See what our 2021 looked like. Numbers grow, the goal stays the same – to deliver a stellar client service to you and the best possible online experience to your users. Thank you for trusting in us. 

In the ever-evolving landscape of content delivery networks, we understand the importance of giving you, our clients, the right tools you need to be more autonomous in your operations. That’s why we’re eager to introduce a significant advancement that puts the power squarely in your hands: CDN77 Edge Computing.

Building upon standard solutions, this platform allows you to customize online interactions according to your business and technical goals, ensuring high performance and security. We aim to empower you with complete control over how our edge servers transform, cache, and handle HTTP requests.

This blog post will explore the essence of CDN77 Edge Computing and the challenges we overcame during its development. We know that for many clients this is a game changer, so we’re launching a beta access program. You can already reach out to your account manager or sales to apply for beta access. We would love to hear your feedback or feature suggestions for your specific use case. You can also chat directly with our platform architect Lukas Forst.

As we specialize in content delivery, we depend heavily on our infrastructure. We operate thousands of edge servers running across the globe. This infrastructure is supported by a 130 Tbps network with a global private backbone.

It’s no secret that our edge servers are powered by our fork of Nginx enhanced with OpenResty. This dynamic combination empowers our servers to store and deliver content efficiently. It offers advanced customization capabilities through real-time manipulation of HTTP requests and responses, ensuring optimal performance and tailored content delivery for every user.

Through our client panel or an extensive API, it’s possible to set up various features, rules and basic request manipulation. These configurations are then propagated to our edge servers and executed when needed. These configs’ technical implementation usually combines Lua scripts and Nginx configurations.

For customers who need more advanced or tailored configurations that aren’t generally available from our API, we have a Support Team that can configure almost any request or routing transformation.

While our API covers most of the common use cases of HTTP request transformation, we wanted to offer more. Our customers know what is best for them and should be allowed to manipulate their requests however they want, whenever they want, in a secure and isolated manner. Essentially, we wanted to give them full autonomy in what they can do themselves and let them see and debug routing changes instantly.

Our focus in designing the Edge Computing platform was to be able to transform requests securely and dynamically. We also needed to load new modifications and plugins dynamically on the fly without influencing or slowing down anything else running on our edge servers. Additionally, we wanted our plugins to work and seamlessly integrate with Nginx, allowing us to adjust virtually any internal behavior as needed.

Another goal was to be able to express any business logic that the client would ever need. Therefore, we want to be able to execute actual code and not be limited by basic grammar and expression. However, this decision posed a challenge: How to execute untrusted code safely without compromising the security of our servers?

Running untrusted code in a trusted environment is hard. Ensuring proper isolation and sandboxing mechanisms to prevent unauthorized access to system resources, like files, network, or customer data, demands meticulous attention to detail and can be complex to implement effectively. Moreover, we need to be running untrusted code fast.

While designing our platform, we considered using isolated processes, microVMs or even containers. We found isolated processes not safe enough for our use case. MicroVMs are secure but bring other challenges like long cold starts and slow communication between plugins and our Nginx. Running containers might help with the cold starts and communication overhead, but it's still light years behind our final choice.

The answer for our use case lies in the binary world - WebAssembly.

WebAssembly (Wasm) is an ideal choice for executing untrusted code on servers due to its sandboxed and efficient design. Wasm provides a low-level binary instruction format that can be executed at near-native speed, ensuring high performance for diverse workloads. The sandboxing mechanism of Wasm provides a secure execution environment that isolates untrusted code from the host system, mitigating potential security risks and preventing unauthorized access to resources.

Wasm’s platform-independent architecture enables code portability across different servers and platforms. Combining that with performance and security, Wasm ensures that untrusted code can be executed reliably and efficiently within a controlled sandbox virtually anywhere.

As a portable format, one only needs a Wasm runtime to execute Wasm plugins. After extensive research, we decided to use Wasmtime as our runtime. It is written in Rust (❤️), with SDK in C, battle-tested, and can be embedded in an already-running process.

One of the many metrics used to measure the performance of a CDN is latency and response time. While building our Edge Computing platform, the main objective was to keep these metrics down and avoid increasing the general infrastructure load. To do that, we decided to run Edge Computing plugins as close as possible to the infrastructure edge, and Nginx is the first place that can understand L7 data and process it. That’s one of the reasons why we decided to embed Wasmtime directly into our Nginx. Nginx Workers processes can now load and execute Wasm plugins and utilize the full power of Wasmtime while keeping the communication overhead between the plugin and the Nginx close to zero.

These are just our examples. You can run virtually any logic that you and your business need. Feel free to utilize the power of thousands of our edge servers and their proximity to the end users.

