Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.

Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting

Although the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by—the still exposed—IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain–IP mappings unstable, and prevent straightforward IP-based browsing tracking. In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website finger-printing technique that allows a network-level observer to identify at scale the website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popular and sensitive web-sites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.

Evaluating and Improving a Content Delivery Network (CDN) Workflow using Stochastic Modelling

Content Delivery Networks (CDN) are the backbone of Internet. A lot of research has been done to make CDNs more reliable. Despite that, the world has suffered from CDN inefficiencies quite a few times, not just due to external hacking attempts but due to internal failures as well. In this research work the authors have analyzed the performance of a content delivery network through various reliability measures. Considering a basic CDN workflow they have calculated the reliability and availability of the proposed multi-state system using Markov process and Laplace transformation. Software/Hardware failures in any network component can affect the reliability of the whole system. Therefore, the authors have analyzed the obtained results to find major causes of failures in the system, which when avoided, can lead to a faster and more efficient distribution network.

Binary Integer Programming Approach to Optimal Content Placement in Cloud-based Content Delivery Networks

Over the last decades, Content Delivery Networks (CDNs) have been developed to overcome the limitation of user perceived latency by replicating contents from origin server to its content servers around the globe close to clients. As some contents occupy most of the storage capacity and processing power in traditional private content servers, cloud computing can provide a pool of storage and processing power resources for caching contents. By adopting cloud computing to CDN, the content provider can use the cloud infrastructure by distributing the contents to cloud servers which will then deliver to near clients. In this paper, we propose a cloud-based CDN framework designed by two schemes 1) UDP/TCP-based content distribution from origin server to cloud servers and 2) SDN-based cloud server coordination. In addition, we also formulate the optimal content placement problem using binary integer programming to minimize the total cost of renting resources including storage, processing power, and network bandwidth in cloud providers for hosting contents from origin server. Then, the optimal solution obtained from binary integer programming is evaluated by greedy algorithm and simulations. The proposed framework helps content provider to offer high quality of services to clients while minimizing the cost of rented cloud resources.