Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “serverless”
Leveraging Serverless Computing for Continuous Integration and Delivery
<p>This work has been a first implementation of the usage of AWS Lambda for running CI/CD tasks. This has been implemented in TeamCity, a CI/CD tool by JetBrains. We investigated the possible solutions for using AWS Lambda for running CI/CD tasks, but also did a depth analysis on how it compares to Amazon ECS for running CI/CD tasks.</p> <p>We were able to understand that there are CI/CD tasks that can be optimised by making use of FaaS. These tasks must be able to be executed in under 15 minutes (or be splittable into 15 minutes tasks), and would benefit more if they're either a high throughput of tasks in a small period of time, or very little executions over time. These tasks will benefit from both a higher performance and cost efficiency.</p> <p>This package includes: </p> <ul> <li>TeamCity Backup - backup with all of the CI/CD configurations used for these experiments. It is important to mention that the replication of these experiments does require a license for TeamCity and to apply a new connection to AWS;</li> <li>Tasks Performance tests - all of the results obtained during the experiments led to our conclusions.</li> </ul>
Serverless Literature Dataset
<p>Systematic database of literature around serverless computing and applications, cloud functions, Function-as-a-Service (FaaS) and closely related topics. Retrieved through keyword search from DBLP and manual additions. Covering 2016-first bits of 2019. Insights into technologies and trends. Evolving through open community curation.</p>
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>
Azure dataset sample for OpenDC Serverless simulator
<p>This dataset is representative of the sample used in the Serverless in the Wild paper [1] experiments. The dataset is used to reproduce the experiments in the OpenDC Serverless FaaS simulator [2]. The original dataset [3] has been sampled converted into the OpenDC Serverless trace format. </p> <p>This is part of Soufiane Jounaid's Bachelor thesis on designing, evaluating, and implementing the first FaaS platform simulator conducted within the AtLarge Research group in the Vrije Universiteit Amsterdam.</p> <p>[1] <a href="https://arxiv.org/pdf/2003.03423.pdf">Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., ... & Bianchini, R. (2020). Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider. arXiv preprint arXiv:2003.03423.</a></p> <p>[2] <a href="https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing">https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing</a> </p> <p>[3] <a href="https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md">https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md</a></p>
CloudButton Serverless Benchmarks results (June 2022)
<p>Results of the <strong>CloudButton Serverless Benchmark</strong>. It includes plots with results of the <strong>Flops benchmark</strong> and the <strong>Storage benchmark</strong>. The first one evaluates compute power and scalability of cloud providers by running multiple compute intensive tasks concurrently. The second one evaluates the throughput of read and write operations to the object storage services of different cloud providers by running such operations in many tasks and concurrently.</p> <p>The benchmark has been run on 6 FaaS services (IBM Cloud Functions, AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, Google Cloud Run, and Alibaba Aliyun Function Compute) and 5 Object Storage services (IBM Cloud Object Storage, AWS S3, Microsoft Azure Blob, Google Storage, Alibaba Aliyun Object Storage Service).</p>
Replication Package for "TriggerBench: A Performance Benchmark for Serverless Function Triggers"
<p>This replication package contains the code (`aws-triggers` and `azure-trigger`), data analysis scripts (`data-analysis`), and dataset (`data`) of the TriggerBench cross-provider serverless benchmark.</p> <p>It also bundles a customized extension of the `serverless-benchmarker` tool to automate and analyze serverless performance experiments.</p> <p><strong>TriggerBench</strong></p> <p>The Github repository <a href="https://github.com/joe4dev/trigger-bench/">joe4dev/trigger-bench</a> contains the last version of TriggerBench. This replication package describes the version for the paper "TriggerBench: A Performance Benchmark for Serverless Function Triggers".</p> <p>TriggerBench currently supports three triggers on AWS and eight triggers on Microsoft Azure.</p> <p><strong>Dataset</strong></p> <p>The `data/aws` and `data/azure` directories contain data from benchmark executions from April 2022.</p> <p>Each execution is a separate directory with a timestamp in the format `yyyy-mm-dd-HH-MM-SS` (e.g., `2022-04-15_21-58-52`) and contains the following files:</p> <ul> <li>`k6_metrics.csv`: Load generator HTTP client logs in CSV format (see [K6 docs](https://k6.io/docs/results-visualization/csv/))</li> <li>`sb_config.yml`: serverless benchmarker execution configuration including experiment label.