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4 results for “runtime monitoring”
Data for: A Comparison of the Influence of Different Multi-Core Processors on the Runtime Overhead for Application-Level Monitoring
<p>Application-level monitoring is required for continuously operating software systems to maintain their performance and availability at runtime. Performance monitoring of software systems requires storing time series data in a monitoring log or stream. Such monitoring may cause a significant runtime overhead to the monitored system.</p> <p>In this paper, we evaluate the influence of multi-core processors on the overhead of the Kieker application-level monitoring framework. We present a breakdown of the monitoring overhead into three portions and the results of extensive controlled laboratory experiments with microbenchmarks to quantify these portions of monitoring overhead under controlled and repeatable conditions. Our experiments show that the already low overhead of the Kieker framework may be further reduced on multi-core processors with asynchronous writing of the monitoring log.</p> <p>Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Dataset for "MarMot: Metamorphic Runtime Monitoring of Autonomous Driving Systems"
<p>This is the dataset used in the paper "MarMot: Metamorphic Runtime Monitoring of Autonomous<br> Driving Systems".</p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Runtime Monitoring of Refinement Progress in CEGAR-based Model Checking: Dataset
<p>Dataset used in the evaluation of Part II of the MSc thesis titled "Extending the Capabilities of the CEGAR Model Checking Algorithm"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.