Dynamic Alert Thresholds for Cloud Resource Utilization Trends
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Solution Overview
Problem
Cloud computing services face challenges in dynamically adjusting resource utilization thresholds to anticipate and prevent resource allocation issues, as existing thresholds are fixed and do not account for changing circumstances such as time of day or user activity patterns.
Innovation Solution
A data center system that analyzes client instance performance trends, aligns and compresses performance data, and dynamically adjusts thresholds for sending resource utilization alerts, using alignment logic, frequency-based filtering, and cluster analysis to anticipate future resource needs based on historical patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed thresholds are used for resource utilization alerts, then the system is simple to operate, but the thresholds cannot adapt to changing circumstances such as time of day or user activity patterns
Solution Approach 1:
The patent implements dynamic threshold adjustment by analyzing historical performance data and automatically modifying thresholds based on detected patterns and trends. The system transitions from static fixed thresholds to dynamic thresholds that adapt to changing circumstances such as time of day, day of week, and user activity patterns, directly resolving the contradiction between adaptability and complexity
Solution Approach 2:
The system continuously monitors performance metrics and uses this feedback to adjust thresholds automatically. By implementing a closed-loop feedback mechanism where performance data informs threshold adjustments, the system achieves adaptability without requiring manual intervention, thereby managing the complexity aspect of the contradiction
2Reliability
If fixed thresholds are used, then the system requires less computational resources, but the alerts are not sent at the most appropriate times
Solution Approach 1:
The system performs preliminary analysis of historical performance data to establish baseline patterns and trends before actual threshold adjustment is needed. By pre-processing and understanding historical data patterns, the system can make more accurate real-time threshold adjustments without excessive computational overhead during critical alert determination moments
Solution Approach 2:
The patent dynamically changes threshold parameters based on analyzed performance trends and contextual factors such as time of day and user activity. This allows the system to optimize alert accuracy by adjusting parameters according to actual system conditions rather than using fixed values, while the computational cost is managed through efficient data analysis methods
3Measurement precision
If dynamic threshold adjustment is implemented, then resource overutilization can be anticipated more accurately, but the system complexity increases
Solution Approach 1:
The system performs self-adjustment of thresholds by automatically analyzing its own performance data and making adjustments without external intervention. This self-service capability allows the system to improve measurement precision through continuous learning from its operational data, while managing complexity through automation rather than manual processes
Solution Approach 2:
The system creates simplified representations or models of complex performance patterns by analyzing historical data. By working with aggregated and processed performance metrics rather than raw data, the system achieves accurate resource utilization prediction while reducing the computational complexity of data processing through effective data summarization and pattern recognition
Data Source
AI summary
The present disclosure includes analyzing client instance performance trends to predict future client instance performance and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. In particular, a data center providing a platform as a service includes a database that stores performance data associated with client instances. The data center also includes alignment logic that temporally aligns the performance data, and a frequency based filter that compresses the aligned performance data based on frequency of values. The data center further includes dynamic threshold adjustment logic that adjusts thresholds associated with sending performance trend alerts based on analyzing the compressed set of performance data. In this manner, the thresholds may be dynamically adjusted for changing circumstances and/or relevant details associated with resource usage, and thus may more accurately send performance trend alerts indicative of situations when resource utilization becomes high and resources become low.