</li> <li>`trigger.csv`: analyzer output CSV per trace. <ul> <li>`root_trace_id`: The trace id created by k6 and adopted by the invoker function</li> <li>`child_trace_id`: The trace id newly created by the receiver function if trace propagation is not supported (this is the case for most asynchronous triggers)</li> <li>`t1`-`t4`: Timestamps following the trace model (see paper)</li> <li>`t5`-`t9`: Additional timestamps for measuring timestamping overhead</li> <li>`coldstart_f1=True|False`: coldstart status for invoker (f1) and receiver (f2) functions</li> <li>`trace_ids.txt`: text file with each pair of `root_trace_id` and `child_trace_id` on a new line.</li> </ul> </li> <li>`traces.json`: raw trace JSON representation as retrieved from the provider tracing service. For AWS, see [X-Ray segment docs](https://docs.aws.amazon.com/xray/latest/devguide/xray-api-segmentdocuments.html). For Azure, see [Application Insights telemetry data model](https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model).</li> <li>`workload_options.json`: [K6 load scenario](https://k6.io/docs/using-k6/scenarios/) configuration.</li> </ul> <p><strong>Replicate Data Analysis</strong></p> <p><strong>Installation</strong></p> <p>1. Install [Python](https://www.python.org/downloads/) 3.10+</p> <p>2. Install Python dependencies `pip install -r requirements.txt`</p> <p><strong>Create Plots</strong></p> <p>1. Run `python plots.py` generates the plots and the statistical summaries presented in the paper.</p> <p>By default, the plots will be saved into a `plots` sub-directory.<br> An alternative output directory can be configured through the environment variable `PLOTS_PATH`.</p> <p>> Hint: For interactive development, we recommend the VSCode [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) in [interactive mode](https://youtu.be/lwN4-W1WR84?t=107).</p> <p> </p> <p><strong>Replicate Cloud Experiments</strong></p> <p>The following experiment plan automates benchmarking experiments with different types workloads (constant and bursty).</p> <p>This generates a new dataset in the same format as described above.</p> <p> </p> <ol> <li> Set up a load generator as vantage point following the description in [LOADGENERATOR](./serverless-benchmarker/docs/LOADGENERATOR.md).</li> <li> Choose the `PROVIDER` (aws or azure) in the [constant.py](./experiment-plans/constant.py) experiment plan</li> <li> Run the [constant.py](./experiment-plans/constant.py) experiment plan <ol> <li> Open tmux</li> <li> Activate virtualenv `source sb-env/bin/activate`</li> <li> Run `./constant.py 2>&1 | tee -a constant.log`</li> </ol> </li> </ol> <p><strong>Contributors</strong></p> <p>The initial trigger implementations for AWS and Azure are based on two master thesis projects at Chalmers University of Technology in Sweden supervised by Joel:</p> <ul> <li>AWS + Azure: <a href="https://odr.chalmers.se/handle/20.500.12380/302822">Performance Comparison of Function-as- a-Service Triggers: A Cross-Platform Performance Study of Function Triggers in Function-as-a-Service</a> by Marcus Bertilsson and Oskar Grönqvist, 2021.</li> <li>Azure Extension: <a href="https://odr.chalmers.se/handle/20.500.12380/305138">Serverless Function Triggers in Azure: An Analysis of Latency and Reliability</a> by Henrik Lagergren and Henrik Tao, 2022.</li> </ul> <p>Joel contributed many improvements to their original source code as documented in the import commits <a href="https://github.com/joe4dev/trigger-bench/commit/a00b67a1dd8476ca77d026e59adf2674c7807e68">a00b67a</a> and <a href="https://github.com/joe4dev/trigger-bench/commit/6d2f5ef8bda0596b3f295cb6c6cbeba212c6ef43">6d2f5ef</a> and developed TriggerBench as an integrated benchmark suite (see commit history for detailed changelog).</p>
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
<p>This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice». Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.</p>
IISWC 2021 Characterizing and Mitigating the I/O Scalability Challenges for Serverless Applications (Dataset and Scripts)
<p>As serverless computing paradigm becomes widespread, it is important to understand the I/O performance characteristics on serverless computing platforms. To the best of our knowledge, we provide the first study that analyzes the observed I/O performance characteristics -- some expected and some unexpected findings that reveal the hidden, complex interactions between the application I/O characteristics, the serverless computing platform, and the storage engines. The goal of this analysis is to provide data-driven guidelines to serverless programmers and system designers about the performance trade-offs and pitfalls of serverless I/O.</p>
SPEC RG Technical Report: A Review of Serverless Use Cases and their Characteristics - Dataset
<p><strong><em>PrettyDataset.xlsx</em></strong></p> <p>Our data set of 89 serverless use cases and their mapping to 24 characteristics. In form of an Excel file to simplify manual inspection. Columns represent characteristics, rows represent use cases.</p> <p><em><strong>Dataset.csv</strong></em></p> <p>Raw version of our data set of 89 serverless use cases and their mapping to 24 characteristics as a CSV file for automated analysis. Columns represent characteristics, rows represent use cases.</p> <p><em><strong>Initial Characterizations.csv</strong></em></p> <p>Reviewer characterizations prior to the discussion and consolidation phase. Required for the calculation of the fleiss kappa score.</p> <p><em><strong>CalculateKappa.py</strong></em></p> <p>Python script for the calculation of the fleiss kappa score shown in the technical report. Requires Python 3.6.</p> <p><em><strong>GenerateFigures.py</strong></em></p> <p>Python script for the generation of all figures shown in the technical report. Requires Python 3.6.</p>
