions for lightweight applications like chatbot development.This guide provides a comprehensive overview of the AI models supported on the Gcore platform, their characteristics, and their most effective use cases to help you choose the right model for your needs. Our inference solution also supports custom AI models.Large language models (LLMs)LLMs are foundational for applications requiring human-like understanding and generation of text, making them crucial for customer service, research, and educational tools. These models are versatile and cover a range of applications:Text generation (e.g., creative writing, content creation)SummarizationQuestion answeringInstruction following (specific to instruct-tuned models)Sentiment analysisTranslationCode generation and debugging (if fine-tuned for programming tasks)Models supported by GcoreGcore supports the following models for inference, available in the Gcore Customer Portal. Activate them at the click of a button.Model nameProviderParametersKey characteristicsLLaMA-Pro-8BMeta AI8 BillionBalanced trade-off between cost and power, suitable for real-time applications.Llama-3.2-1B-InstructMeta AI1 BillionIdeal for lightweight tasks with minimal computational needs.Llama-3.2-3B-InstructMeta AI3 BillionOffers lower latency for moderate task complexity.Llama-3.1-8B-InstructMeta AI8 BillionOptimized for instruction following.Mistral-7B-Instruct-v0.3Mistral AI7 BillionExcellent for nuanced instruction-based responses.Mistral-Nemo-Instruct-2407Mistral AI & Nvidia7 BillionHigh efficiency with robust instruction-following capabilities.Qwen2.5-7B-InstructQwen7 BillionExcels in multilingual tasks and general-purpose applications.QwQ-32B-PreviewQwen32 BillionSuited for complex, multi-turn conversations and strategic decision-making.Marco-o1AIDC-AI1-5 Billion (est.)Designed for structured and open-ended problem-solving tasks.Business applicationsLLMs play a pivotal role in various business scenarios; choosing the right model will be primarily influenced by task complexity. For lightweight tasks like chatbot development and FAQ automation, models like Llama-3.2-1B-Instruct are highly effective. Medium complexity tasks, including document summarization and multilingual sentiment analysis, can leverage models like Llama-3.2-3B-Instruct and Qwen2.5-7B-Instruct. For high-performance needs like real-time customer service or healthcare diagnostics, models like LLaMA-Pro-8B and Mistral-Nemo-Instruct-2407 provide robust solutions. Complex, large-scale applications, like market forecasting and legal document synthesis, are ideally suited for advanced models like QwQ-32B-Preview. Additionally, specialized solutions for niche industries can benefit from Marco-o1’s unique capabilities.Image generationImage generation models empower industries like entertainment, advertising, and e-commerce to create engaging content that captures the audience’s attention. These models excel in producing creative and high-quality visuals. Key tasks include:Generating photorealistic imagesArtistic rendering (e.g., illustrations, concept art)Image enhancement (e.g., super-resolution, inpainting)Marketing and branding visualsModels supported by GcoreWe currently support six models via the Gcore Customer Portal, or you can bring your own image generation model to our inference platform.Model nameProviderParametersKey characteristicsByteDance/SDXL-LightningByteDance100-400 MillionLightning-fast text-to-image generation with 1024px outputs.stable-cascadeStability AI20M-3.6 BillionWorks on smaller latent spaces for faster and cheaper inference.stable-diffusion-xlStability AI~3.5B Base + 1.2B RefinementPhotorealistic outputs with detailed