Let’s explore some examples in Rust. These are the plugins or their variants we’ve been using and deploying to production for some time.

As we journey into the future of web services and content delivery, CDN77’s new Edge Computing platform is set to evolve and expand, bringing more power and flexibility to developers and businesses alike. With a strong foundation already in place, the following features are under active development and will be available soon to the beta testers.

Because any generic computing platform needs to be able to persist data, we’re working on distributed, eventually consistent key-value storage and distributed SQLite database. Persistent distributed storage is currently our priority number one.

We currently have a feature-complete SDK for Rust, Golang, and AssemblyScript. Our JavaScript and Typescript SDKs are still in their infancy, but we plan to open and publish them shortly. Eventually, we plan to deliver even more SDKs for Wasm-enabled languages to make it easy for developers to deploy to our platform using languages they’re familiar with.

While our Edge Computing platform is designed to handle incoming requests to our CDN, we plan to expand its capabilities to enable the dispatch of HTTP requests in the future.

Given our extensive global infrastructure, we aim to provide even greater utility to our customers than is currently possible. One of the ways we're planning to do that is to provide support for server-side rendering frameworks such as Next.js, Nuxt, or SvelteKit.

We hope you share our excitement about the expansive new possibilities opened up by CDN77 Edge Computing. We've been heavily testing both internally and with selected customers for almost a year. We served billions of requests and petabytes of traffic through this platform. In fact, this very site was served to you by one of our edge computing plugins.

We'd love to hear your feedback and suggestions, so do not hesitate to reach out directly to Lukas, the platform architect. Also, stay tuned as we will follow up with a series of more technical deep dives into how we designed and built this platform in the upcoming months.

We aim to empower you with complete control over how our edge servers transform, cache, and handle HTTP requests.

Continuous improvement of product and service aspects is a standard for most tech enterprises; pricing, however, often remains overlooked. Yet this is exactly what sets market leaders apart – their ability to quickly adapt and respond to client’s changing business needs.

At CDN77, all of our clients now benefit from having overage fees set at the unit price of their CDN traffic plan. What’s more is that they enjoy the freedom to easily upgrade or downgrade their plans on a monthly basis. 

This month we've accomplished our long-term goal – a pricing reform where all of our clients have their overage (additional data that exceeds your subscribed traffic volume) fees equal to the unit price of their chosen plan.

When our clients consume more traffic than they subscribe to, we don’t see that as data excess, we would rather see that as growth that we’re here to support.

Let’s see an example. Imagine you’re subscribed to a 3,000TB Plan at $6,600 per month. This means your unit price is $2.2/TB ($6,600/3,000TB). When you transfer more than 3,000TB of traffic in a given month – let’s say 3,500TB – the additional 500TB will be charged at the same rate ($2.2/TB) as the previous 3,000TB. So, your final payment would be $7,700 ($2.2 * 3,500TB).

Over the course of the past 12 months we applied this pricing model to our Custom and High Volume plans. Preset Monthly Plans adjustment marked the end of the transition process this summer, which effectively means that the overage fees are now capped at the unit price for all of CDN77's plans.

Internet usage has never been predictable. The 48% increase in global internet traffic during the 2020 pandemic is a powerful demonstration of traffic volatility. 

At CDN77, we reckon with changes in websites’ and apps’ popularity, traffic seasonality as well as sudden peaks in visits. 

While our 70Tbps+ network absorbs any unexpected traffic surges from the capacity point of view, we make sure to reflect traffic unpredictability also in pricing.

In practice it means that our clients have the freedom to flexibly upgrade or downgrade their traffic plan on a monthly basis according to their actual needs. With no rigid commitments in place, you get the optimal pricing solution, whether you’re experiencing rapid growth or going through a lower traffic span.

For example, you might need a 800 TB/month plan for January, then you can downgrade to 550 TB/month in February and upgrade to 3,500 TB/month in March – if that's how your traffic consumption works, it’s no problem for us.

The combination of flexible plan switching and flat rates for overage traffic makes CDN77 a partner to rely on in all stages of your business.

Adjust Your Plan According to Current Traffic Needs

We’re committed to finding the best fitting solution for all of our clients by consulting their individual use-cases, on both technical and pricing levels. If you’d like to learn more about our services, reach out to our Client Solutions team at clientsolutions@cdn77.com.

With CDN77 you now benefit from flat overages and enjoy the flexibility to change your traffic plans on a monthly basis. Read for more details.

High Overage Fees & Rigid Commitments are Passé at CDN77

Top network infrastructure is a must for fast and reliable content delivery. But it doesn't work on its own. Every requested URL must be evaluated against hundreds of millions of values stored in any chosen data center. And it has to happen fast. We're talking milliseconds. That's when hashing comes into play.