CloudButton Serverless Benchmarks results (May 2020)
<p>Results of the <strong>CloudButton Serverless Benchmark</strong>. It includes plots with results of the <strong>Flops benchmark</strong> and the <strong>Storage benchmark</strong>. The first one evaluates compute power and scalability of cloud providers by running multiple compute intensive tasks concurrently. The second one evaluates the throughput of read and write operations to the object storage services of different cloud providers by running such operations in many tasks and concurrently.</p> <p>The benchmark has been run on 6 FaaS services (IBM Cloud Functions, AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, Google Cloud Run, and Alibaba Aliyun Function Compute) and 5 Object Storage services (IBM Cloud Object Storage, AWS S3, Microsoft Azure Blob, Google Storage, Alibaba Aliyun Object Storage Service).</p>
Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications - Dataset
<p>This dataset contains the raw collected traces, preprocessed versions of the traces, and summary figures for the data associated with our manuscript <em>Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications.</em></p> <p>It contains over 7.5 million (7 564 830) traces of the ten applications integrated with ServiBench. The measurements were conducted on AWS Lambda in the us-east-1 region in late 2021 and early 2022. For more details on how the traces were collected we refer to our manuscript.</p> <p>For details on how to replicate our existing analysis on this dataset, we refer to https://github.com/ServiBench/ReplicationPackage</p>
Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads
<p>Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads</p> <p>Please see the README for information on parsing and data structure.<br> Some of the data might need additional explanations. In case of confusion don't hesitate to contact me under jacob.palecek@outlook.com</p>
Unveiling the Skills and Responsibilities of Serverless Practitioners: An Empirical Investigation
<p>This replication package accompanies the research paper titled "Unveiling the Skills and Responsibilities of Serverless Practitioners: An Empirical Investigation." The package contains the data and code used to perform the analyses presented in the paper.</p> <p><strong>Purpose:</strong> The primary goal of this dataset is to provide the necessary resources for replicating the results of our study on the skills and responsibilities of serverless practitioners. Researchers can use this package to verify our findings, perform additional analyses, or adapt our methodology for their own research.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Analysis Sheet.xlsx</strong>: This Excel file includes the raw data and the processed data used in our analyses. The file consists of the following sheets:</p> <ul> <li><strong>Raw Data</strong>: Contains the unprocessed data collected from various sources. </li> <li><strong>Meta Data</strong>: Provides detailed information about the data collection process.</li> <li><strong>Pilot Sample</strong>: Includes data from the pilot study conducted to test the research methodology. This sheet helps in understanding the preliminary results and adjustments made for the main study.</li> <li><strong>Selected Sample</strong>: Contains data from the sample selected for the main study. </li> <li><strong>Roles Sorting</strong>: This sheet categorizes different roles based on the data collected. </li> <li><strong>Initial Coding</strong>: Provides the initial coding of qualitative data. This includes codes assigned to various responses and observations, serving as a foundation for thematic analysis.</li> <li><strong>Responsibilities Thematic</strong>: Contains thematic analysis of the responsibilities associated with different roles. This sheet categorizes responsibilities into various themes based on the data.</li> <li><strong>Soft Skill Thematic</strong>: Includes thematic analysis of soft skills identified in the study. The sheet categorizes soft skills into different themes, helping in understanding the skill requirements across roles.</li> <li><strong>Hard Skill Thematic</strong>: Provides a thematic analysis of hard skills identified in the study. Similar to the soft skills sheet, this categorizes hard skills into different themes to analyze their distribution and importance across roles.</li> </ul> </li> <li> <p><strong>Python Script</strong>: The Python script used to automate the data collection process from Glassdoor. This script employs Selenium, Selenium Stealth, and PostgreSQL to retrieve real-time job listings, ensuring data integrity and minimizing selection bias.</p> </li> </ol> <p> </p>
Replication package tied with the paper "WasteLess: An Optimal Provisioner for Self-Adaptive Second-Generation Serverless Applications"
