Jun 16, 2021. By S V Aditya
While the focus on edge computing has grown in the last five years due to the desire to be able to deliver more demanding services faster, it has a long history of usage. Content Delivery Networks are one of the earliest and most widespread applications of edge computing. CDN providers have been able to simultaneously increase the quality of content delivered to the user while also improving user experience with faster loading times. Due to these advantages enterprises rely on CDNs to deliver their content - from simple browser pages to high-quality videos to users. And their usage is essential. In ecommerce, for example 70% of consumers report that website load speed affects their willingness to buy. A 100 millisecond delay can damage conversion rates by 7% (Akamai Online Retail Performance Report). Faster delivery is paramount in all industries. Financial brokers, streaming platforms, and search engines all lose revenues by being a tenth of a second behind their competitors. In fact, for any website, the bounce rate rises by 30 percentage points if the load time increases from 1 second to 3 seconds. As websites are the face of a company and the key to modern business - slower load times simply means lost revenue. In other words, every enterprise chooses CDNs based on their performance above all other considerations. So CDNs have to maintain their infrastructure to the top level of performance.
However, there are a lot of infrastructural challenges in planning and maintaining an optimal Content Delivery Network. A CDN is built with Points of Presence(POP) Data Centers across the world. Each of these POPs can have hundreds of caching servers. All of these servers have to be managed for peak performance. The scale of management keeps growing as CDNs add more servers to existing POPs or develop new POPs in distant locations. Some of the larger CDNs manage close to a 100,000 servers. These servers are divided into delivery nodes (with caching and associated delivery applications), storage nodes, origin nodes, etc. Managing all these hardware and software components in the infrastructure level is extremely challenging. Any error in a component has to be fixed fast or it cascades into higher loading times. In addition, content routing has to be optimized and sent to the right nodes balancing loads against costs and performance. This requires identifying network traffic patterns and predicting future traffic demand. The security of the cached content itself has to be managed to ensure that there is no theft of intellectual property. They need to plan out their choice based on the geographical distribution of load now and for months, perhaps years into the future.
AIOps provides the opportunity for CDN infrastructure providers to find new solutions to manage their infrastructure. We have previously talked about the four elements of AIOps. Let's see how these elements fit into CDN management. It assists in the planning (Govern)stage by predicting the relevant KPIs like demand, peak-to-average ratios, etc across geographies, giving the foresight needed to expand access to infrastructure as needed. In its Observe element, AIOps helps monitor the entire distributed CDN infrastructure located across different geographies. A single platform can observe all software and underlying infrastructure components providing a truly single pane of glass view. This means that an ITOps team situated in one location can observe POPs across the globe. Moreover, AIOps technologies like Anomaly Detection can create multivariate analysis combining metrics of software and these hardware components to create accurate meaningful alerts. It can, for example, detect abnormal behaviour in an OS-level component or application that could affect routing and alert operations teams. Incident Recognition, on the other hand, can correlate KPIs, logs, and traces to find root cause issues - whether it is bug in routing application or issues with hardware.
Finally, there's the Act element. This holds the greatest potential for value. Act allows enterprises to use AI-powered models to automate management activities for the entire CDN Infrastructure. Deep reinforcement learning models can power automated workflows that remediate incidents in the underlying infrastructure to ensure it is always running smoothly. Automation can also be used for intelligent network traffic redirection. AI can identify bottlenecks with anomaly detection and use automation workflows to reassign traffic to an optimal edge server. The Act element of AIOps drives automated workflows that are driven by AI models to control all these aspects of CDN Infrastructure management.
AIOps holds great potential in cost savings and automation in handling edge computing cases like Content Delivery Network Infrastructure management. It allows providers to manage their entire CDN Infrastructure from a single point by automating the complexity of tasks. Moreover, it helps CDN providers improve their resilience to outages and helps them deploy content better. To learn more about the potential and the applications of AIOps, please visit Algomox AIOps Platform Page.