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What Is a Load Balancer? | How Do Load Balancers Work?

  • By Gcore
  • June 6, 2023
  • 6 min read
What Is a Load Balancer? | How Do Load Balancers Work?

Have you ever wondered how a web application can scale to serve millions of users worldwide? To serve a vast number of user requests, web applications must build their services to multiple instances. You might then wonder: How can an application evenly distribute the user requests so that all the user requests can be handled with peak efficiency? The short answer to that question is load balancing. The complete answer is…well, please reserve a few minutes to go through the article! You will learn all about load balancing: what a load balancer is, how it works, its benefits, methods of load balancing, and how to implement a load balancer for your use cases.

What Is a Load Balancer?

A load balancer is a hardware device or software application responsible for evenly distributing the requests across multiple application instances. (An “instance” is a single deployment of an application or service running on a server.) As a result, the application can cope with a high volume of requests efficiently.

If an additional app instance is introduced, the load balancer will redistribute the requests to include the new instance, thus reducing the workload on the existing instances. If an app instance goes down, all the requests to the problematic instance will be redistributed to other operational instances. As a result, the app is highly available and fault tolerant, offering users an uninterrupted service.

Load balancers can be categorized into different types based on how they manage and redistribute incoming requests. The two primary types are network load balancers, and application load balancers. Another mode of categorization is by physicality type, in which case we divide them into hardware and software load balancers. Let’s take a look at each of these in depth.

Network Load Balancers

Network load balancers forward the requests at the transport layer, layer 4 of the Open System Interconnection (OSI) model. The forwarding mechanism is based solely on network attributes, such as the IP addresses of the clients and the corresponding application instances.

Network balancers do not consider the contents of the requests when forwarding them to the app instances, which allows them to offer low latencies when redistributing the requests. Network load balancers would be a great fit for applications with extreme performance requirements, such as streaming or game applications.

Application Load Balancers

Application load balancers forward the requests at the application layer, also known as layer 7 of the OSI model. They examine the content of the requests, such as HTTP Headers, request paths, or request methods. This way, the application load balancer can flexibly distribute the requests to different app instances to match the business requirements.

Application load balancers are appropriate for e-commerce or social network applications that need support for custom HTTP responses and health checks for the app instances but do not require extremely low latencies.

Hardware Load Balancers

Network and application are categories of load balancers based on how they manage and redistribute incoming requests.

Hardware load balancers are purpose-built devices designed to redistribute the requests among app instances. They are often used in on-premise infrastructure alongside the company’s network systems and application servers. Hardware load balancers are a good choice for applications that want to store all data in self-managed servers or require special hardware customization when forwarding the requests to the target instances. They also offer enhanced security options.

Benefits of a Load Balancer

A load balancer can help with application performance in a number of ways, including scalability, cost reduction, availability, and request processing speed. Let’s take a closer look at each of these in turn.

Scalability

When more user requests are sent to the application server instances, the CPU utilization of the server instances is high.

An e-commerce application would benefit from the scalability the load balancers offer. Typically, the volume of user requests for e-commerce applications escalates far above normal levels during Black Friday sales.

High Availability

If one application instance goes down, the load balancer will forward the requests to other instances so the end user does not encounter any error or stoppage in service. The load balancer helps to ensure an application’s high availability by circumventing problematic instances.

How Load Balancing Works

To create a load balancing system that effectively forwards requests to the application instances, it’s first essential to understand how a load balancer works. Let’s review the inner workings of load balancing and explore some popular load balancing methods.

How Does a Load Balancer Work?

Different algorithms and combinations thereof are used by load balancers. The algorithm(s) depend on the complexity and features of the load balancer in question. A basic load balancer usually uses an algorithm called Round Robin to assign requests to the app instances. The Round Robin algorithm distributes the requests to the app instances one-by-one, resulting in an equal load distribution. No single app is overly taxed.