<p>This is the replication package tied with paper "WasteLess: An Optimal Provisioner for Self-Adaptive Second-Generation Serverless Applications" submitted to the 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'25)</p> <p><strong>Contents<br></strong>The package contains a single zip file (WlessPackage.zip) with all the material used for conducting the paper experimentsation. Refer to the README.md file contained in the zip package for a detailed description of its content.</p>
The State of Serverless Applications: Collection,Characterization, and Community Consensus - Replication Package
<p>The replication package for our article <em>The State of Serverless Applications: Collection,Characterization, and Community Consensus</em> provides everything required to reproduce all results for the following three studies:</p> <ul> <li>Serverless Application Collection</li> <li>Serverless Application Characterization</li> <li>Comparison Study</li> </ul> <p><strong>Serverless Application Collection</strong></p> <p>We collect descriptions of serverless applications from open-source projects, academic literature, industrial literature, and scientific computing.</p> <p><em>Open-source Applications</em></p> <p>As a starting point, we used an existing data set on open-source serverless projects from <a href="https://gupea.ub.gu.se/bitstream/2077/62544/1/gupea_2077_62544_1.pdf">this study</a>. We removed small and inactive projects based on the number of files, commits, contributors, and watchers. Next, we manually filtered the resulting data set to include only projects that implement serverless applications. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Open%20source%20filtering.xlsx">a table</a> containing all projects that remained after the filtering alongside the notes from the manual filtering.</p> <p><em>Academic Literature Applications</em></p> <p>We based our search on an <a href="https://doi.org/10.5281/zenodo.1175423">existing community-curated dataset</a> on literature for serverless computing consisting of over 180 peer-reviewed articles. First, we filtered the articles based on title and abstract. In a second iteration, we filtered out any articles that implement only a single function for evaluation purposes or do not include sufficient detail to enable a review. As the authors were familiar with some additional publications describing serverless applications, we contributed them to the community-curated dataset and included them in this study. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Academic%20literature%20filtering.xlsx">a table</a> with our notes from the manual filtering.</p> <p><em>Scientific Computing Applications</em></p> <p>Most of these scientific computing serverless applications are still at an early stage and therefore there is little public data available. One of the authors is employed at the German Aerospace Center (DLR) at the time of writing, which allowed us to collect information about several projects at DLR that are either currently moving to serverless solutions or are planning to do so. Additionally, an application from the German Electron Synchrotron (DESY) could be included. For each of these scientific computing applications, we provide a document containing a description of the project and the names of our contacts that provided information for the characterization of these applications.</p> <ul> <li>SC1 Copernicus Sentinel-1 for near-real-time water monitoring</li> <li>SC2 Reprocessing Sentinel 5 Precursor data with ProEO</li> <li>SC3 High-Performance Data Analytics for Earth Observation</li> <li>SC4 Tandem-L exploitation platform</li> <li>SC5 Global Urban Footprint</li> <li>SC6 DESY - High Throughput Data Taking</li> </ul> <p><em>Collection of serverless applications</em></p> <p>Based on the previously described methodology, we collected a diverse dataset of 89 serverless applications from open-source projects, academic literature, industrial literature, and scientific computing. This dataset is can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">i</a>n Dataset.xlsx.</p> <p><strong>Serverless Application Characterization</strong></p> <p>As previously described, we collected 89 serverless applications from four different sources. Subsequently, two randomly assigned reviewers out of seven available reviewers characterized each application along 22 characteristics in a structured collaborative review sheet. The characteristics and potential values were defined a priori by the authors and iteratively refined, extended, and generalized during the review process. The initial moderate inter-rater agreement was followed by a discussion and consolidation phase, where all differences between the two reviewers were discussed and resolved. The six scientific applications were not publicly available and therefore characterized by a single domain expert, who is either involved in the development of the applications or in direct contact with the development team.