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10 results for “execution traces”
Execution Traces of an MNIST Workflow on a Serverless Edge Testbed
<p>For the evaluation of a Serverless Edge Computing platform, we built an Edge Cloud testbed consisting of several machines:</p> <ul> <li>A Cloud VM</li> <li>An Nvidia Jetson TX</li> <li>Four Raspberry Pi 3b+</li> <li>Two Intel NUCs with i5 processors</li> </ul> <p>We were interested in profiling these devices with a Machine Learning workflow deployed as a serverless application. To that end, we implemented three functions: Preprocessing, Training, and Serving as OpenFaaS functions. The workflow trains an MNIST model.</p> <p> </p>
Scalasca trace analysis of HemeLB application execution with 13824 MPI processes on SuperMUC-NG
<p>The CompBioMed HPC CoE flagship application HemeLB was run with a 6.4 micron resolution "circle of Willis" geometry dataset on LRZ's SuperMUC-NG supercomputer, and its execution performance with 13824 MPI processes on 288 dual 24-core compute nodes measured by Score-P (using SIONlib) and analysed by Scalasca trace analyzer.</p>
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data
<p>The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021.</p> <p>The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf.</p> <p>Questionnaire Part 1, data file: all_usecases_wide.csv</p> <p>The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 2, data file: all_real_projects_scores.csv</p> <p>The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 3, data file: all_extrem_values_wide.csv</p> <p>The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br> </p>
Online appendix for "Leveraging Execution Trace with ChatGPT: A Case Study on Automated Fault Diagnosis" (New Ideas and Emerging Results Track in ICSME 2023)
<p>All the prompts we prepared for ChatGPT and the fault diagnosis results</p> <ul> <li>prompt_*: Prompt for ChatGPT <ul> <li>prompt_ChatGPT_setup_*.txt: Prompts to setup ChatGPT before starting the question to ChatGPT</li> <li>The other prompts: Prompts input to ChatGPT for fault diagnosis</li> </ul> </li> <li>result_*: Response from ChatGPT</li> <li>without_trace: Prompt or result when execution trace is not entered in ChatGPT</li> <li>with_trace: Prompt or result when execution trace is entered in ChatGPT</li> </ul>
Execution trace data from: Cross-boundary mobile tracking: exploring Java-to-JavaScript information diffusion in WebViews
Open the record for dataset details and reuse information.
LTTng Execution traces for ten Phoronix benchmarks (part1)
<p>The nine zip files contain the LTTng execution traces for the nine Phoronix benchmarks compress-gzip, ffmpeg, iozone, phpbench, pybench, ramspeed, scimark2, stream and unpack-linux.</p> <p>The traces have been generated on a desktop machine with a x86-64 Xeon E3-1225@3.20GHz processor, 32GB of memory, Gigabit Ethernet connection and SSD storage</p> <p>Each zip file contains 32x3 traces : 3 tracing configurations, 32 runs per configuration.</p> <p>- the all-events configuration traces the kernel.</p> <p>- the libc configuration traces memory-related function calls (malloc, free, ...)</p> <p>- the perf-trace configuration traces the hardware counters for memory accesses (L1-dcache-loads, L1-dcache-stores) and for instructions (Instructions).</p> <p>The traces for the network-loopback benchmark can be downloaded from https://doi.org/10.5281/zenodo.437179 and https://doi.org/10.5281/zenodo.437207.</p> <p>Traces for the ten benchmarks captured on a Juno board may be accessed at https://doi.org/10.5281/zenodo.437207</p> <p> </p>
LTTng Execution traces for ten Phoronix benchmarks (part 3)
<p>The network-loopback.zip2 file contains the second part of the LTTng execution traces for the network-loopback Phoronix benchmark.</p> <p>The traces have been generated on a desktop machine with a x86-64 Xeon E3-1225@3.20GHz processor, 32GB of memory, Gigabit Ethernet connection and SSD storage</p> <p>Each zip file contains 32x3 traces : 3 tracing configurations, 32 runs per configuration.</p> <p>- the all-events configuration traces the kernel.</p> <p>- the libc configuration traces memory-related function calls (malloc, free, ...)</p> <p>- the perf-trace configuration traces the hardware counters for memory accesses (L1-dcache-loads, L1-dcache-stores) and for instructions (Instructions).</p> <p>To obtain the full set of execution traces for the benchmark, you should also download the file at LTTng Execution traces for ten Phoronix benchmarks (part 2) (https://doi.org/10.5281/zenodo.437179) and concatenate the two files (cat network-loopback.zip1 network-loopback.zip2 > network-loopback.zip)</p> <p>The traces for the nine other Phoronix benchmarks, captured on the x86 machine, can be downloaded from https://doi.org/10.5281/zenodo.437170.</p> <p>The file juno.zip contains the traces generated on a Juno board ({http://www.arm.com/products/tools/development-boards/). It has one dual core Cortex-A57 processor, one quad core Cortex-A53 processor, 8GB of memory, Gigabit Ethernet connection and SSD storage. The traces correspond to one run per configuration (all-events, libc, perf-trace) for the ten Phoronix benchmarks (compress-gzip, ffmpeg, iozone, network-loopback, phpbench, pybench, ramspeed, scimark2, stream, unpack-linux). In total there are therefore 3x10 = 30 traces.</p>
LTTng Execution traces for ten Phoronix benchmarks (part 2)
