b10f4704-c6d8-4fb7-b753-6888d579fc7a.pptx

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1. Boosting CDNs with Data Science Explore how data science revolutionises Content Delivery Networks (CDNs) for seamless, high-definition content delivery, overcoming traditional limitations with intelligent algorithms. Name : Sakshi Mishra Enrolment No: A7605223145 Course : B.Tech CS&E

2. The Evolution of Digital Consumption Our digital consumption habits have transformed dramatically. On-demand streaming services like Netflix and YouTube have made traditional broadcast nearly obsolete. We expect instant access, high quality, and zero lag, regardless of location or network conditions. Seamless Access Users expect instant loading and uninterrupted streaming for all content. High-Definition Demand for 4K and higher resolutions constantly pushes network boundaries. Global Reach Content must be delivered consistently across diverse geolocations.

3. Challenges in Traditional CDNs Traditional CDNs, built on fixed rules and static content assumptions, struggle with dynamic, high-demand streaming. They are not designed for real-time adjustment, leading to inefficiencies and poor user experience during traffic surges.

4. Objectives of Data-Driven CDN Enhancement This study investigates how data science can significantly boost CDN efficiency for streaming platforms. By integrating machine learning and predictive analytics, CDNs can become proactive rather than reactive. Key Objectives Reduce video start times and buffering events. Optimise content storage for faster access. Improve load balancing to prevent server overload. Enhance overall User Quality of Experience (QoE).

5. Methodology: Tools and Techniques Our methodology combines robust programming, stream processing, and advanced data analytics to simulate and optimise CDN operations. This allows for predictive caching, intelligent load balancing, and real-time QoE monitoring. Python Ecosystem Utilised Pandas, NumPy for data manipulation, and Scikit-learn for predictive modelling. Stream Processing Apache Kafka emulated real-time traffic and system events for a realistic simulation. Distributed Analytics Apache Spark handled large-scale log processing and sophisticated batch analytics. Visualisation Matplotlib and Seaborn created compelling visual narratives of performance improvements.

6. Predictive Caching & Load Balancing Our approach focuses on proactively caching popular content closer to users and dynamically balancing server loads using data-driven insights. This contrasts sharply with traditional static methods. Predictive Caching Strategy Decision trees categorise content based on anticipated demand windows. LSTM neural networks investigate time-series sequences for improved forecasting. ARIMA and Linear Regression models dissect hourly traffic patterns. Load Balancing Enhancement Compares traditional Round Robin/Least Connections with data-driven methods. Continuous analysis of server health and predicted traffic for dynamic distribution. Decision Tree models make intelligent routing decisions based on real-time data.

7. Quality of Experience (QoE) Monitoring Measuring user satisfaction is paramount. Our framework tracks key QoE metrics to validate the impact of our data-driven enhancements, ensuring a superior streaming experience. 8.5 Video Start Time Time from click to first frame. 9.0 Buffering Rate Frequency of mid-playback pauses. 8.8 Streaming Quality Consistency of high-definition content delivery. Synthetic feedback loops and simulated user surveys provided continuous insights, allowing real-time adjustments to maintain high user satisfaction.

8. Key Observational Results The data-driven CDN approach significantly outperforms traditional methods, demonstrating notable improvements in cache efficiency, latency, and user satisfaction. Traditional CDN Data Science CDN The data science-enhanced CDN achieved an impressive 20% increase in cache hit ratio and a significant reduction in latency, ensuring faster and more reliable content delivery.

9. Conclusion and Future Outlook This study demonstrates how integrating data science into CDN operations dramatically enhances performance, leading to better user experience and operational efficiency. The future of content delivery lies in adaptive, intelligent systems. Future research will explore real-time learning at the edge, decentralised decision-making, and the ethical implications of AI in content distribution, ensuring CDNs remain at the forefront of digital innovation.