Multi-Channel Resource Control via Usage Prediction
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Solution Overview
Problem
Resource management in content delivery systems is challenging due to fluctuating online traffic in some channels, making it difficult to effectively allocate resources and prevent exceeding resource limits, especially when prioritizing higher priority channels.
Innovation Solution
A prediction model is used to calculate resource usage based on historical data, day of the week, and attributes of content delivery campaigns, allowing for dynamic allocation of resources across channels to ensure fair distribution and prevent exceeding resource limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated to content delivery channels with fluctuating traffic, then resource utilization efficiency is improved, but resource limit control becomes difficult to implement
Solution Approach 1:
The system performs preliminary actions by calculating predictions of resource usage for higher priority channels before allocating resources to lower priority channels. This advance calculation allows the system to reserve appropriate resources and prevent exceeding resource limits, resolving the contradiction between efficient resource utilization and reliable resource limit control.
2Productivity
If resources are dynamically allocated based on traffic fluctuations, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system changes parameters by using prediction models that consider multiple factors (prior usage, day of week, campaign attributes) to dynamically adjust resource allocation. This parameter-based approach enables efficient adaptive resource allocation while maintaining manageable system complexity through structured prediction calculations.
3Reliability
If resource allocation prioritizes higher priority channels, then channel priority fulfillment is improved, but lower priority channel resource availability decreases
Solution Approach 1:
The system implements feedback by calculating predictions of resource usage for higher priority channels and using this information to determine appropriate resource allocation to lower priority channels. This feedback mechanism ensures that higher priority channels receive necessary resources while lower priority channels are allocated remaining resources, maintaining both priority fulfillment and reasonable resource availability.
Data Source
AI summary
A system for conserving resources in a multi-channel control system environment is provided. A resource limit that is associated with a content delivery campaign is determined. A prediction of resource usage that is associated with the content delivery campaign and a first content delivery channel is determined. A content request is received through a second content delivery channel that is different than the first content delivery channel. In response, based on data contained within the content request, the content delivery campaign is identified. Also, based on the resource limit and the prediction of resource usage associated with the content delivery campaign and the first content delivery channel, it is determined whether to respond to the content request with data associated with the content delivery campaign.