Cloud Resource Anomaly Control with AI-Validated Request Throttling
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Solution Overview
Problem
Conventional methods for managing cloud-based database resource anomalies, such as DBCPU overuse, often result in service disruptions and inferior customer experiences due to heuristic throttling that may time out requests, leading to high costs and reduced availability.
Innovation Solution
A two-stage anomaly mitigation strategy using AI and human reasoning models to predict and regulate DBCPU overuse, dynamically controlling data request speeds to maintain utilization within a target range, ensuring efficient resource allocation and service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional heuristic throttling is applied to reduce DBCPU overuse, then resource utilization is improved, but service availability and customer experience deteriorate due to request timeouts
Solution Approach 1:
The system performs preliminary actions by predicting future DBCPU usage patterns and identifying potential anomalies before they occur. The AI model analyzes historical usage data to forecast future resource consumption, allowing the system to proactively mitigate anomalies by adjusting throttling strategies in advance, preventing service disruptions before they happen.
Solution Approach 2:
The system implements continuous feedback loops where the AI model monitors real-time DBCPU usage, compares it against predicted patterns, and dynamically adjusts throttling strategies. This feedback mechanism allows the system to learn from past anomalies and improve its predictions, optimizing the balance between maintaining service availability and preventing resource overuse.
2Reliability
If low-speed throttling is applied to reduce DBCPU stress, then resource utilization is improved, but request processing time increases leading to timeouts and service disruption
Solution Approach 1:
The system dynamically adjusts throttling speeds based on real-time conditions and AI predictions. Instead of applying static low-speed throttling, the system varies the throttling intensity according to the predicted anomaly severity and current system state, optimizing the balance between reducing DBCPU stress and maintaining acceptable request processing times.
Solution Approach 2:
The system changes key parameters such as throttling speed, queue priority, and resource allocation based on AI predictions. By dynamically adjusting these parameters in response to predicted anomalies, the system can prevent resource overuse while minimizing the impact on request processing times and maintaining service quality.
3Productivity
If AI models are used to predict and dynamically control data requests, then service throughput is improved, but system complexity increases
Solution Approach 1:
The system introduces an AI model as an intermediary layer between data requests and the DBCPU resources. This intermediary analyzes usage patterns, predicts anomalies, and makes intelligent routing decisions, thereby improving service throughput while managing complexity through a specialized component designed for predictive analysis.
Solution Approach 2:
The system segments the data management functionality into distinct components: the AI prediction model, the throttling control mechanism, and the data request handling system. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by distributing complexity across modular elements.
Data Source
AI summary
A computer-implemented method is disclosed for predicting a future usage of a cloud-based computing resource based on a previous usage of the resource by users, and predicting an anomaly event at the resource. The method also includes identifying a top contributing user responsible for the anomaly event, throttling an access of the top contributing user, evaluating a speed of data requests received from the top contributing user, and maintaining a utilization level of the resource within a predetermined target range. The method further includes dynamically controlling the speed of data requests based on the evaluation of the speed of data requests and a controlling speed of data request recommended by a first artificial intelligence model. The recommendations of the first artificial intelligence model may be validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive recommendation of the first artificial intelligence model.