Let’s say you have three application instances. The first user request will be sent to instance number one. The second request will be sent to instance number two. The third request will be sent to instance number three. The fourth request will be sent to instance number four. Here, we have four instances available, so request number five will be sent to app instance one, and so on.

Figure 1: Demonstration of how a load balancer works

Instead of interacting directly with the application server, your application’s end users send requests to the load balancer.

What Are the Components of a Load Balancer?

A typical load balancer consists of four parts:

  1. Virtual IP: This is the unique digital address of the load balancer, allowing the client to send requests to the load balancer.
  2. Network protocols: Different types of load balancers support different network protocols. For example, a network load balancer supports TCP or UDP protocol, whereas an application load balancer supports HTTP and HTTPS protocols.
  3. Load balancing algorithms: Load balancers use different algorithms, such as Round Robin and IP Hash, to determine to which appropriate application instance they should forward the client’s request.
  4. Health monitoring: The load balancer routinely checks the health status of each app instance.

Load Balancing Methods

Besides the Round Robin algorithm already discussed, other load balancing methods and algorithms exist, including Weighted Round Robin and resourced-based methods. In general, the load-balancing methods can be divided into two categories: static load balancing and dynamic load balancing. Let’s take a closer look at each.

Static Load Balancing

With static load balancing, the load balancers forward the requests to the app instances without examining the current state of these app instances. This makes static load balancing easy to implement. The drawback of the static load balancing method is that it cannot adapt to the states of the app instances, which could be very different in runtime from what you anticipated, potentially affecting performance, and thus user experience. Some static load balancing methods are:

  • Round Robin: The load balancer will forward the requests to the app instances cyclically, distributing requests evenly across the instances.
  • Weighted Round Robin: Each app instance is assigned a weight, serving as an indicator of its processing capacity or priority. The load balancer forwards requests to the app instances according to the weighting. The higher the weighted number the app instance has, the more requests will be forwarded to that instance.
  • IP hash: The load balancer generates a unique hash key based on both the client IP and the app instance IP. This method allows the client to interact with the same app instance repeatedly across multiple sessions. IP hash load balancing algorithm is suitable for applications that need persisted sessions between the client and the app instance because they ensure a continuous, seamless experience for the user.

Dynamic Load Balancing

With dynamic load balancing, load balancers forward requests to the app instances based on the current state of these instances. As a result, the dynamic load balancers can adapt to the ongoing changes of the app instances and tend to be more efficient than the static load balancers. However, dynamic load balancing is more complicated to implement. Some examples of dynamic load balancing methods are:

  • Least connection: With the least connection load balancing method, the requests are forwarded to the app instance with the lowest number of active connections.
  • Weighted least connection: The weighted least connection method will forward the requests to the app instance based on the number of active connections and the weighting of that instance. For example, if there are three app instances with the same number of active connections, the one with the highest weighting number will be chosen to forward the request.

Load Balancer in the Cloud

Setting up and maintaining a group of load balancers is a challenging task. To create and manage load balancers efficiently, you need to:

  • Have a number of different load-balancing algorithms to support different internal business use cases
  • Monitor your system health for the application instances
  • Configure access controls and protection for your load balancers to prevent malicious access from the public internet
  • Ensure scalability of your load balancer as your application needs grow

Gcore’s Load Balancer

At Gcore, we understand the difficulties and challenges of setting up a load balancer from scratch. There are a huge number of options available, and your choice directly affects performance and user experience—for better or for worse. Gcore’s Load Balancer solves these challenges and comes with built-in features support for:

  • Different load balancing algorithms such as Round Robin, least connections, and source IP, so that you can choose the one that fits your need
  • Setting up unhealthy and healthy thresholds
  • Setting up the load balancers firewall, which allows you to set the rules for inbound and outbound traffic to enhance security

To learn more about how to get started, configure, and troubleshoot the Gcore load balancing solutions, please take a look at our knowledge page.

