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.
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:
- Virtual IP: This is the unique digital address of the load balancer, allowing the client to send requests to the load balancer.
- 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.
- 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.
- 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 Balancer, 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 Balancer helps you to distribute your user workload in the most elegant and efficient way possible.
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There is no hypervisor layer between the operating system (OS) and the hardware, so applications use the GPU resources directly.With a virtual GPU, you get a virtual machine (VM) and uses one of two types of GPU virtualization, depending on your or a cloud provider’s capabilities:An entire, dedicated GPU used by a VM, also known as a passthrough GPUA shared GPU used by multiple VMs, also known as a vGPUAlthough a passthrough GPU VM gets the entire GPU, applications access it through the layers of a guest OS and hypervisor. Also, unlike a bare metal GPU instance, other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.The difference between running applications with bare metal and virtual GPUsThese architectural features affect the following key aspects:Performance and latency: Applications running on a VM with a virtual GPU, especially vGPU, will have lower processing power and higher latency for the same GPU characteristics than those running on bare metal with a physical GPU.Cost: As a result of the above, bare metal GPUs are more expensive than virtual GPUs.Scalability: Virtual GPUs are easier to scale than bare metal GPUs because scaling the latter requires a new physical server. In contrast, a new GPU instance can be provisioned in the cloud in minutes or even seconds.Control over GPU hardware: This can be critical for certain configurations and optimizations. For example, when training massive deep learning models with a billion parameters, total control means the ability to optimize performance optimization—and that can have a big impact on training efficiency for massive datasets.Resource utilization: GPU virtualization can lead to underutilization if the tasks being performed don’t need the full power of the GPU, resulting in wasted resources.Below is a table summarizing the benefits and drawbacks of each approach: Bare metal GPUVirtual GPUPassthrough GPUvGPUBenefitsDedicated GPU resourcesHigh performance for demanding AI workloadsLower costSimple scalabilitySuitable for occasional or variable workloadsLowest costSimple scalabilitySuitable for occasional or variable workloadsDrawbacksHigh cost compared to virtual GPUsLess flexible and scalable than virtual GPUsLow performanceNot suitable for demanding AI workloadsLowest performanceNot suitable for demanding AI workloadsShould You Use Bare Metal or Virtual GPUs?Bare metal GPUs and virtual GPUs are typically used for different types of workloads. Your choice will depend on what AI tasks you’re looking to perform.Bare metal GPUs are better suited for compute-intensive AI workloads that require maximum performance and speed, such as training large language models. They are also a good choice for workloads that must run 24/7 without interruption, such as some production AI inference services. Finally, bare metal GPUs are preferred for real-time AI tasks, such as robotic surgery or high-frequency trading analytics.Virtual GPUs are a more suitable choice for the early stages of AI/ML and iteration on AI models, where flexibility and cost-effectiveness are more important than top performance. Workloads with variable or unpredictable resource requirements can also run on this type of GPU, such as training and fine-tuning small models or AI inference tasks that are not sensitive to latency and performance. Virtual GPUs are also great for occasional, short-term, and collaborative AI/ML projects that don’t require dedicated hardware—for example, an academic collaboration that includes multiple institutions.To choose the right type of GPU, consider these three factors:Performance requirements. Is the raw GPU speed critical for your AI workloads? If so, bare metal GPUs are a superior choice.Scalability and flexibility. Do you need GPUs that can easily scale up and down to handle dynamic workloads? If yes, opt for virtual GPUs.Budget. Depending on the cloud provider, bare metal GPU servers can be more expensive than virtual GPU instances. Virtual GPUs typically offer more flexible pricing, which may be appropriate for occasional or variable workloads.Your final choice between bare metal GPUs and virtual GPUs depends on the specific requirements of the AI/ML project, including performance needs, scalability requirements, workload types, and budget constraints. Evaluating these factors can help determine the most appropriate GPU option.Choose Gcore for Best-in-Class AI GPUsGcore offers bare metal servers with NVIDIA H100, A100, and L40S GPUs. Using the 3.2 Tbps InfiniBand interface, you can combine H100 or A100 servers into scalable GPU clusters for training and tuning massive ML models or for high-performance computing (HPC).If you are looking for a scalable and low-latency solution for global AI inference, explore Gcore Inference at the Edge. It especially benefits latency-sensitive, real-time applications, such as generative AI and object recognition.Discover Gcore bare metal GPUs
How to Configure Grafana for Visualizing Kubernetes (K8s) Cluster Monitoring
