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Sudden traffic spikes are unavoidable, so preparation must be done in advance.
Strengthen infrastructure with CDN, scaling, and caching to reduce load.
Remove back-end bottlenecks through caching, CQRS, and asynchronous processing.
Improve client-side performance with HTTP/2/3, compression, and lazy loading.
When system capacity is exceeded, regulate user flow with virtual waiting rooms or rate-control solutions to maintain service stability.
Traffic surges can cripple service stability at the most unexpected moments.
During ticket releases, flash sales, course registrations, flight bookings, or event promotions, user traffic concentrates within a short time window. The resulting spike in requests can overwhelm web servers, application servers, and databases far beyond normal operating levels. This leads to slow response times, 502/504 errors, page load failures, and in severe cases, complete service outages.
In this article, we break down the technical strategies for effectively handling large-scale concurrent traffic, organized step by step from an ROI perspective. Taking into account cloud environments, web resource architectures, and transaction characteristics, we focus on practical, actionable measures that can be applied in real service operations.
When a webpage first loads, the browser downloads dozens to hundreds of static files such as JS, CSS, images and fonts. If these resources are delivered directly from the web server all at once, the server’s network bandwidth is consumed excessively and its processing capacity drops sharply. Under heavy traffic, responses for static assets slow down, degrading the overall page experience for users.
Response Strategy
Cache all static resources (JS, images, CSS, fonts, etc.) through the CDN.
Prioritize using the CDN services provided by your cloud environment (AWS CloudFront, NCP CDN, Azure CDN, etc.).
When possible, replace external libraries with public CDNs (e.g., jsDelivr, cdnjs) to improve load speed.
Cache fully staticized HTML pages at the CDN edge when applicable.
Because a CDN delivers resources from PoP(Point of Presence) servers that are geographically close to users, it not only improves performance but also significantly reduces load on the origin web server.
Even if static resources are offloaded, extremely heavy traffic can still overwhelm the server infrastructure. This is where scaling becomes essential.
Response Strategy
Scale-Up: Improve the server’s core capabilities such as CPU, memory, and disk performance.
Scale-Out: Expand by running multiple identical server instances in parallel (using an Auto Scaling group)
In a cloud environment, load balancers combined with Auto Scaling make it possible to handle sudden spikes in traffic.
For Kubernetes environments, use HPA(Horizontal Pod Autoscaler) or KEDA(Kubernetes Event-Driven Autoscaling) to enable horizontal autoscaling.
Scaling a WAS server vertically or horizontally increases concurrency, thereby improving the number of transactions processed per second. However, this approach merely expands physical capacity and does not fundamentally address the root issue of traffic concentrating at specific moments.
When every request triggers calls to an RDBMS or external APIs, backend bottlenecks inevitably arise. Transactions such as login, payment, and authentication take longer to process and are difficult to parallelize, which leads to delays across the entire system.
Response Strategy
Redesign frequently accessed data using a caching layer such as Redis or Memcached
Pre-cache authentication, session data, and common codes, then refresh them periodically based on TTL
Apply CQRS (Command Query Responsibility Segregation) to split read and write operations
Offload certain requests to asynchronous message queues (Kafka, SQS, RabbitMQ, NATS) to decouple them from the user-facing response
When using request queues, maintain service quality by designing appropriate processing rates and priority queues
This strategy reduces server resource consumption, enabling the system to handle a higher number of concurrent users. However, if transactions surge to a level that the backend cannot realistically process, this approach alone becomes insufficient. While scaling the database might be considered, DB expansion is inherently limited and does not guarantee proportional performance gains.
Web Server Scaling | Application Server Scaling | DB Scaling | |
|---|---|---|---|
Simple H/W expansion, | H/W expansion and integration with service logic H/W, OS, and license purchases, | Scale-Up |
|
Scale-Out |
| ||
Note: Services that rely on DB read/write operations face strict limits in server scaling and cannot guarantee stable performance. | |||
Beyond server and infrastructure improvements, there are also optimizations that can be applied on the client side, namely in the browser. Web performance is influenced not only by server specifications but also by the way content is delivered and the order in which it is rendered.
Response Strategy
Apply HTTP/2 or HTTP/3 to achieve multiplexing-based parallel downloads.
Minimize transfer size using Brotli or Gzip compression.
Load JS/CSS asynchronously only when needed (async, defer, code-splitting)
Apply lazy loading to images and videos (using Intersection Observer, etc.).
Use browser resource hints such as Preload, Preconnect, and DNS Prefetch.
Collaboration with frontend engineers is essential to enhance initial render times and the user’s perceived loading experience.
If a sudden surge of concurrent traffic reaches a level that even CDN offloading, scaling, and caching cannot withstand, it becomes necessary to control the traffic inflow itself. No matter how much server capacity is increased, bottlenecks are inevitable when the volume of transactions exceeds what the system can realistically process.
The fundamental solution is to distribute users over time. Instead of allowing everyone to enter the system at once, users are admitted sequentially, and only a manageable number are allowed to access the service at any given moment.
Response Strategy
Provide users with a virtual waiting room page before granting access to the web service.
Allow only as many users as the system can handle, and place the rest in a sequential waiting queue.
The virtual waiting room interface displays queue position, estimated waiting time, and auto-refresh updates.
Dynamically modify the admission limit to adapt to changing server load in real time.
Can be enabled only in transaction-heavy flows like authentication, ticketing, or payment processing.
Configure traffic limits per API or URI, with individual restriction policies applied to each endpoint.
Although this approach may appear to artificially restrict concurrent users, it is in fact a protective strategy for both the system and its users. It prevents full service collapse caused by system overload, while providing users with a predictable and fair access experience.
Where Virtual Waiting Rooms* Provide the Highest Value :
Scenarios where traffic concentrates at a specific moment (product drops, reservation openings, etc.)
When back-end resources are limited (DB bottlenecks, external APIs, authentication servers, etc.)
When you need to control traffic flow without interrupting the service
When a high mobile-user ratio makes the waiting experience especially important
Sudden traffic spikes are unavoidable. What truly matters is whether you have a strategy prepared in advance. Strengthening infrastructure, distributing resources, and eliminating bottlenecks are essential steps. However, even after all of these are in place, a service can still fall back into danger if it has no mechanism to control the traffic flow itself when a surge of concurrent requests hits all at once.
This final layer is exactly where a virtual waiting room or rate-control system becomes essential. It regulates user flow so that access is granted only within the system’s safe capacity, while providing users in the queue with a predictable and transparent waiting experience.
[References]
*[Virtual Waiting Room]. Namuwiki, as of November 2025
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