Conclusion

With a growing number of users coming to your app, having a load balancer to distribute user requests to your instances appropriately is essential for performance and user experience. However, setting up a load balancer that appropriately distributes user requests takes a lot of work. The Gcore load balancing solutions helps you to distribute your user workload in the most elegant and efficient way possible.

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Once cached, assets are delivered in a few milliseconds.Lower billsMost object storage providers charge $80–$120 per TB in egress fees. By fronting your storage with a CDN, you only pay egress once per edge location—then it’s all cache hits after that. If you’re using Gcore Storage and Gcore CDN, there’s zero egress fee between the two.Caching isn’t the only way you save. Gcore CDN can also compress eligible file types (like HTML, CSS, JavaScript, and JSON) on the fly, further shrinking bandwidth usage and speeding up file delivery—all without any changes to your storage setup.Less origin traffic and less data to transfer means smaller bills. And your storage bucket doesn’t get slammed under load during traffic spikes.Simple scaling, globallyThe CDN takes the hit, not your bucket. That means fewer rate-limit issues, smoother traffic spikes, and more reliable performance globally. Gcore CDN spans the globe, so you’re good whether your users are in Tokyo, Toronto, or Tel Aviv.Setup guide: Gcore CDN + Gcore Object StorageLet’s walk through configuring Gcore CDN to cache content from a storage bucket. This works with Gcore Object Storage and other S3-compatible services.Step 1: Prep your bucketPublic? Check files are publicly readable (via ACL or bucket policy).Private? Use Gcore’s AWS Signature V4 support—have your access key, secret, region, and bucket name ready.Gcore Object Storage URL format: https://<bucket-name>.<region>.cloud.gcore.lu/<object> Step 2: Create CDN resource (UI or API)In the Gcore Customer Portal:Go to CDN > Create CDN ResourceChoose "Accelerate and protect static assets"Set a CNAME (e.g. cdn.yoursite.com) if you want to use your domainConfigure origin:Public bucket: Choose None for authPrivate bucket: Choose AWS Signature V4, and enter credentialsChoose HTTPS as the origin protocolGcore will assign a *.gcdn.co domain. If you’re using a custom domain, add a CNAME: cdn.yoursite.com CNAME .gcdn.co Here’s how it works via Terraform: resource "gcore_cdn_resource" "cdn" { cname = "cdn.yoursite.com" origin_group_id = gcore_cdn_origingroup.origin.id origin_protocol = "HTTPS" } resource "gcore_cdn_origingroup" "origin" { name = "my-origin-group" origin { source = "mybucket.eu-west.cloud.gcore.lu" enabled = true } } Step 3: Set caching behaviorSet Cache-Control headers in your object metadata: Cache-Control: public, max-age=2592000 Too messy to handle in storage? Override cache logic in Gcore:Force TTLs by path or extensionIgnore or forward query strings in cache keyStrip cookies (if unnecessary for cache decisions)Pro tip: Use versioned file paths (/img/logo.v3.png) to bust cache safely.Secure access with signed URLsWant your assets to be private, but still edge-cacheable? Use Gcore’s Secure Token feature:Enable Secure Token in CDN settingsSet a secret keyGenerate time-limited tokens in your appPython example: import base64, hashlib, time secret = 'your_secret' path = '/videos/demo.mp4' expires = int(time.time()) + 3600 string = f"{expires}{path} {secret}" token = base64.urlsafe_b64encode(hashlib.md5(string.encode()).digest()).decode().strip('=') url = f"https://cdn.yoursite.com{path}?md5={token}&expires={expires}" Signed URLs are verified at the CDN edge. Invalid or expired? Blocked before origin is touched.Optional: Bind the token to an IP to prevent link sharing.Debug and cache tuneUse curl or browser devtools: curl -I https://cdn.yoursite.com/img/logo.png Look for:Cache: HIT or MISSCache-ControlX-Cached-SinceCache not working? Check for the following errors:Origin doesn’t return Cache-ControlCDN override TTL not appliedCache key includes query strings unintentionallyYou can trigger purges from the Gcore Customer Portal or automate them via the API using POST /cdn/purge. Choose one of three ways:Purge all: Clear the entire domain’s cache at once.Purge by URL: Target a specific full path (e.g., /images/logo.png).Purge by pattern: Target a set of files using a wildcard at the end of the pattern (e.g., /videos/*).Monitor and optimize at scaleAfter rollout:Watch origin bandwidth dropCheck hit ratio (aim for >90%)Audit latency (TTFB on HIT vs MISS)Consider logging using Gcore’s CDN logs uploader to analyze cache behavior, top requested paths, or cache churn rates.For maximum savings, combine Gcore Object Storage with Gcore CDN: egress traffic between them is 100% free. That means you can serve cached assets globally without paying a cent in bandwidth fees.Using external storage? You’ll still slash egress costs by caching at the edge and cutting direct origin traffic—but you’ll unlock the biggest savings when you stay inside the Gcore ecosystem.Save money and boost performance with GcoreStill serving assets direct from storage? You’re probably wasting money and compromising performance on the table. Front your bucket with Gcore CDN. Set smart cache headers or use overrides. Enable signed URLs if you need control. Monitor cache HITs and purge when needed. Automate the setup with Terraform. Done.Next steps:Create your CDN resourceUse private object storage with Signature V4Secure your CDN with signed URLsCreate a free CDN resource now

