Application Profiling for Predicting Compute and Memory Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems fail to effectively track and predict application execution efficiency, leading to underutilization of computing resources and unnecessary costs for users and cloud service providers.
Innovation Solution
A statistics-based modeling approach utilizing machine learning algorithms to derive efficiency-defining application profiles, classify applications by performance vectors, and predict resource utilization patterns, enabling optimal resource allocation and minimizing underutilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional computing systems are used without predictive modeling, then system simplicity is maintained, but computing resource utilization is poor and costs increase
Solution Approach 1:
The system performs preliminary profiling of applications by executing them on representative machine configurations before actual deployment. This advance characterization captures performance metrics and resource utilization patterns, enabling predictions about future execution behavior without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The invention creates simplified performance models that copy and represent the essential characteristics of complex application-machine interactions. These statistical models serve as lightweight substitutes for actual execution, allowing prediction of resource utilization without needing to run the full application or maintain complex monitoring systems.
2Measurement precision
If no efficiency tracking is implemented, then operational simplicity is maintained, but application performance optimization is prevented
Solution Approach 1:
The system enables applications to self-profile by automatically capturing their own performance metrics during execution on representative machines. The application's own runtime behavior generates the profiling data, eliminating the need for external instrumentation or complex tracking infrastructure while achieving precise performance measurement.
Solution Approach 2:
The system collects performance feedback from actual application executions and uses it to refine predictive models. This feedback loop continuously improves measurement precision by incorporating real-world performance data into the statistical models, enabling more accurate predictions without proportionally increasing system complexity.
3Productivity
If predictive modeling is not used, then computational overhead is minimized, but resource allocation efficiency deteriorates
Solution Approach 1:
The system performs resource allocation predictions in advance by profiling applications on representative machine configurations before actual deployment. This preliminary characterization captures execution patterns and resource requirements, enabling fast predictions during actual allocation without requiring time-consuming real-time analysis.
Solution Approach 2:
The invention creates lightweight statistical models that copy the essential performance characteristics of applications. These simplified models enable rapid prediction of resource allocation efficiency without requiring complex computational analysis during the allocation decision process, thus minimizing time loss while improving productivity.
Data Source
AI summary
Computerized methods of classifying compute-intensive and memory-intensive applications are disclosed. A maximum efficiency for a user application is identified. A peak performance for a machine implementation is identified under one or more Quality of Service (QoS) parameters. Actual performance and resource usage of the application implementation using one or more performance vectors is measured and compared with the maximum efficiency and the peak performance. One or more areas of interest are identified using the performance vectors. If a critical hotspot area is identified from the one or more areas of interest, at least one feature is extracted from the critical hotspot area. An application signature is built if an approximation of performance is acceptable, based on the extracted features. The compute- and memory-intensive application may be classified based on the application signature.