Load balancers are intelligent gatekeepers that optimize the functionality of your services and offer impeccable service to your clients. It helps with your apps’ high availability, performance, and scalability.
While load balancers are vital to modern-day data centers and cloud environments, how do you ensure they perform optimally and bring the desired ROI to your business?
That’s where load balancing metrics come into play.
These metrics (often numerical values) reveal the state of your load balancers and illuminate areas of improvement. So, let’s explore these metrics in this blog.
Why Is It Essential to Measure Load Balancers’ Performance?
Load balancers evenly distribute the load to your server farms to ensure no single server is exhausted and your clients get uninterrupted service. They also look for packet data to mitigate cyber threats and protect your application and customer data.
Since load balancers perform business critical tasks and any interruption can have a direct hit on your bottom line, it’s essential to optimize their usage. Thus, quantifying load balancing performance helps you to:
- Identify loopholes and fix issues in the network or system
- Offer unparalleled client experience
- Prevent bottleneck in the backend
- Maintain and optimize system health
- Boost efficiency and accuracy
So, without further ado, let’s now dive into the blog post.
11 Load Balancing Metrics
1. Active connection
This is the number of active connections between your client and the target server. This metric helps you understand whether or not the load is evenly distributed amongst your serves in the cluster.
Finally, a deeper dive into these metrics helps you monitor how smoothly your inbound and outbound flow runs at IPv6 and IPv4 levels.
2. Failed connection count
Contrary to active connection, failed connection count measures the number of rejected connections.
This helps you inspect the various reasons for rejected requests, such as exhausted servers, unhealthy load balancers, or uneven load distribution.
Furthermore, these metrics also help you understand whether your applications are scaling appropriately or not and whether there is an anomaly that you should look for.
3. Request count
This is the total number of requests coming from all your load balancers. Requests can also be counted on a per-minute basis which gives you an insight into the efficiency of the load balancer. In addition, this metric highlights routing or network issues.
Latency is the time it takes for a connection request to return to the user. This can be called the most critical metric as it’s directly tied to your user experience.
A longer wait time, or latency, may encourage a user to switch to other websites or services, making you lose precious customers’ time.
Thus, it’s critical to analyze and optimize latency for your customers to access your resources with minimal friction.
Finally, latency can be monitored either on a per load balancer basis or as an average over time to identify the root cause of the problem and fix that. This metric counts the seconds, and the report is generated percentile-wise.
5. Error rate
Another great way to check the performance of a load balancer is to monitor the error rate. This can be done either on a per load balancer basis or over time.
The error rate can be monitored on the frontend and backend; while frontend means the number of connection requests returned to the client, which indicates a configuration issue, backend, on the other hand, implies a misalignment of communication between the load balancer and the server.
6. Healthy/unhealthy hosts
Knowing the number of unhealthy hosts helps you mitigate the risk of a service outage.
In addition, this metric enables you to gain insight into the number of unhealthy hosts and conduct maintenance in advance to avoid problems like latency or unavailability.
7. Fault tolerance
Load balancers are infamous for distributing load efficiently among the backend servers, and fault tolerance helps you measure just that.
Fault tolerance essentially means the ability of a system to perform efficiently despite having a faulty server or two. Thus, this is yet another metric that helps you understand how well your load balancers perform.
Throughput is the measurement of a successfully completed job which, in the case of the load balancer, means the number of requests successfully completed per unit time.
It’s an insightful metric to measure since higher throughput indicates higher efficiency of your load balancers, signaling healthy load balancing.
9. Migration time
It’s the time taken for a request to be transferred from one machine to another. The idea here is to minimize the migration time to enhance the efficiency of your load balancers.
Reliability is yet another feature of load balancers that makes measuring it critical. Reliability can be measured by uptime and consistent performance over a period of time to check the efficiency of the load balancer.
11. Response time
Response time is the time algorithms take to respond to a request. This includes a sum total of waiting time, transmission time, and service time that the system requires. Therefore, minimizing response time should be your goal to optimize performance and efficiency.
Load balancers help stabilize traffic, maintain the health of your servers and offer unparalleled service to your clients. Furthermore, they keep your load balancer and servers healthy and avoid any interruption on the client’s end.
Thus, these 11 load balancing metrics are a great starting point to gauge the performance of your load balancers, reveal their health status, and enhance their efficiency.
Learn more about what load balancers are, along with their type and benefits, here.