You wake up thinking about how your computer manages thousands of tasks simultaneously. But here is the uncomfortable truth… many engineers implement load balancing without understanding how it actually works.
They treat it like a magical box that makes things “not crash,” then move on. This gap costs companies millions.
Why Load Balancing Matters
Let’s see, your web server receives 1,000 requests per second, but it can only handle 500.
You have two choices. Turn people away, or distribute the work.
The second option separates companies that crash during traffic spikes from companies that handle Black Friday like any other day. That is the power of load balancing.
A load balancer sits between users and servers. It intercepts incoming requests and decides which server handles each one.
The decision it makes determines everything. Fast responses or timeouts. Equally stressed servers or some are melting down. System resilience or total failure.
Without load balancing, you are betting your business on never having success. With it, success becomes possible.
Round Robin: The Naive Approach
Let me start with the simplest algorithm. Round robin treats all servers like identical workers standing in a line.
Request one goes to server A, request two goes to server B, and request three goes to server C. Then, it cycles back to server A.
The beauty is simplicity. A five-year-old could implement it. You maintain a counter, increment it, use modulo math, done.
But simplicity has a price. Round robin assumes all servers are equally powerful and all requests need equal work.
In reality, this is rarely true. Your request might take two milliseconds. The next one might take two seconds.
Round robin sends them to different servers anyway. One becomes overloaded while the other sits waiting.
Worse, round robin does not check server health. If one crashes, round robin keeps sending it requests anyway.
Your application drowns, not knowing it is already dead.
Least Connections: Getting Smarter
The next evolution asks a practical question. Which server currently has the fewest active connections?
This is better. If server B has one hundred connections and server C has five, the next request goes to server C.
Over time, this distributes the load more evenly, especially when requests have varying durations.
But least connections make assumptions too. It assumes that active connection count equals the server load.
For lightweight requests with high throughput, this works. For requests with long idle periods, it fails.
You send new requests to the server with the most stale connections instead of the most available resources.
Weighted Round Robin: Domain Knowledge
Some engineers realised something important. Not all servers are created equal.
Maybe you have a newer machine with better specs. Maybe one server specialises in image processing and another handles text.
Should they receive the same number of requests? No.
Weighted round robin lets you inject domain knowledge. You configure each server with a weight.
A weight of one gets one request per cycle. A weight of three gets three requests per cycle.
Your powerful server handles three times the traffic. Specialised servers concentrate on their strengths.
This works remarkably well when you know your infrastructure and configure weights correctly.
The problem? Knowing the right weights requires monitoring, testing, and constant adjustment.
When infrastructure changes, weights become stale. Many teams set weights once and forget about them.
That defeats the entire purpose.
IP Hash: Sticky Requests
Some applications have a state. A user logs into server A, and session data stays in memory.
If the load balancer sends the next request to server B, the session is lost. User gets logged out.
IP hash solves this elegantly. It hashes the client IP address and always sends requests from the same IP to the same server.
Users get consistent sessions. The algorithm is deterministic… no state tracking needed.
Multiple load balancers can run independently and make the same decision. That is powerful.
But IP hash has a fatal flaw in modern applications. Behind corporate networks, many users share the same IP address.
Also, IP hashing breaks when you add or remove servers. The hash changes, and all sessions break.
Many users get logged out simultaneously because their hash now points to a different server.
Least Response Time: Performance Metrics
Smart operators realised they could measure what actually matters. Not connection count. Not IP address.
But how fast is the server responding right now?
Least response time algorithms track the average response time of each server and send requests to the fastest one.
This captures everything. CPU usage, memory pressure, disk I/O, network latency, anything affecting performance.
The catch? Measuring response time requires active monitoring of thousands of requests.
You need to calculate averages, handle gradual slowdowns, and balance exploration versus exploitation.
Should you send requests to slower servers to see if they recovered? How often? Not too much or you hammer them.
Random Selection: Surprisingly Effective
Here is something that surprises people. Random selection is not as bad as it sounds.
If you simply choose a random server for each request, you get reasonably even distribution.
Why? Because randomness is surprisingly good at finding balance.
Over large numbers of requests, random selection converges toward equal distribution. Each server gets approximately the same percentage.
The advantage is simplicity. No state tracking. No measuring. Just pick a random number and route there.
Even when servers have different power levels, random selection works okay because it does not amplify small differences into big problems.
The disadvantage? Random selection ignores information.
If one server is overwhelmed, random selection sends more requests to it thirty percent of the time anyway.
If one server crashed, random selection still routes to it until health checks catch it.
For small numbers of servers without good monitoring, random selection is legitimate. It is simple and good enough.
Consistent Hashing: The Distributed Solution
When you have many servers or need to maintain state across them, consistent hashing becomes essential.
This powers content delivery networks, distributed caches, and peer-to-peer systems.
The core idea is elegant. You arrange all servers in a circle… imagine a clock.
You hash each client ID to a point on the circle. Then find the first server clockwise from that point.
That server handles the request.
The magic happens when you add or remove a server. Instead of everything rehashing, only a fraction of requests redirect.
When you remove a server, its requests go to the next server clockwise. Only local changes happen.
When you add a server, only requests within its range get redirected. Ninety percent of traffic stays stable.
This property makes distributed systems feasible at scale.
But consistent hashing requires understanding its properties. Virtual nodes help distribute load evenly when servers differ.
Hash function choice matters. And when servers have truly different performance, consistent hashing alone is not enough.
The Real World: Combining Strategies
In practice, production load balancers never use a single algorithm.
They combine multiple techniques intelligently.
Many modern systems start with least response time for primary balancing because it measures what actually matters.
They layer in IP hashing for session affinity. Users stick to the same server for their session duration.
Once sessions expire, requests get rebalanced fresh. Best of both worlds.
They implement consistent hashing for cache coherency. Cache nodes get assigned by consistent hashing so lookups go to the right place.
They use health checks that actively detect failures and remove unhealthy servers.
Some systems use machine learning to predict server performance. Others use game theory thinking about adversarial clients.
Sophistication depends on scale and specific requirements.
Implementation Realities
When you actually build this, hard truths emerge fast.
No algorithm is perfect. Every choice makes tradeoffs.
Round robin is simple but ignores reality. Least connections is smart but makes wrong assumptions. Least response time is intelligent but requires monitoring overhead.
Your choice of algorithm matters less than you think when you have excellent health checks.
A suboptimal algorithm with great health checking beats an optimal algorithm with blind spots.
Many failures come from misconfiguration, not bad algorithms.
Teams deploy load balancers and never tune them. They set round robin, declare it good enough, ignore it for years.
Infrastructure evolves around it while the balancer stays frozen in time.
Conclusion
Load balancing algorithms are unsexy. They do not get press coverage or awards.
But they determine whether your system can handle success or crashes under it.
Understanding these algorithms is practical, not academic.
The next time someone asks you to design a scalable system, you will not just draw a box labelled “load balancer.”
You will think about which algorithm fits your constraints. You will consider what happens when servers fail.
You will understand the tradeoffs and explain them clearly.
That is the difference between cargo cult engineering and real expertise. And that expertise lives in algorithm details that most people never think about.