Commercial content delivery networks (CDNs) are the bulwark of modern internet communications. But since their introduction in the 1990s, a lot of technology has come down the product development road, technology that can optimize network performance or enter it maliciously to slow it down or stop it altogether.
Now as global internet traffic and cyberattack sophistication grow, machine learning contributes the built-in benefits of high-speed, automated data monitoring and management, It’s just the ticket for security teams wanting to optimize CDN server performance.
Putting Machine Learning to Work in Your CDN
As huge volumes of content are routinely processed with every query, intelligence becomes an essential part of managing data and optimizing networks. Something faster than human capabilities are needed to identify, prioritize, and respond to service requests in internet traffic. Machine learning provides automated responses that provide faster, more reliable network performance that users appreciate.
CDNs cache data across a network of data centers strategically placed around the world. But which edge server will the CDN send the request to? CDNs make this decision with the help of a set of algorithms designed to make routing and data intelligence functions smart and efficient.
Machine learning capabilities that support optimization
Optimal performance—delivering content in the fastest, most reliable, and most secure way possible—defines the purpose of CDNs. So, which technologies and capabilities help to optimize CDN function?
In one approach to CDN optimization, security team members gather existing performance data. Then, they use machine learning algorithms to provide real-time insights into network behavior. Machine learning supports important CDN capabilities such as:
- Intelligent web caching. Machine learning supports this important capability by automatically identifying new caching opportunities for static and dynamic content (the latter are objects created on the fly). This ability, which flags and prioritizes pages for updates, keeps content fresh and cuts the time, effort, and costs needed to keep content accurate and up to date.
- Load balancing. These services monitor the “front gate” of CDN servers at points of presence (PoPs). Local and global load balancing algorithms measure and reroute traffic across CDN servers to prevent data slowdowns or outright stoppages. The result: maximum application availability and lower server load. Machine learning uses real-time pattern recognition and automated response algorithms to find, identify, and react to possible malicious threats or accidental network failures.
- Security services. CDNs that provide security services often include PoPs with DDoS scrubbing servers and machines that provide other security functions. Machine learning algorithms can recognize and respond to odd network behavior and data patterns gathered and analyzed from earlier incidents. If the software responds more quickly than malicious actors can find a vulnerability, the security team avoids a possibly costly slowdown or shutdown of network activity.
In this way, CDNs bolstered with machine learning speed and analytical prowess can help organizations stop DDoS attacks as well as block scrapers, spammers, and other malicious software.
Building or Upgrading a CDN? Here’s What to Look For
Machine learning presents many ways to optimize CDN security, cost savings, and efficiency. To achieve these benefits, however, CDN operators must build virtual infrastructures embedded with next-generation machine learning solutions.
What’s the goal?
Adding machine learning capabilities to CDNs requires training a model with lots of data and using it to make effective real-time decisions. This process is designed to:
- Minimize latency by predicting network bottlenecks and proactively re-routing traffic to the optimal cache server.
- Manage an increasingly complex network, effectively and dynamically,
- Use intelligent management solutions that deliver a positive experience to content consumers.
- Use data-driven learning and predictive analytics to inform decisions.
In other words, CDNs that use machine learning are set to deliver better network performance at an affordable cost.
Must-have CDN features and capabilities
But what should you look for when you install or upgrade CDNs? Here’s a list of features, capabilities, and tactics, which help deliver on the promise of better CDN performance at lower cost:
- Intelligent caching servers, which reduce latency and bandwidth consumption (which reduces operating costs).
- Network load balancing capabilities, which help network admins avoid traffic slowdowns by monitoring traffic flow and transferring it to other CDN servers.
- Machine learning-based malware and attack defenses, which use automated monitoring and defense tactics to identify and protect against scrapers, spammers, and other malicious software.
- DDoS attack protection, which is based on CDN servers positioned at your network’s edge. When combined with automatic threat identification and mitigation via machine learning, CDNs can go toe to toe with modern attack threats.
- Machine learning-based data management, which includes high-speed, automated monitoring, control, and optimization functions. When paired with data analytics software, machine learning provides a highly effective way to monitor and route data, securely and efficiently.
Especially when used in CDN environments, machine learning supports many forms of optimization, making data management, communications, and protection services fast, efficient, and safe.