composition.stable-diffusion-3.5-large-turboStability AI8 BillionBalances high-quality outputs with faster inference.FLUX.1-schnellBlack Forest Labs12 BillionDesigned for fast, local development.FLUX.1-devBlack Forest Labs12 BillionOpen-weight model for non-commercial applications.Business applicationsIn high-quality image generation, models like stable-diffusion-xl and stable-cascade are commonly employed for creating marketing visuals, concept art for gaming, and detailed e-commerce product visualizations. Real-time applications, such as AR/VR customizations and interactive customer tools, benefit from the speed of ByteDance/SDXL-Lightning and FLUX.1-schnell. FLUX.1-dev and stable-diffusion-3.5-large-turbo are excellent options for experimentation and development, allowing startups and enterprises to prototype generative AI workflows cost-effectively. Specialized use cases, such as ultra-high-quality visuals for luxury goods or architectural renders, also find tailored solutions with stable-cascade.Speech recognitionSpeech recognition models are essential for industries like media, healthcare, and education, where transcription accuracy and speed directly impact their efficacy. They facilitate:Accurate speech-to-text transcriptionLow-latency live audio conversionMultilingual speech processing and translationAutomated note-taking and content creationModels supported by GcoreAt Gcore, our inference service supports two Whisper models, as well as custom speech recognition models.Model nameProviderParametersKey characteristicswhisper-large-v3-turboOpenAI809 MillionOptimized for speed with minimal accuracy trade-offs.whisper-large-v3OpenAI1.55 BillionHigh-quality multilingual speech-to-text and translation with reduced error rates.Business applicationsSpeech recognition technology supports a wide range of business functions, all requiring precision and accuracy, delivered at speed. For real-time transcription, whisper-large-v3-turbo is ideal for live captioning and speech analytics applications. High-accuracy tasks, including legal transcription, academic research, and multilingual content localization, leverage the advanced capabilities of whisper-large-v3. These models enable faster, more accurate workflows in sectors where precise audio-to-text conversion is crucial.Multimodal modelsBy bridging text, image, and other data modalities, multimodel models unlock innovative solutions for industries requiring complex data analysis. These models integrate diverse data types for applications in:Image captioningVisual question answeringMultilingual document processingRobotic visionModels supported by GcoreWe currently support the following multimodal models:Model nameProviderParametersKey characteristicsPixtral-12B-2409Mistral AI12 BillionExcels in instruction-following tasks with text and image integration.Qwen2-VL-7B-InstructQwen7 BillionAdvanced visual understanding and multilingual support.Business applicationsFor tasks like image captioning and visual question answering, Pixtral-12B-2409 provides robust capabilities in generating descriptive text and answering questions based on visual content. Qwen2-VL-7B-Instruct supports document analysis and robotic vision, enabling systems to extract insights from documents or understand their physical surroundings. These applications are transformative for industries ranging from digital media to robotics.A multitude of models, supported by GcoreStart developing on the Gcore platform today, leveraging top-tier GPUs for seamless AI model training and deployment. Simplify large-scale, cross-regional AI operations with our inference-at-the-edge solutions, backed by over a decade of CDN expertise.Get started with Inference at the Edge today

