AI Call Prediction Cache for Peak Load and Idle Compute Balance
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Solution Overview
Problem
AI computing services face inefficiencies due to high computational costs during peak demand and idle times, leading to strain and long wait times, as they struggle to optimize processing power and scale with varying request volumes.
Innovation Solution
Implementing a cache system that predicts and pre-computes cacheable requests when processor usage is below a threshold, storing outputs in a data repository, and utilizing a messaging system to manage and distribute these requests efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processing power is increased to support high demand at peak times, then the system can handle more requests, but it results in increased idle processing power during off-peak times
Solution Approach 1:
The system performs preliminary actions by predicting future cacheable requests during off-peak times and pre-computing their results before they are actually needed. This allows the AI computer to utilize idle processing power productively and avoid peak-time strain, resolving the contradiction between handling high demand and avoiding idle waste.
2Measurement precision
If the AI computer processes all requests in real-time, then accuracy is maintained, but wait times increase during peak demand
Solution Approach 1:
The system predicts future cacheable requests and pre-computes their results in advance during periods of low demand. When these predicted requests are actually received by users, the pre-computed results can be returned immediately, maintaining accuracy while dramatically reducing wait times.
Solution Approach 2:
The system dynamically adjusts its processing strategy by identifying cacheable requests that can be pre-computed versus those requiring real-time processing. This dynamic approach allows the system to optimize between accuracy and response time based on request characteristics and system load conditions.
3Productivity
If the system pre-computes cacheable requests during off-peak times, then peak-time strain is reduced, but system complexity increases
Solution Approach 1:
The system introduces a call prediction processing computer as an intermediary component that coordinates between the AI computer and the orchestration service. This intermediary manages the prediction and pre-computation process, handling the complexity of identifying cacheable requests and scheduling pre-computation tasks, thereby reducing peak-time strain while containing system complexity.
4Loss of time
If a cache system is implemented to store pre-computed results, then response time is reduced, but data storage requirements increase
Solution Approach 1:
The system applies local quality by selectively caching only cacheable requests and their results, rather than storing all possible data. The cache stores pre-computed results for specific predicted requests that are likely to be requested, optimizing the balance between response time improvement and storage resource utilization.
Data Source
AI summary
Methods and systems of predicting and processing cacheable AI calls are disclosed. One method includes determining, by a call prediction processing computer, that an AI computer is operating below a threshold processor usage. The method then includes requesting, by the call prediction processing computer, a set of cacheable requests from an orchestration service computer and receiving, from the orchestration service computer, the set of cacheable requests. Then the method includes sending, by the call prediction processing computer, a cacheable request from the set of cacheable requests to the AI computer. The method then includes receiving, by the call prediction processing computer, an output. The output is generated by the AI computer based on the cacheable request. Then the call prediction processing computer stores the output in a data repository.