Kubernetes monitoring allows you to observe your workloads and cluster resources, spot issues and failures, and efficiently manage pods and other resources. Cluster admins should prioritize tracking the performance and stability of clusters in these environments. One popular tool that can help you visualize Kubernetes monitoring is Grafana. This monitoring solution lets you display K8s metrics through interactive dashboards and real-time alerts. It seamlessly integrates with Prometheus and other data sources, providing valuable insights.Gcore Managed Kubernetes simplifies the Grafana setup process by providing a managed service that includes tools like Grafana. 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After installation, retrieve the admin password by using the command below:kubectl get secret --namespace default grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echoThen access the Grafana UI by port-forwarding:kubectl port-forward svc/grafana 3000:80Open your web browser and navigate to http://localhost:3000. Log in using the default username admin and the password you retrieved. Once logged in, you can configure Grafana to monitor your Kubernetes environment by adding data sources such as Prometheus and creating custom dashboards.You’ve now successfully set up Grafana for Kubernetes monitoring!Key Metrics for Kubernetes MonitoringUnderstanding metrics for Kubernetes monitoring allows you to visualize your cluster’s reliability. Key metrics are the following:Node resources. Track CPU and memory usage, disk utilization, and network bandwidth to understand resource consumption and identify bottlenecks.Cluster metrics. Monitor the number of nodes to understand resource billing and overall cluster usage, and track running pods to determine node capacity and identify failures.Pod metrics. Measure how pods are managed and deployed, including instances and deployment status, and monitor container metrics like CPU, memory, and network usage.State metrics. Keep an eye on persistent volumes, disk pressure, crash loops, and job success rates to ensure proper resource management and application stability.Container metrics. Track container CPU and memory usage relative to pod limits, and monitor network data to detect bandwidth issues.Application metrics. Measure application availability, performance, and business-specific metrics to maintain optimal user experience and operational health.Setting Up Grafana DashboardsYou can opt to design and tailor Grafana dashboards to monitor your Kubernetes cluster. This will help you better understand your systems’ performance and overall well-being at a glance.Log into Grafana. Open your web browser, go to http://localhost:3000/, and log in with the default credentials (admin for both username and password), then change your password if/when prompted.Grafana—Log In to Start MonitoringAdd data source. Navigate to Configuration and select Data Sources. Click on Add Data Source and choose the appropriate data source, such as Prometheus.Create a dashboard. Go to Create > Dashboard, click Add New Panel, choose the panel type (e.g., Time series chart, Gauge, Table), and configure it with a PromQL query and visualization settings.Adding a New Panel in Grafana DashboardOrganize and save the dashboard. Arrange panels by clicking Add Panel > Add Row and dragging panels into the desired rows. To save the dashboard, click the save icon, name it, and confirm the save.Gcore Managed Kubernetes for Kubernetes MonitoringWhether you’re getting started with monitoring Kubernetes or you’re a seasoned pro, Gcore Managed Kubernetes offers significant advantages for businesses seeking efficient and reliable Kubernetes cluster monitoring and container management:Ease of integrating Grafana: The service seamlessly integrates with Grafana, enabling effortless visualization and monitoring of performance metrics via dashboards.Automated control: Gcore Managed Kubernetes simplifies the setup and monitoring process by using automation. This service conducts health checks on your nodes, automatically updating and restarting them when needed to keep performance at its best.Enhanced security and reliability: Gcore Managed Kubernetes guarantees the management of nodes by integrating features like automatic scaling and self-repairing systems to maintain optimal performance.Discover Gcore Managed Kubernetes, including automated scaling, one-click provisioning, and Grafana integration.
TCO Comparison: Self-Managed Kubernetes vs. Managed Kubernetes Provider
Calculating the total cost of ownership (TCO) for Kubernetes requires identifying all major expenses, including infrastructure costs, personnel costs, and potential cloud provider fees. With a clear picture of TCO, you can make a more informed decision when choosing between self-managed (self-hosted) Kubernetes and a managed Kubernetes provider. The TCOs of the two approaches are significantly different, and this article will show you exactly how and why.TCO Comparison SummaryThe table below shows the key aspects of the TCO comparison between self-managed Kubernetes and managed Kubernetes providers. It compares infrastructure expenses, including provider fees, and an engineer’s salary.For this comparison, we’ll assume that a company would need only one DevOps engineer for managed Kubernetes, whereas companies opting for self-hosted Kubernetes would need three. We’ll look at rented cloud VMs for self-hosted, and out-of-the-box K8s clusters for managed Kubernetes—two standard scenarios for a fair comparison. For both scenarios, the infrastructure costs shown in the table are the average when considering AWS, Azure, Google Cloud, and