Application Workload Characterization via Intrinsic Dimensionality
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Solution Overview
Problem
Business enterprises face challenges in efficiently utilizing computational resources due to fluctuations in capacity, leading to resource wastage or missed opportunities, as analyzing and managing the behavior of multiple applications in a computational system is complex and resource-intensive.
Innovation Solution
The application of Principal Component Analysis (PCA) to CPU utilization datasets from servers, allowing for the generation of a workload model that classifies applications based on features like periodic, noisy, or spiky patterns, enabling efficient resource allocation and synthetic workload generation for capacity planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze and manage multiple applications in a computational system, then comprehensive monitoring is achieved, but the complexity and resource consumption increase significantly
Solution Approach 1:
The patent extracts dominant behavioral patterns from complex application traces by identifying and isolating key features such as periodicity, spikes, and noise. This extraction process separates the essential characteristics from the raw data, enabling simplified representation and analysis of application behavior without losing critical information.
Solution Approach 2:
The patent transforms raw computational resource usage data into standardized behavioral parameters by detecting patterns like periodic components, spike frequencies, and noise levels. This parameter transformation converts complex time-series data into manageable metrics that characterize application behavior, reducing analysis complexity while maintaining measurement precision.
2Reliability
If computational capacity is increased to handle fluctuating demands, then service reliability is improved, but resource wastage occurs when capacity exceeds实际需求
Solution Approach 1:
The patent enables dynamic capacity adjustment by continuously monitoring and characterizing application behavior patterns. Based on detected behavioral changes, the system can dynamically scale computational resources to match actual demand, ensuring service reliability during peak periods while reducing resource allocation during low-demand periods to prevent wastage.
Solution Approach 2:
The patent implements feedback mechanisms that use characterized application behavior to inform capacity planning and resource allocation decisions. By continuously analyzing behavioral patterns and feeding this information back into resource management systems, the enterprise can optimize capacity levels to maintain reliability while minimizing resource wastage.
3Measurement precision
If detailed analysis of each application is performed individually, then precise characterization is achieved, but the time and computational resources required increase exponentially
Solution Approach 1:
The patent merges the analysis of multiple applications by identifying and extracting common behavioral patterns across different workloads. By combining analysis efforts and focusing on dominant patterns that appear across multiple applications, the system achieves precise characterization of individual applications while significantly reducing the total analysis time and computational resources required.
Data Source
AI summary
Example methods, apparatus and articles of manufacture to characterize applications are disclosed. A disclosed example method includes collecting resource utilization trace data from the two or more applications simultaneously running on one or more computational devices, determining an intrinsic dimensionality of the collected trace data, the intrinsic dimensionality representing a number of predominate features that substantially characterize the trace data, and characterizing each application's workload based on the determined intrinsic dimensionality.