Cloud Database Throttling for Predictive DBCPU Anomaly Control
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Solution Overview
Problem
Conventional throttling methods for DBCPU overuse in cloud-based databases result in service disruptions and inferior customer experience due to heuristic speed adjustments, leading to timeouts and high costs.
Innovation Solution
A two-stage anomaly mitigation strategy using artificial intelligence and machine learning to predict and detect DBCPU overuse early, followed by dynamic adjustment of data request speeds to maintain utilization within a predetermined target range, employing regularized regression models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional throttling methods are used to reduce DBCPU usage, then DBCPU utilization decreases and server stress is reduced, but service quality deteriorates and customer requests timeout
Solution Approach 1:
The system performs preliminary actions by predicting future DBCPU usage patterns using machine learning models before anomalies occur. By analyzing historical usage data and identifying trends, the system proactively adjusts throttling strategies to prevent service disruptions while maintaining optimal resource utilization.
Solution Approach 2:
The throttling speed is made dynamic rather than static. The system continuously monitors actual DBCPU usage and adjusts the throttling rate in real-time based on current system conditions and predicted future usage, allowing the system to adapt to changing workloads and maintain service quality while optimizing resource utilization.
2Productivity
If data request speed is reduced to mitigate DBCPU overuse, then resource utilization improves, but customer experience deteriorates due to timeouts
Solution Approach 1:
The system implements feedback mechanisms where customer experience metrics and actual DBCPU usage are continuously monitored and fed back into the machine learning models. This feedback loop allows the system to learn from past decisions and optimize future throttling strategies to balance resource utilization with customer satisfaction.
Solution Approach 2:
The system changes the parameter of throttling speed dynamically based on multiple factors including predicted usage patterns, current system state, and customer priority levels. By adjusting this parameter in real-time, the system can maintain acceptable customer experience while optimizing resource utilization.
3Device complexity
If heuristic methods are used for throttling speed adjustment, then implementation is simple, but service disruption occurs due to inadequate adaptability
Solution Approach 1:
The system replaces simple mechanical heuristic adjustment mechanisms with intelligent machine learning-based control. The ML models analyze complex usage patterns and automatically determine optimal throttling strategies, substituting rule-based mechanical systems with adaptive intelligent systems that can handle complex, changing conditions.
Solution Approach 2:
The throttling system performs self-service by automatically monitoring its own performance, analyzing usage patterns, and adjusting its own parameters without external intervention. The machine learning models continuously optimize the throttling strategy based on real-time data, enabling the system to self-regulate and maintain service continuity.
Data Source
AI summary
A computer-implemented method is disclosed for predicting, based on a previous usage of a cloud-based computing resource by a number of users, a future usage of the cloud-based computing resource and then predicting, based on the predicted future usage, an anomaly event at the computing resource. The method also includes identifying a top contributing user that is responsible for the anomaly event and throttling an access of the top contributing user to the computing resource. The method further includes evaluating a speed of data requests received at the computing resource from the top contributing user after the throttling, and a utilization level of the computing resource. The method also includes dynamically adjusting the speed of data requests received at the computing resource, based on the evaluation of the utilization level of the computing resource, to maintain the utilization level of the computing resource within a predetermined target range.