December 20, 2024 4 min read
Developers

What is a DDoS attack?

A DDoS (distributed denial-of-service) attack is a type of cyberattack in which a hacker overwhelms a server with an excessive number of requests, causing the server to stop functioning properly. This can cause the website, app, game, or other online service to become slow, unresponsive, or completely unavailable. DDoS attacks can result in lost customers and revenue for the victim. DDoS attacks are becoming increasingly common, with a 46% increase in the first half of 2024 compared to the same period in 2023.How do DDoS attacks work?DDoS attacks work by overwhelming and flooding a company’s resources so that legitimate users cannot get through. The attacker creates huge amounts of malicious traffic by creating a botnet, a collection of compromised devices that work together to carry out the attack without the device owners’ knowledge. The attacker, referred to as the botmaster, sends instructions to the botnet in order to implement the attack. The attacker forces these bots to send an enormous amount of internet traffic to a victim’s resource. As a result, the server can’t process real users trying to access the website or app. This causes customer dissatisfaction and frustration, lost revenue, and reputational damage for companies.Think of it this way: Imagine a vast call center. Someone dials the number but gets a busy tone. This is because a single spammer has made thousands of automated calls from different phones. The call center’s lines are overloaded, and the legitimate callers cannot get through.DDoS attacks work similarly, but online: The fraudster’s activity completely blocks the end users from reaching the website or online service.Different types of DDoS attacksThere are three categories of DDoS attacks, each attacking a different network communication layer. These layers come from the OSI (Open Systems Interconnection) model, the foundational framework for network communication that describes how different systems and devices connect and communicate. This model has seven layers. DDoS attacks seek to exploit vulnerabilities across three of them: L3, L4, and L7.While all three types of attacks have the same end goal, they differ in how they work and which online resources they target. L3 and L4 DDoS attacks target servers and infrastructure, while L7 attacks affect the app itself.Volumetric attacks (L3) overwhelm the network equipment, bandwidth, or server with a high volume of traffic.Connection protocol attacks (L4) target the resources of a network-based service, like website firewalls or server operating systems.Application layer attacks (L7) overwhelm the network layer, where the application operates with many malicious requests, which leads to application failure.1. Volumetric attacks (L3)L3, or volumetric, DDoS attacks are the most common form of DDoS attack. They work by flooding internal networks with malicious traffic, aiming to exhaust bandwidth and disrupt the connection between the target network or service and the internet. By exploiting key communication protocols, attackers send massive amounts of traffic, often with spoofed IP addresses, to overwhelm the victim’s network. As the network equipment strains to process this influx of data, legitimate requests are delayed or dropped, leading to service degradation or even complete network failure.2. Connection protocol attacks (L4)Protocol attacks occur when attackers send connection requests from multiple IP addresses to target server open ports. One common tactic is a SYN flood, where attackers initiate connections without completing them. This forces the server to allocate resources to these unfinished sessions, quickly leading to resource exhaustion. As these fake requests consume the server’s CPU and memory, legitimate traffic is unable to get through. Firewalls and load balancers managing incoming traffic can also be overwhelmed, resulting in service outages.3. Application layer attacks (L7)Application layer attacks strike at the L7 layer, where applications operate. Web applications handle everything from simple static websites to complex platforms like e-commerce sites, social media networks, and SaaS solutions. In an L7 attack, a hacker deploys multiple bots or machines to repeatedly request the same resource until the server becomes overwhelmed.By mimicking genuine user behavior, attackers flood the web application with seemingly legitimate requests, often at high rates. For example, they might repeatedly submit incorrect login credentials or overload the search function by continuously searching for products. As the server consumes its resources managing these fake requests, genuine users experience slow response times or may be completely denied access to the application.How can DDoS attacks be prevented?To stay one step ahead of attackers, use a DDoS protection solution to protect your web resources. A mitigation solution detects and blocks harmful DDoS traffic sent by attackers, keeping your servers and applications safe and functional. If an attacker targets your server, your legitimate users won’t notice any change—even during a considerable attack—because the protection solution will allow safe traffic while identifying and blocking malicious requests.DDoS protection providers also give you reports on attempted DDoS attacks. This way, you can track when the attack happened, as well as the size and scale of the attack. This enables you to respond effectively, analyze the potential implications of the attack, and implement risk management strategies to mitigate future disruptions.Repel DDoS attacks with GcoreAt Gcore, we offer robust and proven security solutions to protect your business from DDoS attacks. Gcore DDoS Protection provides comprehensive mitigation at L3, L4, and L7 for websites, apps, and servers. We also offer L7 protection as part of Gcore WAAP, which keeps your web apps and APIs secure against a range of modern threats using AI-enabled threat detection.Take a look at our recent Radar report to learn more about the latest DDoS attack trends and the changing strategies and patterns of cyberattacks.Read our DDoS Attack Trends Radar report

