| 1. |
- Imtiaz, Shamoona
(författare)
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Automated Performance Profiling of Software Applications
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- For industrial systems performance, it is desired to keep the IT infrastructure competitive through the efficient use of computer resources. However, modern software applications are complex and often utilize a broad spectrum of available hardware resources. The way how applications utilize these resources may vary from platform to platform due to the different architectural features, requirements and performance levels guaranteed by the hardware as well as due to the type of application under analysis. It becomes challenging to predict how the deployed applications will perform on a particular platform, how to improve the hardware resource utilization, and how to meet the Quality of Service (QoS) requirements. Computers these days enable us to precisely trace down the performance of applications using the Performance Monitoring Counters (PMCs) available in the Performance Monitoring Unit (PMU) of the processors. PMCs can record micro-architectural events, called PMU events, at the CPU cycle level. Tools like perf API and PAPI provide performance information using manual and selective function calls. Nevertheless, it is difficult for humans to make analyses, visualize performance over time and draw conclusions from this wealth of data without automatic and intelligent tools. In this thesis, our first contribution is to propose a cross-platform automated approach to investigate the overall performance profile of the applications. Instead of relying on a static and pre-selected list of hardware and software performance events we avoid the selection bias by capturing the entire range of performance events specific to the platform on which applications are running. The performance data being generated from shared resource environments and hierarchical resource utilization demands makes it harder to represent the behavior in one model. That being the case, it was deemed appropriate to demonstrate the compact representation of behavior. So, our next contribution is to present a simplified model to understand the behavior of performance events. Therefore, we determine segments in performance data by locating the points in their data distribution using the change point detection method. The proposed solution reduces the complexity of data handling, allows the application of further statistical analyses and provides better visualization. Lastly, to reveal the out-of-sight information, we present a customized approach to automatically identify the groups of similar performance events based on the change in their behavior. There can be several ways to group the performance data, we opt to form the groups based on change points in the behavior of the performance events. The knowledge can then be used by the decision-makers as per their interests such as for load balancing, deployments, scheduling and anomalous behavior detection.
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| 2. |
- Imtiaz, Shamoona, et al.
(författare)
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Automatic Clustering of Performance Events
- 2023
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Imtiaz, Shamoona, et al.
(författare)
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Automatic Platform-Independent Monitoring and Ranking of Hardware Resource Utilization
- 2021
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Ingår i: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728129891
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Imtiaz, Shamoona, et al.
(författare)
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Automatic Segmentation of Resource Utilization Data
- 2022
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Ingår i: 1st IEEE Industrial Electronics Society Annual On-Line Conference (ONCON) 2022. - 9798350398069
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Imtiaz, Shamoona, et al.
(författare)
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Towards Automatic Application Fingerprinting Using Performance Monitoring Counters
- 2021
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. - 9781450390576
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Konferensbidrag (refereegranskat)abstract
- In this paper, we discuss a method for application fingerprinting using conventional hardware and software performance counters. Modern applications are complex and often utilizes a broad spectra of the available hardware resources, where multiple performance counters can be of significant interest. The number of performance counters that can be captured simultaneously is, however, small due to hardware limitations in most modern computers. We propose to mitigate the hardware limitations using an intelligent mechanism that pinpoints the most relevant performance counters for an application's performance. In our proposal, we utilize the Pearson correlation coefficient to rank the most relevant PMU events and filter out events of less relevance to an application's execution. Our ultimate goal is to establish a comparable application fingerprint model using performance counters, that we can use to classify applications. The classification procedure can then be used to determine the type of application's fingerprint, such as malicious software.
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