Gcore. InfrastructureEngineers’ salaryTotal annual costSelf-hosted Kubernetes$13,737.64$321,500$335,238Managed Kubernetes$6,157.8$107,167$113,325As you can see, the TCO of self-hosted Kubernetes is almost three times higher than that of managed Kubernetes. Let’s explore the reasons for this major cost discrepancy.Infrastructure Cost ComparisonKubernetes is a free software. But to run it, you have to rent or buy infrastructure, such as VMs or physical servers. The way you do so differs depending on whether you opt for self-hosted or managed Kubernetes. To understand infrastructure costs, we need to take a closer look at each method in turn and explore the components required.Self-hosted KubernetesIf you choose to run K8s independently, you’ll need to rent VMs for the Kubernetes master node (the control plane) and worker nodes. Let’s consider a production-grade cluster consisting of the following:3 VMs for the control plane, required for fault tolerance2 VMs for the worker nodesFor simplicity, we choose VMs with a configuration suitable for an average web project: 8 vCPU, 16 GB RAM, and 75 GB SSD.Here is the pricing* offered by four cloud providers for VMs available in the US:ProviderVM types and resourcesTotal annual cost of five VMsAWSc6g.2xlarge—8 vCPU, 16 GB RAM, 75 GB SSD$12,273.6AzureA8 v2 series—8 vCPU, 16 GB RAM, 64 GB SSD**$17,764.2Google CloudN1 series—8 vCPU, 16 GB RAM, 75 GB SSD$16,721.33Gcoreg1 standard series—8 vCPU, 16 GB RAM, 75 GB SSD$8,191.42Average$13,737.64* Prices are for on-demand VMs; no commitment; no VAT; ingress traffic is not included.** Azure only offers fixed volume sizes for built-in storage.Managed KubernetesWith managed K8s, you don’t have to worry about renting separate VMs and setting up the Kubernetes software. You choose the VM configurations for your worker nodes, and a provider prepares them for you. The result is an out-of-the-box Kubernetes cluster.Sometimes, you also have to consider fees for control plane management (fixed) and egress traffic (consumption-based). Providers like AWS, Google Cloud, and Azure charge for this, while others—like Gcore—don’t.Here are the prices* offered by four cloud providers for similar cluster configurations in the US:ProviderControl plane managementCluster of two worker nodesTotal annual costConfigurationAnnual costAmazon EKS$8768 vCPU, 16 GB RAM, 75 GB SSD$4,909.44$5,785.44AKS (Azure)$8768 vCPU, 16 GB RAM, 64 GB SSD**$7,048.08$7,924.08GKE (Google)$876X vCPUs, X GB RAM$6,832.08$7,708.08Gcore Managed Kubernetes08 vCPU, 16 GB RAM, 75 GB SSD$3,213.6$3,213.6Average$6,157.8* Prices are for on-demand VMs; no commitment; no VAT; ingress traffic is not included.** Azure only offers fixed volume sizes for built-in storage.Engineer Cost ComparisonTo maintain a production-grade cluster for an average web project, you need:For a self-hosted K8s cluster—3 DevOps engineersFor a managed K8s cluster—1 DevOps engineerTo learn more about the technical reasons behind these calculations, read our article on the difference between managed and self-managed Kubernetes.According to Glassdoor, the median salary for a DevOps engineer is as follows:In the US: $140,000In Germany: €69,000 (or $74,333, the highest in Europe) DevOps salary in the USDevOps salary in GermanyAverage annual salarySelf-hosted Kubernetes (3 engineers)$420,000$222,999$321,500Managed Kubernetes (1 engineer)$140,000$74,333$107,167Final ComparisonHere is the final TCO comparison between self-managed Kubernetes and managed Kubernetes providers:ProvidersInfrastructureEngineers’ salaryTotal annual costBy providerAverageSelf-hosted KubernetesAWS$12,273.6$13,737.64$321,500$335,238Azure$17,764.2GCP$16,721.33Gcore$8,191.42Managed KubernetesAmazon EKS$5,785.44$6,157.8$107,167$113,325AKS (Azure)$7,924.08GKE (Google)$7,708.08Gcore Managed Kubernetes$3,213,6Summing UpPlease note that these approximate calculation probably aren’t exactly what you’ll experience. The actual numbers will depend on many factors, including:Size and complexity of your projectLocation where you hire engineers and deploy a K8s clusterChoice of providerHow you consume and scale computing resourcesHowever, the difference between the TCO of the two methods is relevant to what we got above: the total cost of ownership of self-managed Kubernetes is about three times higher than that of managed Kubernetes.The main reason is that Managed Kubernetes means a provider handles many of the most complex operations. This includes managing the underlying infrastructure and control plane, regular and security upgrades, monitoring, scaling the cluster, and, critical to production, high availability guaranteed by an SLA. With self-hosted K8s, you have to do that yourself, which means a larger infrastructure, larger team size, and higher salary costs.ConclusionUnderstanding the TCO difference between self-managed Kubernetes and a managed Kubernetes provider can help you choose a solution that is more suitable for your team and meets your budget. Kubernetes cost analysis can also help you identify areas for optimization, such as right-sizing your infrastructure or optimizing workloads for better resource utilization. However, the TCO isn’t the only aspect of choosing how to run Kubernetes: you should also consider things like the setup and maintenance responsibilities, as well as your project requirements.If you’re looking for reliable, high-performance, and scalable Kubernetes clusters, try Gcore Managed Kubernetes. We offer free cluster management with a 99.9% SLA, bare metal and GPU support for worker nodes, and free egress traffic.Explore Gcore Managed Kubernetes
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