November 7, 2024 4 min read

What is a CDN?

Whether you’re running an e-commerce store, streaming videos, or managing an app, delivering content quickly and reliably is essential to keeping users satisfied. This is where a content delivery network (CDN) comes into play. A CDN is a globally distributed network of servers that work together to deliver content to users quickly, minimizing latency. Instead of relying on a single server, a CDN uses edge servers—called points of presence (PoPs)—to cache or temporarily store copies of your content closer to the user. This optimizes website performance, drastically cuts down on load times, and improves the user experience. Research suggests that a one-second lag in page loading speed can significantly decrease engagement, citing a 7% decline in conversions and an 11% decrease in page visits. CDNs considerably speed up load times by reducing latency through content caching closer to the user. By splitting up your website’s traffic over several servers, CDNs also protect it from online threats. Distributed denial-of-service (DDoS) attacks are lessened by CDNs because they spread traffic among a network of servers, improving security and availability. What Challenges Do CDNs Address?CDNs tackle two key challenges to improve website and application performance: Slow load times: Users sometimes experience frustratingly slow-loading websites and applications. This is because data must travel from a server to the end user’s device, causing latency. CDNs move servers closer to end users, reducing the distance that data has to travel and speeding up load times.  High traffic volumes: High traffic volumes during peak times or cyberattacks can overwhelm your website and lead to latency or site unavailability. Since CDNs distribute traffic across multiple servers, no single server is overwhelmed. This helps prevent crashes and delivers smooth performance for all users.Common Use Cases for CDNsCDNs are vital across a range of industries, providing measurable improvements in content delivery and user experience. E-commerce websites use CDNs to guarantee quick page loading and frictionless shopping experiences, even during periods of high traffic. Speed is crucial for online businesses. A study found that the average cost of downtime for e-commerce websites is around $500,000 per hour. This includes lost sales, operational costs, and long-term damage to brand reputation Streaming services rely on CDNs to deliver high-quality video content while minimizing buffering. Netflix states that its CDN contributes to the daily delivery of over 125 million hours of streaming content, guaranteeing a seamless experience for customers worldwide. Gaming companies use CDNs to lower latency and provide a consistent real-time user experience, especially during live multiplayer matches, where it is essential to preserve an engaging and fair gameplay experience. News outlets and blogs benefit from CDNs by ensuring their content loads quickly for readers around the world, during large-scale traffic surges, especially during major events like elections or breaking news.  The Benefits of a CDNFaster Website PerformanceEvery second counts when delivering content online. Slow websites frustrate users and harm your business. CDNs speed up content delivery by caching data closer to users, reducing page and file load times. Whether you’re delivering static content (such as CSS, HTML or JPG files) or dynamic content (like data generated by user interactions or API calls), a CDN ensures optimal performance regardless of user location. While factors like DNS settings, server configurations, and code optimization all play a role, the physical distance between your origin server and your users is a factor that only a CDN can solve. Increased Availability and ReliabilityDowntime can seriously affect online businesses. Hardware failures, traffic surges, and cyberattacks can reduce your website’s availability, harming your customers’ experience and causing financial or reputational damage. In fact, around 98% of organizations report that just one hour of downtime costs over $100,000. A CDN ensures that your website remains available, fast, and reliable by leveraging essential features such as: Load balancing: This process dynamically distributes traffic across multiple servers to optimize performance and prevent overload.Intelligent failover: Automatically redirects traffic if a server goes offline, ensuring continuity with minimal disruption.Anycast routing: Directs users to the closest or most efficient server, further reducing latency and enhancing response times.Security FeaturesAs cyber threats continue to grow in sophistication and frequency, securing your website or application is more critical than ever. According to recent statistics from Cobalt’s 2024 Cybersecurity Report, weekly attacks worldwide increased by 8% in 2023, while attackers used more sophisticated strategies to exploit vulnerabilities. Strong security measures that not only safeguard your website but also guarantee optimal performance are necessary in light of these evolving threats. CDN security features not only improve website performance but also defend against a wide range of attacks by distributing traffic across multiple servers, which mitigates DDoS attacks and filters out malicious traffic before it reaches your website. These features, from DDoS protection to safeguarding APIs, help maintain uptime, protect sensitive data, and guarantee a seamless user experience. Most modern solutions like Gcore CDN integrate robust security measures into content delivery, such as:SSL/TLS encryption facilitates secure data transmission by encrypting traffic, protecting sensitive information from being intercepted.L3/L4 DDoS protection blocks large-scale cyberattacks designed to flood your network and disrupt services.L7 DDoS protection guards your website from more complex attacks targeting how the website functions, helping it continue to operate smoothly.Web application firewall (WAF) acts as a shield, blocking harmful traffic such as hacking attempts or malicious scripts before they can affect your site.API security protects the communication between your application and other software, preventing unauthorized access or data theft.Bot protection identifies harmful automated traffic (bots), preventing activities like data scraping or login attempts with stolen credentials while allowing useful bots (like search engine crawlers) to function normally. Elevate Your Online Experience With a CDNA CDN is no longer a luxury—it’s a necessity for businesses that want to deliver fast, reliable, and secure online experiences. Whether your goal is to optimize performance, manage high traffic, or protect your site from attacks, a well-configured CDN makes all the difference.Ready to enhance your website’s performance? Our futureproof CDN runs on a global network of over 180 points of presence, so your customers get outstanding performance no matter where in the world they’re located. Get in touch with our team today to learn how our CDN can benefit your business.Discover Gcore CDN

