Cloud Architecture Recommender Using Automated Workload Instrumentation
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Solution Overview
Problem
Legacy systems for configuring computing platforms are inadequate in evaluating alternative cloud architectures, leading to inefficient resource utilization and high costs due to oversimplified models, lack of in-situ measurements, and inability to recommend optimal configurations based on automated test measurements.
Innovation Solution
A method and system using automated workload instrumentation to measure and compare performance metrics across different cloud architectures, providing detailed recommendations for optimal resource allocation and configuration adjustments based on actual usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated workload instrumentation is implemented to measure performance metrics across multiple cloud architectures, then measurement precision and reliability of architecture evaluation is improved, but device complexity and implementation complexity increase
Solution Approach 1:
The system performs self-instrumentation by automatically injecting measurement code into the application without requiring external manual setup. The instrumentation framework autonomously identifies performance-critical operations and inserts appropriate measurement instruments, enabling the system to evaluate its own performance across different cloud architectures while maintaining high measurement precision.
Solution Approach 2:
The system pre-instruments the application with measurement code before deployment to different cloud architectures. By establishing the instrumentation framework in advance, the system captures performance metrics across multiple architectures without requiring complex real-time setup, thereby improving measurement precision while reducing implementation complexity.
2Reliability
If in-situ measurements are performed on actual applications deployed to cloud infrastructure, then reliability of performance data is improved, but loss of time and computational overhead increase
Solution Approach 1:
The system performs measurements at strategically selected intervals rather than continuously monitoring all operations. By sampling performance metrics periodically at critical decision points in the application execution flow, the system maintains high reliability of performance data while minimizing the time overhead associated with continuous measurement.
Solution Approach 2:
The system instruments only the most performance-critical operations within the application rather than measuring every operation. By selectively applying measurement instruments to key bottlenecks and performance-determining operations, the system achieves reliable performance data with minimal computational overhead and time loss.
3Adaptability or versatility
If comprehensive performance metrics are collected across multiple cloud architectures, then adaptability and quality of architecture recommendations are improved, but loss of information and data processing complexity increase
Solution Approach 1:
The system extracts and isolates the most critical performance metrics from the comprehensive set of collected data. By identifying and extracting key performance indicators that directly influence architecture selection decisions, the system maintains high adaptability and evaluation coverage while reducing data management complexity through focused analysis of essential metrics.
Solution Approach 2:
The system dynamically adjusts which performance parameters are measured and emphasized based on the specific application characteristics and workload patterns. By changing the set of active measurement parameters according to the evaluation context, the system achieves comprehensive architecture evaluation while managing information loss through adaptive parameter selection.
4Ease of operation
If legacy modeling techniques are used to estimate system performance, then ease of operation and implementation simplicity are maintained, but measurement precision and reliability of performance predictions deteriorate
Solution Approach 1:
The system replaces manual modeling and estimation techniques with automated instrumentation that directly measures performance. By substituting the mechanical process of creating and maintaining performance models with automated code injection and direct measurement, the system maintains ease of operation while dramatically improving measurement precision and prediction reliability.
Data Source
AI summary
A method, system, and computer program product for of configuring cloud computing platforms. One such method serves for recommending alternative computing architectures for a selected application using automated instrumentation of the application under an abstracted workload. The method commences by measuring workload characteristics of the selected application using pre-determined performance parameters. Additional performance parameters to be measured are selected based on previous measurements, and further analysis includes instrumenting the application to provide measurement instruments corresponding to the respective selected additional performance parameters. Such hardware- or software-based instruments are used for determining a baseline set of performance metrics by measuring the performance of the selected application on a first (e.g., currently-in-use) computing architecture, then, measuring the performance of the application on other proposed computing architectures. Charts and reports are used for comparing the performance of the selected application on the currently-in-use architecture to any of the other proposed computing architectures.