Bare metal vs. virtual machines: performance, cost, and use case comparison

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 cloud 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. Here’s a quick breakdown to help you decide what works best for you.FactorBare metal serversVirtual machinesPerformanceDedicated resources; ideal for high-performance workloadsShared resources; suitable for moderate or variable workloadsScalabilityOften requires manual scaling; less flexibleHighly elastic; easy to scale up or downCustomizationFull control over hardware, OS, and configurationLimited by hypervisor and provider’s environmentSecurityIsolated by default; no hypervisor layerShared environment with strong isolation protocolsCostHigher upfront cost; dedicated hardwarePay-as-you-go pricing; cost-effective for flexible workloadsBest forHPC, AI/ML, compliance-heavy workloadsStartups, dev/test, fast-scaling applicationsAll about bare metal serversA bare metal server is a single-tenant physical server rented from a cloud provider. Unlike virtual servers, the hardware is not shared with other users, giving you full access to all resources and deeper control over configurations. 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. With no shared resources or hypervisor layer, there’s less risk of vulnerabilities that come with multi-tenant environments.Dedicated resources: Because you aren’t sharing the server with other users, all server resources are dedicated to your application so that you consistently get the performance you need.Who should use bare metal servers?Here are examples of instances where bare metal servers are the best option for a business:High-performance computing (HPC)Big data processing and analyticsResource-intensive applications, such as AI/ML workloadsGame and video streaming serversBusinesses requiring enhanced security and complianceAll about virtual machinesA virtual server (or virtual machine) runs on top of a physical server that’s been partitioned by a cloud provider using a hypervisor. This allows multiple VMs to share the same hardware while remaining isolated from each other.Unlike bare metal servers, virtual machines share the underlying hardware with other cloud provider customers. That means you’re using (and paying for) part of one server, providing cost efficiency and flexibility.The benefits of virtual machinesHere are some advantages of using a shared virtual machine:Scalability: Virtual machines are ideal for businesses that need to scale quickly and are starting at a small scale. With cloud-based virtualization, you can adjust your server resources (CPU, memory, storage) on demand to match changing workloads.Cost efficiency: You pay only for the resources you use with VMs, making them cost-effective for companies with fluctuating resource needs, as there is no need to pay for unused capacity.Faster deployment: VMs can be provisioned quickly and easily, which makes them ideal for anyone who wants to deploy new services or applications fast.Who should use virtual machines?VMs are a great fit for the following:Web hosting and application hostingDevelopment and testing environmentsRunning multiple apps with varying demandsStartups and growing businesses requiring scalabilityBusinesses seeking cost-effective, flexible solutionsWhich should you choose?There’s no one-size-fits-all answer. Your choice should depend on the needs of your workload:Choose bare metal if you need dedicated performance, low-latency access to hardware, or tighter control over security and compliance.Choose virtual servers if your priority is flexible scaling, faster deployment, and optimized cost.If your application uses GPU-based inference or AI training, check out our dedicated guide to VM vs. BM for AI workloads.Get started with Gcore BM or VMs todayAt Gcore, we provide both bare metal and virtual machine solutions, offering flexibility, performance, and reliability to meet your business needs. Gcore Bare Metal has the power and reliability needed for demanding workloads, while online virtual machines offers customizable configurations, free egress traffic, and flexibility.Compare Gcore BM and VM pricing now

