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How to Choose the Best ADC Load Balancer for Kubernetes

Dave Blakey
February 12, 2020 at 2:55 PM

If you’re running your business applications in containers and managing those with Kubernetes, you’re probably aware of the limitations of traditional Application Delivery Controllers (ADCs) and Load Balancers in this environment. Traditional load balancers simply do not have the scalability and agility needed for cloud native deployments. Whether you’re already up and running in containers or just getting started, this blog will tell you all you need to know for choosing the best load balancer for Kubernetes.

But first, some context on Kubernetes and containers. IT networking is changing rapidly with the shift from deploying static, physical servers in data centers to implementing dynamic VMs and containers in public, private or hybrid clouds. The dynamic nature of modern networking enables not only scaling up and down with demand but also scaling out horizontally to achieve hyperscale with the significant added benefits of agility and cost efficiency. This is what the world’s largest hyperscale companies have figured out – like Amazon and Netflix.

To paraphrase the Cloud Native Computing Foundation’s definition of cloud-native, technologies and techniques like microservices, containers, service meshes, declarative application programming interfaces (APIs), along with powerful automation, allow enterprises to massively scale applications in dynamic cloud environments.

The knock-on effect for ADCs is that “cloud-native” needs a completely different way of ensuring application delivery load balancing and scalability.

How is Kubernetes Different?

Kubernetes (a.k.a., K8S) is a management platform for orchestrating containers that provides automation as well as declarative configuration, which is an easier and faster way to make incremental changes to applications and infrastructure. It provides the foundation for ensuring that the containers running your applications are always up and right-scaled according to changing capacity requirements.

When it comes to scaling the Kubernetes platform, there are three priorities for your team: visibility, performance and reliability. You need an ADC that fits the capabilities of Kubernetes to deliver detailed real-time insight into container and microservices performance as well as the right tools to guarantee application availability, security and fast response times.

If a load balancer isn’t built for cloud-native containers managed by Kubernetes, then it won’t integrate well with Kubernetes and will fail to meet these priorities.

These are the key features that a load balancer must have to support Kubernetes:

Full Cloud-Native Support

First of all, an ADC for Kubernetes must be cloud-native. It should be packaged as a Docker image that can run on any container, any cloud or any VM. It needs to be very easy to deploy and launch into Kubernetes in a matter of minutes from a single centralized management platform. It needs to be easy for developers to use through REST APIs. It also needs the ability to grow into multi-location, multi-cloud deployments for hyperscale across different geographic regions, and centrally manage ADC instances across Kubernetes containers as well as other cloud instances, such as AWS.

Automated Service Discovery

Support for Kubernetes automated service discovery is essential because it enables dynamic backends and scaling. You should be able to scale out ADC infrastructure in 10 seconds or less. Since Kubernetes exposes containers using the DNS name, an ADC should automatically discover servers that are online, all the containers that are running and their health status. In Kubernetes parlance, ADCs instantly discover clusters that comprise all the nodes and the worker nodes that host the pods (which are the components of your applications). In other words, automated service discovery is a faster, far more granular and more effective way to distribute traffic and workloads and scale deployments to achieve the best performance for your applications.  

Built-In Intelligence and Analytics

You need high visibility into the state of the microservices that make up your business-critical applications. Detailed telemetry, alerting, monitoring and performance data are a must. An ADC should display easy-to-access Layer 7 statistics and reports on all your containers with data including latency and HTTP error rates, as well as threat detection.

Cloud-Native Security

In a cloud-native environment, you need fast, highly scalable protection for your business-critical microservices, company data and your customers. A load balancer and WAF should monitor container deployments for threats such as DoS and botnets, raise alerts in milliseconds and automatically block attacks. A closed loop design should continuously monitor, detect and prevent attacks from damaging your business. The right ADC will also use machine learning techniques and artificial intelligence to understand traffic behaviour to better detect anomalies.

In addition to the technical requirements for Kubernetes, a cloud-native load balancer also needs a modern software-as-a-service (SaaS) payment model so that you only pay for what you need when you need it.

Conclusion

Containers, microservices and cloud-native architecture in general change the game for ADCs. The cloud-native era demands a new kind of ADC, like the new Snapt Nova. Nova is a Kubernetes-native ADC capable of true hyperscale. It provides dynamic, self-scaling ADC deployments capable of massive scale and automation with a focus on the telemetry and quality of your services. It achieves this by removing the cost and complexity from the data-plane and shifting the value to the control-plane. This enables you to dynamically manage your entire ADC deployment from a single intelligent platform.

To learn more about the world’s fastest and most scalable ADC platform, start your free Nova trial today.

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