October 24, 2024 4 min read
Developers

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

October 17, 2024 2 min read
Developers

How to Migrate Your Video Files to Gcore Video Streaming

Migrating large volumes of video files from different platforms can be daunting and time-consuming, often discouraging companies from moving to a superior provider. But it doesn’t have to be this way. We’ve created this three-step guide to help you efficiently migrate your video files to Gcore from other popular streaming platforms.Step 1: Get Links to Your VideosFirst, obtain links to your videos and download them. Look for your provider in the list below, or refer to the general SFTP/S3 storage section if applicable. After completing the steps for your provider, go straight to step 2.Google DriveShare the file: Open Google Drive and locate the MP4 file you want to download. Right-click on the file and select “Share.”Get the shareable link: In the sharing settings, click “Get link.” Ensure the link-sharing option is turned on.Set sharing permissions: Adjust the sharing permissions so “Anyone with the link” can view or download the file. Copy the generated link.Amazon S3Edit S3 block public access settings: Go to the S3 management console, select the bucket containing your MP4 file, and edit the Block Public Access settings if necessary.Add a bucket policy: Implement a bucket policy that grants public read access to your files.Get the list of objects: Navigate to the Objects tab, find your MP4 file, and click on the file to obtain the Object URL, which will be your download link.VimeoAccess the video: Log in to your Vimeo account and go to the video you wish to download.Select options: Click on the “Settings” button (gear icon) below the video player.Get video file link: In the settings menu, go to the “Video File” tab, where you can find the download link for your MP4 file.MUXEnable master access: Log in to your MUX account, navigate to the video asset, and enable master access if it’s not already enabled.Retrieve URL to master: Once master access is enabled, the URL to the master file will be available in the video asset details. Copy this URL for downloading the file.DropboxCreate a shareable link: Log in to your Dropbox account and locate the MP4 file you want to share. Click on the “Share” button next to the file.Set access permissions: In the sharing settings, create a link and set the permissions to “Anyone with the link.” Copy the generated link to download the file.General SFTP or S3 StorageAccess storage: Log in to your SFTP or S3 storage service control panel.Manage buckets/directories: Navigate to the appropriate bucket or directory containing your MP4 files.Retrieve download links: Generate HTTP/S links for the files you want to download. You can then use these links to download the files directly.Step 2: Check Availability to DownloadEnsure that your video files are available and ready for download, preventing any interruptions or issues during the migration process.Open HTTP/S link in a browser: Copy the HTTP/S link for the MP4 file and paste it into your browser’s address bar. Press Enter to navigate to the link.Check the video plays correctly in the browser: Verify that the video starts playing once the link is opened. This step ensures that the file is accessible and the link is functioning properly.Right-click to download: While the video is playing, right-click on the video player. Select “Save video as…” from the context menu. Choose a destination on your local disk to save the MP4 file.Step 3: Upload to Gcore Video StreamingNo matter which provider you’re migrating from, you need to upload your videos to Gcore Video Streaming storage. There are three primary methods to upload videos to Gcore storage:Copy from external storage: If your videos are available via public HTTPS URLs, you can directly copy the video files from external storage to Gcore. This method efficiently transfers files without downloading them to your local device first.Upload from a local device: Videos can be uploaded from your local host, backend, browser, or mobile app using the TUS resumable upload protocol. This method is resilient to interruptions, ensuring a smooth upload process by resuming from the point of failure.Batch upload: This method will soon be available to migrate extensive collections of videos, allowing you to transfer vast numbers of video files efficiently.The simplest migration option is to obtain video URLs and copy them to Gcore Video Hosting, eliminating the need to download and reupload videos.Example API Request to Copy Video from External StorageTo copy a video from another server, specify the origin_url attribute in the POST API request. The original video will be downloaded for video hosting on our server. Here is an example of the API request to set a task for copying a video from external storage:curl -L 'https://api.gcore.com/streaming/videos/' \-H 'Content-Type: application/json' \-H 'Authorization: APIKey 1234$0d16599c' \-d '{ "video": { "name": "Gcore Demo", "description": "Video copied from an external S3 Storage", "origin_url": "https://s-ed1.cloud.gcore.lu/demo-video/gcore.mp4" } }Refer to the complete documentation for detailed steps and examples of API requests. The original file must be in MP4 format or one of the following formats: 3g2, 3gp, asf, avi, dif, dv, flv, f4v, m4v, mov, mp4, mpeg, mpg, mts, m2t, m2ts, qt, wmv, vob, mkv, ogv, webm, vob, ogg, mxf, quicktime, x-ms-wmv, mpeg-tts, vnd.dlna.mpeg-tts. Streaming formats like HLS (.m3u8/.ts) and DASH (.mpd/.m4v) are intended for final video distribution and cannot be used as original file formats. Here are examples of good and bad links:Good link: https://demo-files.gvideo.io/gcore.mp4Bad link (chunked HLS format): https://demo-files.gvideo.io/hls/master.m3u8Note: Currently, only one video can be uploaded per request, so transferring your library in batches will require automation.Migrate to Gcore Video Streaming TodayGcore Video Streaming makes video migration easy with support for multiple sources and automatic transcoding. Whether you’re moving files from cloud storage, hosting platforms, or API-based services, Gcore streamlines video administration. Store, process, and distribute videos in various formats, complete with features like subtitles and timeline previews.With seamless migration and automatic transcoding, Gcore ensures your videos are optimized and ready for distribution, saving you time and effort. Simplify your video management and ensure your content is always accessible and in the best format for your audience with Gcore’s robust video streaming solutions.

September 18, 2024 4 min read

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