The high-defense CDN intelligent scheduling system is a core component of modern content delivery networks. Leveraging integrated distributed nodes, real-time monitoring, machine learning algorithms, and dynamic routing strategies, it achieves efficient global traffic distribution and secure protection. This high-defense CDN system helps improve user access speeds and defend against various network attacks, ensuring business continuity and stability.
The foundation of the intelligent scheduling system is a globally distributed network of nodes. These nodes are typically deployed close to users and feature BGP Anycast multi-line access, automatically selecting the optimal path based on user location and network conditions. For example, some leading high-defense CDN service providers have deployed over 200 acceleration nodes in major regions such as Asia, North America, Europe, and the Middle East. Using Anycast technology, the same IP address can be announced by multiple nodes. Using the BGP routing protocol, user requests are automatically routed to the physically closest node, significantly reducing access latency. This network architecture not only improves access speeds but also provides the infrastructure support for subsequent intelligent scheduling.
Real-time data collection and monitoring form the perception layer of the intelligent scheduling system. The system uses probes deployed at edge nodes and a distributed monitoring platform to continuously collect multi-dimensional data from the network layer (bandwidth, latency, packet loss rate), the node layer (CPU utilization, memory usage, cache hit rate), and the user layer (geographic location, access time, and terminal type). This data is updated at a frequency of seconds or even milliseconds, providing a real-time, accurate foundation for scheduling decisions. A health check mechanism uses Ping, TCP, and HTTP(S) to probe nodes and origin servers at a frequency of up to once a minute, ensuring rapid detection of faults or anomalies.
The core of the intelligent scheduling system lies in its decision-making engine, which utilizes machine learning and artificial intelligence technologies to analyze and predict collected data. The scheduling engine integrates multiple factors for comprehensive decision-making, including user location and carrier affiliation, real-time network quality, node service capabilities and status, service type and service level, and cost factors. Machine learning models, such as a hybrid model combining deep learning (LSTM+Transformer) and reinforcement learning (DQN), can analyze historical data and real-time traffic patterns to predict the packet loss rate of candidate paths in the short term, with an error rate of less than 12%. This enables the system to implement dynamic weighted calculations based on models such as weighted scoring, decision trees, or reinforcement learning, generating the optimal target node mapping for each user request in real time.
Dynamic traffic scheduling and path optimization are the execution layer of intelligent scheduling. The system supports multiple scheduling methods, including DNS scheduling, HTTP 302/307 redirects, and Anycast routing. The intelligent scheduling system adopts a strategy that combines global scheduling with local regulation. Global scheduling is performed by the decision engine layer based on the status of nodes across the entire network. Local regulation is implemented autonomously by the edge node cluster. Through real-time communication between nodes, nodes obtain load information from neighboring nodes. When the local load exceeds a threshold, some requests are proactively forwarded to less loaded neighboring nodes. This mechanism avoids scheduling bottlenecks at central nodes and improves system scalability and reliability. For high-value traffic flows, the system can also utilize multi-path transmission redundancy, transmitting data simultaneously through two or three alternative paths. Packet reordering algorithms at the application layer restore complete data, reducing the effective packet loss rate to below 1%.
Security protection and attack mitigation are key functions of the high-defense CDN intelligent scheduling system. The system uses a real-time behavior recognition engine to dynamically model request behavior, analyzing metrics such as access paths, parameter change frequency, request header behavior, and IP address change frequency. Combined with machine learning algorithms, it trains "anomaly profiles" to identify potential attack traffic. When a DDoS attack is detected, the intelligent scheduling system redirects attack traffic to dedicated scrubbing centers. These scrubbing centers are located globally, with a combined scrubbing bandwidth exceeding 2.4Tbps, enabling rapid interception and processing of abnormal traffic in the event of a large-scale attack. Legitimate traffic after scrubbing is forwarded back to the origin server to ensure unimpeded services. The intelligent scheduling system also supports integration with a Web Application Firewall (WAF), providing comprehensive protection from L3 to L7 layers.
Cache optimization and content distribution efficiency are also key areas of focus for the intelligent scheduling system. The intelligent caching system dynamically adjusts caching strategies by analyzing content popularity, user access patterns, and historical data. The system utilizes a tiered cache architecture, dividing the cache into hot, warm, and cold tiers based on content access frequency. These tiers utilize high-speed storage media (such as SSDs) and high-capacity HDDs, respectively, to balance storage costs and access efficiency. Intelligent prefetching and prewarming mechanisms use machine learning to predict the distribution of popular content and pre-cache frequently accessed resources to nodes near the user, reducing cross-domain back-to-origin requests and improving cache hit rates. Some advanced systems also support sharded caching technology, which breaks large files into shards and caches only the most frequently accessed portions, further improving storage efficiency and node hit rates.
Disaster recovery and fault recovery are key to ensuring service continuity. The intelligent scheduling system uses real-time monitoring and health checks to quickly locate node or link failures and automatically trigger failover mechanisms. When a node fails or a link is congested, BGP automatically converges routes and switches traffic to a neighboring node, with failover time kept within 500 milliseconds. The system also supports multi-level backup and intelligent mutual redundancy between CDN vendors. Using the cross-domain Smart Routing scheduling mechanism, service scheduling is achieved across multiple CDNs, further improving service reliability and enabling CDN complementarity.
Performance optimization of the intelligent scheduling system is an ongoing process. It relies on a full-link performance monitoring system that covers multiple metrics, including user experience, CDN node status, and back-to-origin performance. Through A/B testing and phased rollouts, the system verifies the effectiveness of new strategies, ensuring that changes deliver positive benefits without negative impacts before fully launching. This data-driven continuous tuning enables the system to consistently maintain optimal or suboptimal states in dynamic network environments.
