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In this paper, we discuss a method for automatic monitoring of hardware and software events using performance monitoring counters. Computer applications are complex and utilize a broad spectra of the available hardware resources, where multiple performance counters can be of significant interest to understand. The number of performance counters that can be captured simultaneously is, however, small due to hardware limitations in most modern computers. We suggest a platform independent solution to automatically retrieve hardware events from an underlying architecture. Moreover, to mitigate the hardware limitations we propose a mechanism that pinpoints the most relevant performance counters for an application's performance. In our proposal, we utilize the Pearson's correlation coefficient to rank the most relevant performance counters and filter out those that are most relevant and ignore the rest. 

Advancement of industrial systems seek improvements to achieve required level of quality of service and efficient performance management. It is essential though to have better understanding of resource utilization behaviour of applications in execution. Even the expert engineers desire to envision dependencies and impact of one computer resource on the other. For such situations it is significant to know statistical relationship between data sets such as a resource with higher cache demand should not be scheduled together with other cache hungry process at the same time and same core. Performance monitoring data coming from hardware and software is huge and grouping of this time series data based on similar behaviour can display distinguishable execution phases. For benefits like these we opt to choose change point analysis method. By using this method study determined the optimal threshold which can identify more or less same segments for other executions of same application and same event. These segments are then validated with the help of test data. Finally the study provided segment-wise, local, compact statistical model with decent accuracy.

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 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.