Content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Prefetching is well investigated in operating systems, compiler instructions, in-memory cache, local storage systems, high-speed networks, and cloud systems. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of reinforcement learning to tackle the changes in user access patterns and automatically adapt over time. To this end, we propose, DeePref, a Deep Reinforcement Learning agent for online video content prefetching in Content Delivery Networks. DeePref is a prefetcher implemented on edge networks and is agnostic to hardware design, operating systems, and applications. Our results show that DeePref DRQN, using a real-world dataset, achieves a 17% increase in prefetching accuracy and a 28% increase in prefetching coverage on average compared to baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. In terms of transfer learning, the increase in prefetching accuracy and prefetching coverage are [$30%$, $10%$], respectively. Our source code will be available on Github.

Content delivery networks (CDNs) are the backbone of the Internet and are key in delivering high quality video on demand (VoD), web content and file services to billions of users. CDNs usually consist of hierarchically organized content servers positioned as close to the customers as possible. CDN operators face a significant challenge when analyzing billions of web server and proxy logs generated by their systems. The main objective of this study was to analyze the applicability of various clustering methods in CDN error log analysis. We worked with real-life CDN proxy logs, identified key features included in the logs (e.g., content type, HTTP status code, time-of-day, host) and clustered the log lines corresponding to different host types offering live TV, video on demand, file caching and web content. Our experiments were run on a dataset consisting of proxy logs collected over a 7-day period from a single, physical CDN server running multiple types of services (VoD, live TV, file). The dataset consisted of 2.2 billion log lines. Our analysis showed that CDN error clustering is a viable approach towards identifying recurring errors and improving overall quality of service.

In the coming years, there are few parts of daily life that artificial intelligence won't touch in one way or another, and the automotive sector is no exception. When it comes to vehicles, however, implementing AI will place enormous demands on data centre technology. Christian Ott, director of Solution Engineering at NetApp, explores how storage and compute can keep pace with the challenge. Usually, when you mention the idea of applying AI in the automotive sector to someone, their first thought is self-driving cars. In the current wave of research and development in autonomous vehicles (AVs), it's estimated that the leading thirty companies have invested $16 billion into building a car that can take itself from A to B, and, as we all know, that technology hasn't arrived just yet.

Picture a world where machines (advanced analytics systems) predict bottlenecks for humans and route traffic to its most appropriate CDN and optimal proxy. This is not far from being true. Let's take an example of a self-driving car. With the help of algorithms and sensors, a self driving car lowers the rate of accidents and gives us more time for ourselves and enables us to just relax during the ride. What happens when thousands of people have self driving cars?

Using technologies such as Machine Learning (ML), content can be made available to meet individualized needs. The cloud is emerging as a way to provide services such as storage and AI that can make content, including valuable video content, available for repurposing and monetization. Google's ML tools include TensorFlow and its Cloud Machine Learning Engine. In addition to the Google keynote there was an entire session at CS 2017 looking at the role of AI in creating metadata for video content.