Best Load Balancing Algorithms and Techniques to Improve Performance
We’ve all been there before: A new product or website goes live, or you’re trying to watch the latest celebrity boxing match on your favorite streaming service. But instead of a fast-loading website or a smooth crispy video, all you get is buffering, broken web pages, and a poor experience.
This is what happens when incoming requests aren’t taken into account, and it’s often a result of poor (or nonexistent) load balancing. And with over 20 years of server management and hosting experience, we at ServerMania know exactly how important a load balancer can be.
At its core, load balancing is the process of distributing incoming traffic across multiple servers to ensure reliability and speed. But not all load balancing approaches are created equal. Understanding the right techniques and algorithms tailored to your traffic patterns can mean the difference between a seamless user experience and costly downtime.
How Load Balancing Impacts Server Performance
The world is more connected now than ever before, with services spanning across the globe. But with all these connections, it’s impossible to rely on a single server setup–it’s nearly impractical to scale up without adding more, but how do you balance the requests and resources between them?
Server load balancing is the process of distributing traffic across multiple servers to ensure your resources are being used optimally, which results in lower latency, higher availability, and better scalability. By using them, organizations can handle higher volumes of traffic while still maintaining performance and stability.
So what exactly do load balancing solutions do? Let’s break it down:
- High availability and redundancy. Distributing traffic across multiple servers ensures that if one server goes down or becomes overloaded, another can seamlessly take it’s place and minimize downtime.
- Better performance and scalability. Incoming application traffic is automatically routed to the best available server, reducing response times and allowing systems to scale horizontally as demand grows.
- Efficient resource utilization. Dynamically allocating requests ensures that no single server gets overwhelmed, leading to more efficient resource usage and lower operational costs over time.
Common Load Balancing Algorithms
Round Robin
The most common load balancing algorithm, this distributes requests in a simple circular order. Let’s say we have three servers (A, B, and C): The first request would go to A, the second goes to B, and so on.
Round robin is simple to implement and works for most situations, as long as servers have similar performance. However, this load balancing algorithm can quickly fall behind if there are significant differences between servers. Even if one server is significantly slower than the rest, it still receives the same number of requests.
Weighted Round Robin
Similar to it’s sibling, weighted round robin instead assigns each server a “weight” (as in a numerical value) to each server, based on it’s capacity or performance. Severs with better capacity and resources will get a proportionally greater number of requests, while lower-powered ones will receive less.
This makes it far more flexible than the traditional round-robin technique, and better for businesses that don’t have several identical servers on hand. The downside is these weights tend to be static, meaning they will need to be adjusted manually as conditions change.
Least Connections
As the name implies, this load balancing algorithm directs traffic to whichever node has the fewest active connections within the server farm. This way, businesses can utilize the server with the most available capacity at any given moment.
The advantage that least connections algorithms offers is that it’s a dynamic load balancing algorithm–it can respond to changes in loads, making it excellent for applications with long-lived connections. However, it also assumes that all connections will consume similar resources, which isn’t a guarantee. A single heavy connection might skew resource utilization, impacting other requests.
There’s also a weighted least connections algorithm that operates in a similar way, adding weights to servers and distributing loads based on a ratio of active connections to the server’s weight. This offers nearly the same advantages as the standard least connections algorithm, but with slightly more complexity.
Hash-Based Algorithms
Instead of relying on automation and distributing loads based on when connections come in, you can instead route traffic based on unique hash key from incoming connections, such as an IP address, URL, or HTTP headers.
These allow you to determine what server should handle the request, and then maps that client to the same server every time. That way, there can be some form of session persistence without for applications that don’t support them natively. The downside is that these methods aren’t nearly as flexible as others; users will still be sent to the same server for consistency every time, even if that server is under a heavy load.
Adaptive Algorithms
Finally, the last option relies on using real-time analysis like health checks, performance metrics, or machine learning to determine what particular server gets the request. These can be highly responsive and rapidly adjust to changing conditions as they happen, and tends to be the most efficient way to enable dynamic load balancing.
Yet, these also tend to be the most complex way to implement a load balancer. They require significantly more overhead to develop, analyze, and fine-tune in a fast and reliable manner.
Load Balancing Techniques for Different Traffic Patterns
When it comes to maintaining (and improving) server performance, adapting your load balancing strategy to different traffic patterns is essential–not all network traffic is the same, and you can’t tackle them all with the same method. Different situations like traffic spikes, steady flows, and large file transfers require different approaches to guarantee reliability and efficiency (even unmetered servers).
Traffic Spikes
Sudden surges can create traffic bottlenecks, leading to slow response times or even crashes. Load balancing can help prevent these issues, keeping services online even with such high volumes. Using a load balancer like Round Robin can help distribute incoming client requests evenly across the server farm fairly, while Least Connections can redirect traffic to less populated servers, ensuring no single server becomes a bottleneck.
Steady Flows
For consistent and predictable traffic, algorithms like Weighted Round Robin and IP Hashing make great options. The first factors in server resources and assigns user requests to more powerful servers to evenly distribute traffic. IP Hashing, on the other hand, directs requests from a specific IP address to the same server, creating session persistence and improving response times.
Large File Transfers
Handling large files means using a solution that can handle a large load without taking up network bandwidth or overloading individual servers, such as Least Bandwidth. It routes data-heavy requests to available servers with the most bandwidth, preventing any single server from getting bogged down.
Integrating Load Balancing into Server Infrastructure
Integrating load balancing into your existing infrastructure can be complex, but with a solid and laid-out approach it becomes much more manageable. Let’s take a look at a typical path to set up a load balancer:
- Assess your current traffic patterns: Analyze your server workload, including peak times, steady flows, and high-demand events, to figure out out your specific needs.
- Choose the right load balancer: Figure out what strategies fit best within your needs and align with your business goals and resources.
- Inventory your existing infrastructure: Document servers, networking hardware, and application requirements to make sure your chosen solution actually fits your needs and constraints.
- Set up a testing environment: Before a full deployment, create a test environment to put your load balancer through realistic tests to make sure it’s an effective solution before a full deployment to production.
- Start slow and implement gradually: Incrementally roll out your load balancer, starting with less critical workloads to adjust and monitor as needed (while minimizing disruptions).
- Tweak as needed: Once it’s fully deployed, keep tabs on your load balancer with real-time analytics and make ongoing adjustments to maintain effectiveness.
Choosing Between Software and Hardware Load Balancing
You have two options when it comes to implementing a load balancer: do you opt for a software load balancer, or use a physical solution?
Hardware load balancers provide dedicated resources for ultra-low latency and high throughput, making them an excellent choice for enterprises with heavy traffic or strict performance benchmarks. They’re great for handling encrypted traffic and come with strong security features.
Software load balancers, on the other hand, offer flexibility and scalability, integrating into both cloud-native and hybrid environments. They’re more cost-effective and make a great fit for businesses with dynamic traffic or those using containerized architectures.
Effortless Scalability Starts with the Right Balance
Adding load balancing into your server infrastructure is essential for maintaining performance, reliability, and a great user experience. By understanding your traffic patterns and sticking to the right solution (whether it’s convenient software or powerful hardware solutions), you can distribute workloads evenly among different servers for maximum reliability and resource usage.
And if you’re not sure where to start–or if you want an expert opinion–ServerMania’s got your back. With over 20 years of experience handling servers big and small, we can get you right where you want to be. Contact us today and let’s get started.