Optimize your workload: a guide to selecting the best virtual machine configuration

Virtual machines (VMs) offer the flexibility, scalability, and cost-efficiency that businesses need to optimize workloads. However, choosing the wrong setup can lead to poor performance, wasted resources, and unnecessary costs.In this guide, we’ll walk you through the essential factors to consider when selecting the best virtual machine configuration for your specific workload needs.﹟1 Understand your workload requirementsThe first step in choosing the right virtual machine configuration is understanding the nature of your workload. Workloads can range from light, everyday tasks to resource-intensive applications. When making your decision, consider the following:Compute-intensive workloads: Applications like video rendering, scientific simulations, and data analysis require a higher number of CPU cores. Opt for VMs with multiple processors or CPUs for smoother performance.Memory-intensive workloads: Databases, big data analytics, and high-performance computing (HPC) jobs often need more RAM. Choose a VM configuration that provides sufficient memory to avoid memory bottlenecks.Storage-intensive workloads: If your workload relies heavily on storage, such as file servers or applications requiring frequent read/write operations, prioritize VM configurations that offer high-speed storage options, such as SSDs or NVMe.I/O-intensive workloads: Applications that require frequent network or disk I/O, such as cloud services and distributed applications, benefit from VMs with high-bandwidth and low-latency network interfaces.﹟2 Consider VM size and scalabilityOnce you understand your workload’s requirements, the next step is to choose the right VM size. VM sizes are typically categorized by the amount of CPU, memory, and storage they offer.Start with a baseline: Select a VM configuration that offers a balanced ratio of CPU, RAM, and storage based on your workload type.Scalability: Choose a VM size that allows you to easily scale up or down as your needs change. Many cloud providers offer auto-scaling capabilities that adjust your VM’s resources based on real-time demand, providing flexibility and cost savings.Overprovisioning vs. underprovisioning: Avoid overprovisioning (allocating excessive resources) unless your workload demands peak capacity at all times, as this can lead to unnecessary costs. Similarly, underprovisioning can affect performance, so finding the right balance is essential.﹟3 Evaluate CPU and memory considerationsThe central processing unit (CPU) and memory (RAM) are the heart of a virtual machine. The configuration of both plays a significant role in performance. Workloads that need high processing power, such as video encoding, machine learning, or simulations, will benefit from VMs with multiple CPU cores. However, be mindful of CPU architecture—look for VMs that offer the latest processors (e.g., Intel Xeon, AMD EPYC) for better performance per core.It’s also important that the VM has enough memory to avoid paging, which occurs when the system uses disk space as virtual memory, significantly slowing down performance. Consider a configuration with more RAM and support for faster memory types like DDR4 for memory-heavy applications.﹟4 Assess storage performance and capacityStorage performance and capacity can significantly impact the performance of your virtual machine, especially for applications requiring large data volumes. Key considerations include:Disk type: For faster read/write operations, opt for solid-state drives (SSDs) over traditional hard disk drives (HDDs). Some cloud providers also offer NVMe storage, which can provide even greater speed for highly demanding workloads.Disk size: Choose the right size based on the amount of data you need to store and process. Over-allocating storage space might seem like a safe bet, but it can also increase costs unnecessarily. You can always resize disks later, so avoid over-allocating them upfront.IOPS and throughput: Some workloads require high input/output operations per second (IOPS). If this is a priority for your workload (e.g., databases), make sure that your VM configuration includes high IOPS storage options.