How Does an Image Cache Server Reduce Latency in Image Delivery?
An image cache server reduces latency by storing frequently accessed images closer to the end user, cutting down the distance the data has to travel. This means faster delivery times, less strain on the origin server, and an overall smoother experience for the user.
You’ve probably noticed that when you visit a website loaded with images, sometimes those images take a moment to show up. Other times, they pop up instantly.
The difference often comes down to how close you are to the image’s source and whether an image cache server is being used to speed things up. When you're aiming for low latency, an image cache server is one of the most effective tools you can leverage.
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What is an Image Cache Server?
Think of an image cache server like a middleman that holds onto images that people request often. It’s designed to sit between the user and the origin server (where the images are first hosted) and stores copies of those images.
When a user requests an image, instead of pulling it directly from the origin server, which could be far away geographically, the request is routed to the cache server that’s physically closer to the user.
This process is often handled by a Content Delivery Network (CDN), which spreads image cache servers across different locations globally. The idea is simple: the closer the cache server is to the end user, the faster the image can load, and since the CDN latency is lower, the overall latency is reduced.
How Does Latency Work?
Latency is the time delay between when a request is made (e.g., when a user’s browser requests an image) and when the response is received. This delay can come from several factors, including:
- Physical distance between the user and the server: The longer the distance, the more time it takes for data packets to travel, leading to increased latency.
- Network hops: Data typically travels through multiple routers and servers before reaching its destination, adding small amounts of delay with each hop.
- Server processing time: The origin server needs to retrieve the requested image from its storage, process the request, and transmit the response.
How the Cache Reduces Latency
An image cache server works by storing copies of frequently requested images close to the user geographically or within their network topology.
This cached data remains in a CDN server for a temporary amount of time, after which it’s re-fetched, and the loop continues.
1. Proximity
When a user requests an image, that request typically goes to the origin server, which could be hundreds or thousands of miles away.
This distance adds propagation delay. The cache server, however, is positioned closer to the user, reducing the distance the data has to travel. This is known as reducing the "round-trip time" (RTT). A shorter RTT means lower latency.
If the origin server is in New York, but the user is in London, a CDN with a cache server in London would serve the image from the local server, significantly cutting down on the physical distance and therefore the transmission time.
2. Fewer Network Hops
Typically, data travels across multiple routers and networks (hops) to reach its destination. Each hop introduces a slight delay due to routing and packet forwarding.
Cache servers often exist within major ISPs or at the edge of different networks, meaning fewer hops are required for the data to reach the user.
For example, if your user is accessing an image from a CDN’s edge server (a cache server close to the user), it may bypass several network hops compared to fetching it directly from the origin server.
3. Reduced Server Load
Cache servers alleviate the load on the origin server by offloading requests for commonly accessed images. When fewer requests hit the origin server, its processing time and bandwidth usage decrease, improving overall responsiveness.
This prevents bottlenecks that can occur if the origin server is overwhelmed by too many requests. Lower load on the origin server translates to faster responses when it needs to serve something, thus reducing overall latency when images are served from cache.
4. TCP Handshake Efficiency
Every time a browser connects to a server, a TCP handshake occurs to establish the connection. This handshake requires back-and-forth communication between the client and server, which adds latency. When a cache server handles a request instead of the origin server, the handshake occurs over a shorter distance.
Additionally, most CDN cache servers support technologies like TCP Fast Open, which reduces the overhead associated with the TCP handshake by speeding up the initial request-response cycle.
5. Reduced Time to First Byte (TTFB)
TTFB measures the time between making a request and receiving the first byte of data. Cache servers reduce TTFB because the images are already preloaded and cached.
There’s no need for the server to process a database request or retrieve the file from slow disk storage—it’s immediately available in the cache’s memory or fast storage layer.
If the cache server has a solid-state drive (SSD) or uses in-memory caching (like Redis, Memcached or DragonflyDB), the retrieval time for the image is nearly instantaneous.
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6. HTTP/2 Multiplexing
Many modern CDN cache servers support HTTP/2, which allows multiple requests and responses to be sent simultaneously over a single TCP connection.
