Project Challenge
Objective
Building an effective predictive CDN system presents unique challenges in distributed computing — balancing cache efficiency with network throughput while minimizing latency across geographically dispersed edge nodes.
I developed this system using advanced machine learning models and distributed systems architecture, implementing both content popularity prediction and geographic request pattern analysis. The network intelligently anticipates content demand patterns, strategically pre-positioning assets across edge locations while dynamically adjusting cache priorities based on real-time metrics.
The CDN supports diverse content types from video streaming to interactive applications, adapts to changing network conditions and traffic patterns with autonomous rebalancing, and achieves sub-20ms global response times. Sophisticated failover mechanisms ensure 99.99% availability while custom compression algorithms reduce bandwidth requirements by up to 40%.
The Research
Predictive Algorithms
Our research began with comparative analysis of traditional time-based caching against probabilistic models. We implemented a baseline using LRU (Least Recently Used) and LFU (Least Frequently Used) algorithms before developing more sophisticated predictive approaches.
Through extensive experimentation, we discovered that Markov chain models analyzing content request patterns significantly outperformed static caching policies. Our implementation uses higher-order Markov models to capture temporal dependencies between content requests, allowing the system to accurately predict which content will be requested next based on observed request sequences.
The Markov chain implementation analyzes transition probabilities between content states, creating a probabilistic graph of likely future requests. We enhanced this with context-aware features that incorporate time-of-day patterns, geographic clustering, and content metadata to further refine predictions.
Our evaluation metrics include cache hit ratios, Time-to-First-Byte (TTFB), and bandwidth savings. The Markov-based approach achieved a 54% improvement in cache hit rates over traditional CDNs, resulting in average latency reductions of 78% for dynamic content and substantial bandwidth savings across our global edge network.
Technical Implementation
The system uses a distributed architecture where:- Edge nodes continuously train local Markov models based on regional traffic patterns
- A central orchestrator aggregates and refines these models for global optimization
- Dynamic rebalancing algorithms shift content based on real-time prediction confidence scores
- Efficient matrix operations enable sub-millisecond prediction time even under heavy load