In recent years, the word "hashing" has become well known even among non-tech people mostly thanks to the growing crypto market. Generally speaking, we use a hash function (#) to map data of arbitrary length to a range defined by a hash function. Bitcoin, for example, checks whether the final hash is less than the difficulty level, making it valid and eligible to add to the chain. [1]

In terms of content delivery, hashing is crucial for increasing the efficiency of various parts of the delivery process. In the example below, we will quickly discover how calculating hashes speeds it up.

Imagine you are given the task of building a simple CDN. For simplification,  you can ignore all metadata, such as headers, and you are only required to check whether any of the servers have the desired URL in cache or not.

The most naive and obviously wrong approach would be using string matching. You store cached URLs in an array of strings, check for equality, and do that on every server. The complexity of this approach is m x n, where m is the length of string and n is a number of cached URLs on all your servers).

Simple math: This solution quickly becomes infeasible, even with relatively small numbers. The URL to the background image on our homepage https://www.cdn77.com/_next/image?url=%2Fimage%2Fpage%2Fhome%2Fglobe.png&w=3840&q=75 is 84 characters long (m). We need to fetch it from a DC with 100 servers, with each having 10 million files in a cache (n). 84 x 100 x 10 000 000 = 84 000 000 000.

This means that a single 1GHz processor would take 89 seconds(!) just to find the file in cache. Even if you do it concurrently on all 100 servers,  it would take almost a second to serve a single file. With millions of requests every second, it's evident that just throwing more computing power at the problem won't do it. It will waste a lot of computing power only to come up with a lot of "no" answers and maybe one "yes".

Hashing is a way you want to take. Keep in mind that all the examples that I'm about to provide still give you an extremely simplified description of reality, but enough to give you an idea of what happens when a user's request is delivered to one of our PoPs.

We can upgrade the above approach by storing a hash of the URL instead of its string value. Now, when the server is queried for URL, it will first hash it to an integer and use it as a key. We can then easily search for such a key with logarithmic complexity at worst using a suitable data structure (e.g., AVL or any other balanced tree). That's a significant improvement over the first solution. Instead of m x n, we reduced search time to log(n). But still, doing it on all the servers would be too costly.

Every PoP consists of hundreds of servers, and we can't query them all for the same thing. We need to pick the server before asking it whether it has the URL in a cache.

The most straightforward solution is to number your servers and map those numbers to their IP address. When a request reaches the PoP, you will first hash the URL to an integer and then modulo it by the number of servers (k). The result of key mod k will always be the correct server to ask if it has the requested URL in a cache. This is a well-known load balancing technique.

If the number of servers stayed the same, we would be done here. But that is rarely the case. In reality, we often want to scale up and add some servers to the pool or take some down for maintenance. Every time the number of servers changed, every key mod j (new no. of servers) would point to a different server, and the entire cache would effectively be lost. A minor modification will solve that.

Consistent hashing directly addresses the problem of changing the number of servers. Instead of assigning one number to the server, we map them to several (hundreds realistically) unsigned integers, evenly distributed in the unsigned int range (2^32). Conceptually, you can imagine a clock face on which each server appears several times.

An incoming request for a URL is hashed to a single integer. The next biggest number on the clock is the correct server, which should hold the requested file. If the value of the hash is greater than the biggest number assigned to any server, we choose the first one.

Combining this approach with an appropriate hash function guarantees that only a tiny portion of keys end up pointing to a different server when we add or remove one.

Consistent hashing also boosts the performance of a multi-layered setup we have described here (although maybe not so much boosts as makes it possible).

When the request needs to be forwarded to the next layer, we instantly know not only where the next layer is located, but we also know which server exactly should have the requested file in cache. The mechanism is identical to choosing a server inside a single data center.

Trie, or prefix tree, fits the case of a CDN almost perfectly. Lookup complexity is linear with a length of string, the same as calculating the hash. Unfortunately, they won't help with the second function of hashing, which is choosing the correct server. 

That being said, we do use prefix trees and numerous other structures for more complex operations than simple lookups, such as prefix purge.

Even tiny optimizations become noticeable during traffic peaks. Search must be nearly instantaneous, at the rigth location and on the correct server. Sufficient hashing power is helping us do just that.

Milliseconds matter to us, and we are always looking for new ways to improve and speed up one thing or another. Visit our website to learn more about our network and other CDN features.

[1] https://blockgeeks.com/guides/what-is-hashing/#The_mining_process 

When serving content via CDN, every requested URL must be evaluated against hundreds of millions of values stored in any chosen data center. And it has to happen fast. Learn about how hashing fits into the picture.

When serving content via CDN, every requested URL must be evaluated against hundreds of millions of values. And it has to happen fast. Learn about how hashing fits into the picture.