Which Traffic Patterns Need Load Balancing Most?
You need load balancing most when your traffic is uneven in time, uneven across users, or uneven in how heavy each request is. If your traffic spikes fast, piles up behind a few hot endpoints, holds long connections open, or drifts across regions and devices, you should balance it.
I’ll keep it simple: if you see sudden surges, sticky hotspots, or slow stragglers, you need a load balancer.
And if you think “we’re small, we don’t need it,” your site will be a breeding ground for failures, and they won’t be tens, hundreds, or even thousands, but cascading outages that multiply when you least expect them.
The Patterns That Need Load Balancing Most
It’s always good to have, but it becomes essential in the following scenarios:
1. Spiky And Bursty Traffic
If your traffic arrives in waves, not a steady stream, you’re the prime candidate. Think launches, paydays, flash sales, breaking news, or social media mentions where requests jump 10x in seconds. Without a load balancer, one server melts while others sit idle, queues grow, and everyone gets timeouts.
You want a load balancer algorithm that spreads connections quickly and adapts to changing load. Round robin is ok for baseline, but least connections or power-of-two choices respond faster to spikes.
If the spikes are region-wide, use global traffic steering so new waves don’t land on the same cluster every time.
2. Uneven Hotspots Across Endpoints
You’ll see it when a single endpoint like /search or /checkout is suddenly popular. CPU-heavy or cache-miss prone paths turn into chokepoints.
Classic symptom: overall CPU looks fine, but p95 latency is bad for one route.
A Layer 7 load balancer that understands URLs, headers, and methods can split that specific route to a larger pool or to instances with more headroom.
Weighted round robin or dynamic weights based on observed latency are practical load balancing strategies here.
3. Slow Or Heavy Requests Mixed With Fast Ones
When a few requests are “elephants” and most are “mice,” the mice get stuck behind elephants on the same server. That’s head-of-line blocking at the instance level. If that’s you, prefer least time or least outstanding requests rather than simple round robin.
Queue-length aware algorithms and EWMA-style latency weighting help.
Another trick you’ll use is isolating heavy endpoints into their own pool, then using content-based routing at the load balancer to keep big jobs away from general traffic.
4. Long-Lived Connections
WebSockets, SSE, and gRPC streams keep connections open for minutes or hours. If those connections cluster on a handful of nodes, you get uneven memory and file descriptor usage. Use least connections and connection draining so new long-lived sessions land on the coolest nodes.
If you shard users by a key, consistent hashing keeps each user sticky to a shard without breaking when you scale out.
This is one of the types of load balancing where a subtle algorithm choice matters far more than raw capacity.
5. Session-Dependent Workloads
If you rely on in-memory sessions or local caches tied to the app node, you’ll see stickiness bake in hotspots. You can still load balance, but do it consciously.
Either externalize sessions so any node can serve a user, or use cookie-based or source-IP hashing to keep a user on the same node while distributing users evenly.
If you skip this, you’ll watch one node host all your loyal power users and run hot while others are cool.
6. Real-Time And Interactive Apps
Chats, games, trading screens, collaborative docs, live dashboards. You care about latency first, throughput second. You’ll want a load balancer close to users and algorithms that consider response time. Latency-based routing at the global layer sends each user to the closest healthy region.
Locally, least time with passive health checks avoids sick instances. If fan-out is involved, using a message broker helps, but you still need request balancing in front of your stateless edges.
7. Microservices And East–West Traffic
Inside your platform, services talk to services. Spikes often happen between specific pairs, not at the public edge. If service A talks mostly to service B partition p3, that shard gets hammered. A service mesh or client-side load balancing helps distribute requests per destination subset.
Use discovery plus outlier detection, and prefer algorithms like least requests so no single pod gets swamped. If you only balance at the gateway, you’ll miss these internal hotspots.
8. Geographic And Multi-Zone Audiences
If users are global or you depend on multiple availability zones, you need two layers. Global server load balancing sends users to the nearest or healthiest region, and a regional load balancer spreads requests within the cluster.
Anycast and latency-based DNS help at the top, while L4 or L7 balancing works inside each region.
If you have daylight cycles or country-specific events, this pattern is practically a guarantee that you need balancing.
9. Mixed Device And Network Conditions
Mobile clients on variable networks create lumpy traffic. Retries, timeouts, and reconnect storms appear during network blips and app restarts.
That turns into thundering herds against login or token refresh endpoints. Rate-aware and surge-friendly balancing helps, as does circuit breaking and per-endpoint pools. If you see retry storms during deploys, connection draining plus gradual rollouts through the load balancer smooths the pain.
10. Batch Windows And Event Storms
Cron jobs, nightly exports, cache warms, search reindexing, IoT device check-ins at the top of the hour. These are scheduled, which means they collide. Balance them or they’ll starve user traffic.
Weighted algorithms that deprioritize batch pools, or separate listener ports with different backends, give you control.
If it all lands at once, even a simple least connections policy is better than a flat spread that ignores queue depth.
Algorithm Matching
If you want a tiny decision helper, use this.
You can add health checks, outlier ejection, and slow-start as your traffic matures.