This reduces the overhead of having to open new connections for every image request. In a traditional setup without HTTP/2, browsers would have to create separate connections, increasing latency for large numbers of images.
By supporting multiplexing, cache servers can deliver multiple images in parallel over the same connection, minimizing delays associated with connection management and request queuing.
7. Edge Computing
Some advanced CDNs deploy edge computing techniques, which allow cache servers to pre-process or optimize images at the edge (closer to the user).
This reduces the amount of data that needs to travel across the network, further cutting down on delivery time. For instance, images may be resized or compressed on the edge server, ensuring only the optimized version is sent to the user.
In some mobile edge computing environments, intelligent caching frameworks have demonstrated a 50% improvement in cache hit ratios, which translates to significant reductions in image retrieval latency.
Compression and Optimization at the Cache Level
Cache servers often optimize images on the fly, using techniques like:
- Image Compression: The cache server can compress images before sending them to the user, reducing the file size and cutting down on the total amount of data that needs to be transmitted. This is especially useful on slow networks or mobile devices.
- Format Negotiation: A cache server might detect that the user’s browser supports more efficient image formats (e.g., WebP instead of JPEG). By converting the image to a more optimized format before delivery, the cache server reduces the file size and, thus, the amount of time it takes to deliver the image.
Network-Level Protocols to Reduce Latency
Many cache servers use protocols designed to reduce latency:
- Anycast Routing: Cache servers rely on anycast routing, a network addressing method that directs the user’s request to the nearest available cache server. This ensures that no matter where the user is located, the request is routed to the closest possible cache server for faster delivery.
- QUIC and HTTP/3: Some advanced cache servers support HTTP/3 (based on the QUIC protocol), which further reduces latency by avoiding the slow-start problem of traditional TCP connections. HTTP/3 enables faster, more efficient transmission of data over networks, particularly in high-latency environments.
When Does It Make Sense to Use an Image Cache Server?
If your site or app relies heavily on images, the answer is almost always “yes, it makes sense.”
Even if you have a fast origin server, distance matters, and an image cache server ensures that no matter where your users are, they’re getting a fast and responsive experience.
But there are a few specific cases where it’s an absolute no-brainer:
- Global Audience: If you have users spread across different regions, using a CDN with image caching ensures they all get equally fast access to your images.
- Image-Heavy Sites: Whether it’s an online store, a photo gallery, or a social platform, large images can slow things down. An image cache server helps avoid that.
- SEO Considerations: Google uses page speed as a ranking factor. Faster-loading images can boost your rankings, especially on mobile where latency issues are more noticeable.
A study conducted using Amazon Web Services (AWS) regions revealed that distributing image chunks across multiple nodes and caching frequently accessed images at frontend servers can drastically reduce data retrieval times.
In these experiments, caching in-memory at strategically placed servers reduced access latency by over 50%, especially for high-demand images
Impact of Image Cache Server on Latency for a Global Audience
Let’s talk numbers, taking in a scenario where we need to transmit some image data from one place to another. Here are our assumptions:
- Origin Server Location: New York, USA
- User Location: London, UK
- Image Size: 1MB
- Network Speed: 100 Mbps
- Round Trip Time (RTT) between New York and London: 80ms
- Round Trip Time (RTT) between a London-based cache server and the user: 15ms
- Number of Network Hops (from user to origin): 10
- Number of Network Hops (from user to cache server): 3
Without Cache Server
- Data Transfer Time = (Image Size / Network Speed) = (1 MB / 100 Mbps) = 80ms
- Round Trip Time (RTT) = 80ms × 2 = 160ms
- Network Hops Delay = 10 hops × 5ms = 50ms
- Total Latency = Round Trip Time + Data Transfer Time + Network Hops Delay = 160ms + 80ms + 50ms = 290ms
With Cache Server
- Data Transfer Time = (Image Size / Network Speed) = (1 MB / 100 Mbps) = 80ms
- Round Trip Time (RTT) = 15ms × 2 = 30ms
- Network Hops Delay = 3 hops × 5ms = 15ms
- Total Latency = Round Trip Time + Data Transfer Time + Network Hops Delay = 30ms + 80ms + 15ms = 125ms
Comparison Result
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