﹟5 Weigh up your network requirementsWhen working with cloud-based VMs, network performance is a critical consideration. High-speed and low-latency networking can make a difference for applications such as online gaming, video conferencing, and real-time analytics.Bandwidth: Check whether the VM configuration offers the necessary bandwidth for your workload. For applications that handle large data transfers, such as cloud backup or file servers, make sure that the network interface provides high throughput.Network latency: Low latency is crucial for applications where real-time performance is key (e.g., trading systems, gaming). Choose VMs with low-latency networking options to minimize delays and improve the user experience.Network isolation and security: Check if your VM configuration provides the necessary network isolation and security features, especially when handling sensitive data or operating in multi-tenant environments.﹟6 Factor in cost considerationsWhile it’s essential that your VM has the right configuration, cost is always an important factor to consider. Cloud providers typically charge based on the resources allocated, so optimizing for cost efficiency can significantly impact your budget.Consider whether a pay-as-you-go or reserved model (which offers discounted rates in exchange for a long-term commitment) fits your usage pattern. The reserved option can provide significant savings if your workload runs continuously. You can also use monitoring tools to track your VM’s performance and resource usage over time. This data will help you make informed decisions about scaling up or down so you’re not paying for unused resources.﹟7 Evaluate security featuresSecurity is a primary concern when selecting a VM configuration, especially for workloads handling sensitive data. Consider the following:Built-in security: Look for VMs that offer integrated security features such as DDoS protection, WAAP security, and encryption.Compliance: Check that the VM configuration meets industry standards and regulations, such as GDPR, ISO 27001, and PCI DSS.Network security: Evaluate the VM's network isolation capabilities and the availability of cloud firewalls to manage incoming and outgoing traffic.﹟8 Consider geographic locationThe geographic location of your VM can impact latency and compliance. Therefore, it’s a good idea to choose VM locations that are geographically close to your end users to minimize latency and improve performance. In addition, it’s essential to select VM locations that comply with local data sovereignty laws and regulations.﹟9 Assess backup and recovery optionsBackup and recovery are critical for maintaining data integrity and availability. Look for VMs that offer automated backup solutions so that data is regularly saved. You should also evaluate disaster recovery capabilities, including the ability to quickly restore data and applications in case of failure.﹟10 Test and iterateFinally, once you've chosen a VM configuration, testing its performance under real-world conditions is essential. Most cloud providers offer performance monitoring tools that allow you to assess how well your VM is meeting your workload requirements.If you notice any performance bottlenecks, be prepared to adjust the configuration. This could involve increasing CPU cores, adding more memory, or upgrading storage. Regular testing and fine-tuning means that your VM is always optimized.Choosing a virtual machine that suits your requirementsSelecting the best virtual machine configuration is a key step toward optimizing your workloads efficiently, cost-effectively, and without unnecessary performance bottlenecks. By understanding your workload’s needs, considering factors like CPU, memory, storage, and network performance, and continuously monitoring resource usage, you can make informed decisions that lead to better outcomes and savings.Whether you're running a small application or large-scale enterprise software, the right VM configuration can significantly improve performance and cost. Gcore provides flexible online virtual machine options that can meet your unique requirements. Our virtual machines are designed to meet diverse workload requirements, providing dedicated vCPUs, high-speed storage, and low-latency networking across 30+ global regions. You can scale compute resources on demand, benefit from free egress traffic, and enjoy flexible pricing models by paying only for the resources in use, maximizing the value of your cloud investments.Contact us to discuss your VM needs

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