Auto-scaling is the process of ramping capacity up or down in response to changing application traffic demands. This process is typical for applications hosted on cloud computing platforms. The goal is to keep your applications running as smoothly as possible regardless of traffic volumes.
How does auto-scaling work?
Let's first examine a traditional setup. When a service experiences higher traffic volumes, the server will commit more resources to ensure strong performance and high availability. By accessing a future-proofed capacity of available CPU power and memory (based on hardware components), a service can effectively scale capacity and process more concurrent requests. However, this can be tougher to automate, and procuring high-performance servers is costly up front.
Instead of adding resources, what if we simply increased the number of virtual instances with fixed resource allocations? You'll often see this in Kubernetes environments, for example, where pods multiply to support increased demand. Generally though, a cloud computing platform might automatically add nodes to account for spikes. This is called "scaling out" (horizontally). When a service's traffic begins to dwindle, the number of available pods or nodes will automatically decrease. Instead of buying one powerful machine, we're leveraging numerous tiny instances to handle traffic spikes.
The challenges of auto-scaling
While auto-scaling attempts to simplify application delivery through automation, setting it up can be complex. There are a number of ways to configure scalability based on auto-scaling groups, memory usage thresholds, or CPU load. An auto-scaling group is typically assigned a minimum and maximum limit to determine just how elastic that process can be. Once a threshold is crossed, the system will respond based on pre-configured rules.
Plus, each piece of your backend (web servers, databases, etc.) must have their own auto-scaling rules to ensure consistency and predictability. This emphasis on separate layers also requires expertise to match.
Applications also need to be designed to account for auto-scaling. Microservices have an advantage over monolithic architectures here, since each decoupled service is easier to manage individually. Statelessness is important to enable horizontal scaling, since states would automatically be lost as client requests hit different pods, servers, or processes.
Finally, auto-scaling doesn't guarantee high performance and availability. While we can establish auto-scaling rules for the traffic spikes we anticipate, higher activity spikes can introduce cost concerns for organizations that need to reserve more virtual capacity. A good auto-scaling strategy doesn't just consider servers but also encompasses databases and other infrastructure components under dynamic load. Even then, it's incredibly challenging to account for all breaking points within a system.
Does HAProxy support auto-scaling?
Yes! HAProxy products work seamlessly with major cloud computing platforms such as AWS. By running HAProxy Enterprise load balancing nodes in front of these backends, it's possible to distribute traffic effectively.
HAProxy Enterprise Kubernetes Ingress Controller or the tandem of HAProxy Enterprise plus HAProxy Fusion Control Plane can effectively load balance traffic in Kubernetes environments, where K8s' ephemeral nature lends itself to auto-scaling dynamic workloads.
