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Speeding up profiling program's runtime characteristics for workload consolidation.

Lin Wang1,2, Depei Qian1,2,3, Zhongzhi Luan1,2

  • 1Sino-German Joint Software Institute, Beihang University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a fast profiling method to accurately measure program resource sensitivity for efficient workload consolidation in data centers. The approach significantly reduces profiling time while maintaining high accuracy.

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Area of Science:

  • Computer Science
  • Systems and Networking
  • Performance Engineering

Background:

  • Workload consolidation enhances resource utilization in data centers.
  • Effective consolidation requires understanding program runtime characteristics, specifically resource sensitivity.
  • Current methods for measuring resource sensitivity are time-consuming.

Purpose of the Study:

  • To propose a fast and accurate method for profiling program resource sensitivity.
  • To enable efficient workload consolidation by providing detailed runtime characteristics.
  • To reduce the time and experimental overhead associated with resource sensitivity analysis.

Main Methods:

  • Developed a two-level profiling acceleration strategy.
  • Utilized resource usage information to set upper bounds for resource control, narrowing the experimental range.
  • Employed an interpolation-based prediction model to estimate resource sensitivity, reducing direct program executions.

Main Results:

  • The proposed method significantly shortens profiling time.
  • The two-level strategy accurately captures program resource sensitivity.
  • Average absolute error for resource sensitivity was controlled within 0.05.

Conclusions:

  • The fast profiling method effectively accelerates resource sensitivity analysis.
  • This technique supports more efficient and accurate workload consolidation in data centers.
  • The approach balances speed and accuracy in performance profiling.