Web Content Delivery Header Modification for Load Balancing
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Solution Overview
Problem
Existing web content delivery systems face challenges in maintaining a consistent user experience during flash crowds due to unpredictable traffic surges, leading to computational inefficiencies and potential system overload, as they struggle to dynamically adjust resources to match demand.
Innovation Solution
A computer-implemented method and system that collects utilization metrics from web servers, determines device and system load levels, and modifies client requests with service level headers to dynamically adjust the delivery of web content variants, ensuring optimal resource utilization and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If web servers maintain high computational resources to handle peak traffic, then user experience during flash crowds is improved, but resource utilization efficiency deteriorates during low traffic periods
Solution Approach 1:
The system dynamically adjusts the delivery of web content variants based on real-time server load conditions. During low traffic periods, simpler content variants are delivered requiring fewer computational resources. During flash crowds, the system can deliver appropriate content variants without requiring full peak-capacity infrastructure, thus resolving the contradiction between maintaining reliability and reducing resource waste.
Solution Approach 2:
The patent changes the parameter of content delivery by providing multiple content variants with different computational requirements. The server selectively delivers different variants based on load conditions, transforming the static resource allocation problem into a dynamic parameter adjustment solution that balances reliability and resource efficiency.
2Loss of energy
If web servers reduce computational resources to improve efficiency, then resource utilization improves, but ability to handle flash crowds deteriorates
Solution Approach 1:
The system implements dynamic resource allocation where content delivery parameters are adjusted in real-time based on actual server load. This allows the system to operate efficiently during low traffic while maintaining the capability to handle flash crowds by delivering appropriate content variants, thus improving resource efficiency without permanently reducing traffic handling capacity.
Solution Approach 2:
The system prepares multiple content variants in advance with different computational requirements. When flash crowds occur, the server can quickly switch to delivering content variants that are appropriate for high-load conditions without needing to provision full peak resources continuously, thus maintaining productivity while improving overall resource efficiency.
3Adaptability or versatility
If multiple content variants are maintained for different user capabilities, then adaptability to client devices is improved, but system complexity increases
Solution Approach 1:
The patent segments content into multiple variants with different levels of richness and computational requirements. This segmentation allows the system to adapt to different client capabilities and server load conditions independently, managing complexity by organizing content into discrete, selectable units rather than handling monolithic content delivery.
Solution Approach 2:
The content variant selection mechanism serves multiple functions: it adapts to different client device capabilities, responds to server load conditions, and optimizes resource utilization. This multi-functionality reduces overall system complexity by using a single mechanism to address multiple requirements that would otherwise require separate systems.
4Loss of energy
If manual content shedding is performed to reduce load, then immediate load reduction is achieved, but response time increases and user experience deteriorates
Solution Approach 1:
The system implements automated content variant selection based on real-time load monitoring and prediction. Instead of manual content shedding, the server automatically determines which content variants to deliver based on current and projected load conditions, eliminating the time delay associated with manual intervention while achieving load reduction.
Solution Approach 2:
The system uses load monitoring and prediction mechanisms to continuously feedback server conditions to the content selection process. This closed-loop feedback enables automatic, real-time adjustment of content delivery without manual intervention, reducing response time while maintaining load management effectiveness.
Data Source
AI summary
A method and system for dynamically altering the delivery of web content to end users based on server load. Responsive to receiving a client request for web content, utilization metrics are collected from a plurality of devices that deliver the web content to the end users. Individual load levels for the devices are determined respectively, based on the utilization metrics of the devices, a combined load level is determined for two or more of the devices having the same device type based on the individual-load levels, and a service level to provide to the client is determined based on the combined load level. The request header is modified to specify a rate to deliver the web content to the client based on the service level. The request is sent with the modified header to one of the devices to serve a variant of the web content to the client at the specified rate.