Hardware Safety Margin Prediction via Resource Histogram Analysis
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Solution Overview
Problem
Cloud companies face challenges in determining the optimal hardware allocation for software as a service (SaaS) to ensure smooth operation without oversupply or undersupply, leading to performance issues or unnecessary resource waste.
Innovation Solution
A computer system and method that analyze historic records of hardware metrics and feedback metrics to generate a histogram plotting the frequency of performance issues based on the difference between allocated and used resources. This allows for the determination of a threshold value indicating a safety margin for resource allocation, ensuring optimal performance without excessive resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware allocation is increased to ensure smooth service operation, then service reliability is improved, but hardware cost and resource waste increase
Solution Approach 1:
The patent changes the parameter of safety margin from a fixed static value to a dynamic value that adjusts based on historical performance data. By analyzing historical hardware metrics and feedback metrics, the system determines optimal safety margin values that vary over time, allowing the hardware allocation to adapt to changing service demands while minimizing resource waste.
Solution Approach 2:
The system enables self-service by automatically determining optimal safety margins through analysis of historical data and performance feedback. The automated system processes historical hardware metrics, calculates safety margin values, and adjusts hardware allocation without manual intervention, reducing both resource waste and operational costs while maintaining service reliability.
2Productivity
If hardware allocation is decreased to reduce cost, then resource efficiency is improved, but service performance and reliability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring service performance and hardware metrics. The system uses feedback metrics indicating whether software applications experienced performance issues to adjust safety margin values. This closed-loop feedback ensures that hardware allocation is optimized to maintain service reliability while improving resource utilization efficiency.
3Stability of the object's composition
If safety margin is increased for new customers, then service stability is improved, but hardware cost and business margin decrease
Solution Approach 1:
The patent applies preliminary action by analyzing historical hardware metrics and performance data before making hardware allocation decisions for new customers. The system uses historical feedback metrics to predict optimal safety margin values in advance, allowing the business to allocate the minimum necessary hardware resources while maintaining service stability, thereby preserving business margins.
4Measurement precision
If hardware metrics analysis is performed to determine optimal allocation, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The patent uses copying by creating simplified representations of complex hardware metrics and performance data through histograms. The system copies historical data into binned histogram representations that capture essential patterns while reducing data complexity. This allows precise determination of safety margin values without requiring overly complex analysis systems.
Data Source
AI summary
To predict hardware safety margins, historic records of hardware metrics indicating amounts of allocated and used resources for one or more software applications are obtained. Feedback metrics indicating performance issues for the software are determined based on the metrics. Then a histogram is generated plotting a frequency of the feedback metric using bins based on a difference between the allocated resources and the used resources. A threshold value is determined for the difference by iteratively determining, starting with a rightmost bin, whether data points in that bin indicate poor performance of the software based on the difference between the allocated resources and the used resources. The threshold value indicates a safety margin for operating the one software applications without performing poorly. Resources for the one or more software applications are then re-allocated according to the safety margin.