Traffic Spike Prediction Service for Content Delivery Networks
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Solution Overview
Problem
Existing technologies face challenges in effectively detecting and managing legitimate traffic spikes in content delivery networks, often leading to disruptions due to the inability to distinguish between legitimate and malicious traffic, resulting in potential service unavailability during spikes.
Innovation Solution
A traffic spike prediction service is implemented to analyze historical network traffic data to identify 'traffic-spike referrers' and predict subsequent spikes, allowing for proactive allocation of additional computing resources and notification to network-attack mitigation systems to manage legitimate traffic spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques focus on identifying and isolating malicious traffic patterns, then network attack mitigation is improved, but legitimate traffic spikes are incorrectly blocked causing service unavailability
Solution Approach 1:
The system performs preliminary actions by proactively predicting traffic spikes before they occur. It analyzes historical traffic data to identify traffic-spike referrers and predicts future spikes, allowing the system to prepare and differentiate legitimate traffic patterns before the actual spike happens, thus avoiding incorrect blocking of legitimate traffic
Solution Approach 2:
The system segments traffic analysis by identifying specific referrer sources that historically cause traffic spikes. Instead of treating all traffic uniformly, it segments traffic based on referrer patterns, allowing differentiated handling of legitimate referral traffic versus malicious traffic patterns
2Reliability
If the system responds to traffic spikes after they occur, then resource allocation can be adjusted, but service disruption has already happened reducing user experience
Solution Approach 1:
The system performs preliminary actions by predicting traffic spikes before they occur. By analyzing historical traffic data and identifying traffic-spike referrers, the system can proactively allocate additional computing resources in advance, ensuring service continuity and avoiding user-facing disruptions when the actual spike occurs
3Productivity
If computing resources are increased manually during traffic spikes, then service capacity is improved, but the rapid nature of spikes provides little opportunity for manual intervention
Solution Approach 1:
The system implements self-service by automatically predicting traffic spikes and triggering resource allocation without manual intervention. The predictive analytics system monitors traffic patterns, identifies upcoming spikes, and automatically initiates resource provisioning, enabling the system to self-manage capacity adjustments in real-time based on predicted demand
Solution Approach 2:
The system uses feedback mechanisms by continuously analyzing historical traffic data and performance metrics to refine its predictions. The system learns from past traffic patterns and adjusts its predictive model, creating a closed-loop system that improves its accuracy over time and enables more effective automatic resource allocation
Data Source
AI summary
Systems and methods are described to predict spikes in requests for content on a computing network based on referrer field values of prior requests associated with spikes. Specifically, a traffic spike prediction service is disclosed that can analyze information regarding past requests on the computing network to detect a spike in requests to a content item, where a significant number of request within the spike include a common referrer field value. The traffic spike prediction service can then detect a request to a second content also including the common referrer field value, and predict that a spike is expected to occur with respect to the second content. The traffic spike prediction service can manage the expected spike by increasing an amount of computing resources available to service requests to the second content.