<p>The zip file contains the first part of the LTTng execution traces for the network-loopback Phoronix benchmark.</p> <p>The traces have been generated on a desktop machine with a x86-64 Xeon E3-1225@3.20GHz processor, 32GB of memory, Gigabit Ethernet connection and SSD storage</p> <p>Each zip file contains 32x3 traces : 3 tracing configurations, 32 runs per configuration.</p> <p>- the all-events configuration traces the kernel.</p> <p>- the libc configuration traces memory-related function calls (malloc, free, ...)</p> <p>- the perf-trace configuration traces the hardware counters for memory accesses (L1-dcache-loads, L1-dcache-stores) and for instructions (Instructions).</p> <p>To obtain the full set of execution traces for the benchmark, you should also download the file at LTTng Execution traces for ten Phoronix benchmarks (part 3) (https://doi.org/10.5281/zenodo.437207) and concatenate the two files (cat network-loopback.zip1 network-loopback.zip2 > network-loopback.zip).</p> <p>The traces for the nine other Phoronix benchmarks, captured on the x86 machine, can be downloaded from https://doi.org/10.5281/zenodo.437170.</p> <p> </p> <p> </p>
Data Set Generated by the Fuzzy Model Constructed to Describe Execution Tracing Quality
<p>The uploaded data set was generated by the fuzzy model published in T. Galli, F. Chiclana, and F. Siewe. Genetic algorithm-based fuzzy inference system for describing execution tracing quality. Mathematics, 9(21), 2021. ISSN 2227-7390. doi: https://doi.org/10.3390/ma th9212822. URL https://www.mdpi.com/2571-5577/4/1/20.</p> <p>The goal of the data generation is to make the published model available in the form of data points in a 5D space, which facilitates the construction of simpler models to approximate the original model. The names of the columns in the .csv file constitute the quality properties of execution tracing: (1) accuracy, (2) legibility, (3) implementation, and (4) security, while column (5) contains execution tracing quality derived from the fuzzy model. The indices in brackets show the column indices in the .csv file.</p> <p>All variables lie in the continuous range [0, 100], where 100 means the best possible quality value and 0 the complete lack of quality or the lack of the given quality property. While generating the data, the inputs were increased by a step-size 5 and the model's output was collected, i.e. 4 inputs, from including 0 to 100 with 21 data points (21^4 = 194481).</p> <p> </p>
DYNAMISM - Postprocessed Execution Traces Of Android Malware and Benign Apps
<p>Protection against malware is particularly relevant on systems running the Android operating system, due to its huge use base and, therefore, its potential for monetization from the attackers.</p> <p>Protection against malware is particularly relevant in systems running the Android operating system, due to its huge users’ base and, therefore, its potential for monetization from the attackers.</p> <p>Dynamic malware detection has been widely adopted by the scientific community but not yet in practical applications.</p> <p>We release <em>DYNAMISM (Dynamic Analysis of Malware)</em>, a dataset containing execution traces of both benign and malicious applications running on Android OS, in order to facilitate further research as well as to facilitate the adoption of dynamic detection in practice. The dataset contains execution traces from 2,386 benign applications and 2,495 malicious applications taken from the Malware Genome Project repository [<a href="http://www.malgenomeproject.org/">http://www.malgenomeproject.org</a>] and from Drebin Dataset [<a href="https://www.sec.cs.tu-bs.de/~danarp/drebin/">https://www.sec.cs.tu-bs.de/~danarp/drebin/</a>]. Execution records were obtained by running the applications, one at a time, on the Android emulator. For each application, a maximum of 2,000 stimuli were applied with a maximum execution time of 10 minutes. For most of the applications, all the stimuli could be applied in this timeframe. In some of the traces none of the two limits is reached due to emulator hiccups. Collected features are related to the memory and CPU usage, network interaction and system calls and their monitoring is performed with a period of two seconds. The Android emulator of the Android Software Development Kit for Android 4.0 (release 20140702) was used. To guarantee that the system was always in a mint condition when a new sample is started, thus avoiding possible interference (e.g., changed settings, running processes, and modifications of the operating system files) from previously run samples, the Android operating system was each time re-initialized before running each application. The application execution process was automated by means of a shell script that made use of Android Debug Bridge (adb) and that was run on a Linux PC. The Monkey application exerciser was used in the script as a generator of the aforementioned stimuli. The Monkey is a command-line tool that can be run on any emulator instance or on a device; it sends a pseudo-random stream of user events (stimuli) into the system, which acts as a stress test on the application software.</p> <p>In this dataset, we provide both per-app CSV files as well as unified files, in which CSV files of single applications have been concatenated. The CSV files contain the features extracted from the raw execution record. The provided files are listed below:</p> <ul> <li> <p>benign-per_app-csv.zip - features obtained by executing benign applications, one CSV per application</p> </li> <li> <p>benign-unified-csv.zip - features obtained by executing benign applications, only one CSV file</p> </li> <li> <p>malicious-per_app-csv.zip - features obtained by executing malicious applications, one CSV per application</p> </li> <li> <p>malicious-unified-csv.zip - features obtained by executing malicious applications, only one CSV file</p> </li> </ul>
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