Automatic Clustering of Performance Events

Modern hardware and software are becoming increasingly complex due to advancements in digital and smart solutions. This is why industrial systems seek efficient use of resources to confront the challenges caused by the complex resource utilization demand. The demand and utilization of different reso...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1 - 8
Main Authors: Imtiaz, Shamoona, Capannini, Gabriele, Carlson, Jan, Behnam, Moris, Jagemar, Marcus
Format: Conference Proceeding
Language:English
Published: IEEE 12.09.2023
Subjects:
Behavioral sciences
Change point detection
Cost function
Hardware
Hierarchical clustering
Manuals
Measurement
Performance events
Performance monitoring counters
Sigmoid function
Similarity detection
Similarity measurement
Software
Software algorithms
ISSN:1946-0759
Online Access:Get full text
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Summary:Modern hardware and software are becoming increasingly complex due to advancements in digital and smart solutions. This is why industrial systems seek efficient use of resources to confront the challenges caused by the complex resource utilization demand. The demand and utilization of different resources show the particular execution behavior of the applications. One way to get this information is by monitoring performance events and understanding the relationship among them. However, manual analysis of this huge data is tedious and requires experts' knowledge. This paper focuses on automatically identifying the relationship between different performance events. Therefore, we analyze the data coming from the performance events and identify the points where their behavior changes. Two events are considered related if their values are changing at "approximately" the same time. We have used the Sigmoid function to compute a real-value similarity between two sets (representing two events). The resultant value of similarity is induced as a similarity or distance metric in a traditional clustering algorithm. The proposed solution is applied to 6 different software applications that are widely used in industrial systems to show how different setups including the selection of cost functions can affect the results.
ISSN:1946-0759
DOI:10.1109/ETFA54631.2023.10275660