Distributed Computing System for Video Ad Delivery
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Solution Overview
Problem
Existing video advertisement delivery systems face challenges in processing large volumes of data efficiently, particularly in real-time, and struggle to scale resources effectively to meet demand, leading to suboptimal response times and billing inaccuracies due to the limitations of traditional infrastructure.
Innovation Solution
A distributed computing system is employed to process data from geographically dispersed advertisement servers, utilizing cloud resources to analyze operational parameters such as geographic data, consumer profiles, and unique impressions, enabling scalable and efficient video advertisement delivery with improved response times and billing precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional infrastructure is used for video advertisement delivery, then system simplicity is maintained, but processing speed and scalability deteriorate
Solution Approach 1:
The system segments the video advertisement delivery infrastructure into multiple distributed computing nodes that operate independently but coordinate through standardized protocols. This allows parallel processing of advertisement data while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from traditional single-dimension sequential processing to multi-dimensional parallel processing by distributing computation across multiple nodes in the cloud. This adds spatial and temporal dimensions to processing, enabling simultaneous handling of multiple advertisement streams.
2Productivity
If cloud computing resources are scaled up to handle large data volumes, then processing capability improves, but resource management complexity increases
Solution Approach 1:
The system implements universal resource management protocols that allow the same infrastructure to handle multiple types of advertisement data and processing tasks. This multi-functionality reduces the need for specialized management mechanisms for different resource types.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor resource utilization and dynamically adjust allocation. This closed-loop control automates resource management, reducing complexity by eliminating manual intervention while optimizing processing capability.
3Loss of time
If real-time processing is implemented to improve response times, then customer satisfaction improves, but system resource requirements increase
Solution Approach 1:
The system implements dynamic resource allocation that adjusts computational power in real-time based on actual processing needs. During low-demand periods, resources are scaled down; during peak periods, resources are automatically expanded, optimizing the balance between response time and resource consumption.
Solution Approach 2:
The patent employs preliminary caching and pre-processing of advertisement data to reduce real-time processing requirements. By preparing data in advance and storing it in optimized formats, the system achieves fast response times without requiring excessive computational resources during actual delivery.
Data Source
AI summary
A distributed computing system is configured to compute operational data for a video advertisement delivery system. Cloud-based resource are used to calculate operational parameters such as geographical data, unique advertisement delivery instances and segments of consumers that received the video advertisements.