</p> <p><em>Initial Ratings & Interrater Agreement Calculation</em></p> <p>The initial reviews are available as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Initial%20Characterizations.csv">a table</a>, where every application is characterized along with the 22 characteristics. A single value indicates that both reviewers assigned the same value, whereas a value of the form <code>[Reviewer 2] A | [Reviewer 4] B</code> indicates that for this characteristic, reviewer two assigned the value A, whereas reviewer assigned the value B.</p> <p>Our script for the calculation of the Fleiß-Kappa score based on this data is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/CalculateKappa.py">publically available</a>. It requires the python package <code>pandas</code> and <code>statsmodels</code>. It does not require any input and assumes that the file <code>Initial Characterizations.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 CalculateKappa.py </code></pre> <p><em>Results Including Unknown Data</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find.</p> <p>For six characteristics, many applications were assigned the ''Unknown'' value, i.e., the reviewers were not able to determine the value of this characteristic. Therefore, we excluded these characteristics from this study. For the remaining characteristics, the percentage of ''Unknowns'' ranges from 0–19% with two outliers at 25% and 30%. These ''Unknowns'' were excluded from the percentage values presented in the article. As part of our replication package, we provide the raw results for each characteristic including the ''Unknown'' percentages in the form of bar charts.</p> <p>The script for the generation of these bar charts is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateResultsIncludingUnknown.py">part of this replication package</a>). It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It does not require any input and assumes that the file <code>Dataset.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 GenerateResultsIncludingUnknown.py </code></pre> <p><em>Final Dataset & Figure Generation</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find. Following this process, we were able to resolve all conflicts, resulting in a collection of 89 applications described by 18 characteristics. This dataset is available here: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">link</a></p> <p>The script to generate all figures shown in the chapter "Serverless Application Characterization can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateFigures.py">here</a>. It does not require any input but assumes that the file <code>Dataset.csv</code> is located in the same folder. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It can be executed as follows:</p> <pre><code>python3 GenerateFigures.py </code></pre> <p><em>Comparison Study</em></p> <p>To identify existing surveys and datasets that also investigate one of our characteristics, we conducted a literature search using Google as our search engine, as we were mostly looking for grey literature. We used the following search term:</p> <pre><code>("serverless" OR "faas") AND ("dataset" OR "survey" OR "report") after: 2018-01-01 </code></pre> <p>This search term looks for any combination of either serverless or faas alongside any of the terms dataset, survey, or report. We further limited the search to any articles after 2017, as serverless is a fast-moving field and therefore any older studies are likely outdated already. This search term resulted in a total of 173 search results. In order to validate if using only a single search engine is sufficient, and if the search term is broad enough, we checked if the seven studies the authors were already familiar with are contained in the search results. As all seven studies were contained in the search results, we concluded that the literature search was broad enough. In a first iteration, we filtered out all results that do not either report original data or report on data from another study. Next, we removed all reports on secondary data, where the original study was already contained in the search results. This process resulted in a total of 16 identified studies. Finally, we determined for each identified study if they investigate one of our characteristics. This resulted in a total of ten related studies. The results from the literature search and the notes from the filtering are part of this replication package as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/ComparisonSearch.xlsx">a table</a>.</p> <p>As these studies use different answer options than our study, we mapped their answer options to ours. In many cases, this was straightforward, such as mapping HTTP to HTTP Request. If the answer option granularities between the studies differed, we aggregated answer options from the study with lower granularity to match the higher granularity study. In case the lower granularity study allowed multiple answers, we selected only the highest value instead of aggregating them, to avoid counting a single study participant multiple times. As this mapping process is somewhat subjective, we provide a detailed account of the mapping for each characteristic and related study <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">as a multi-sheet excel table</a>, where each sheet shows our mapping alongside our notes which answer options were mapped.</p> <p>For many studies, not all information required for traditional meta-analysis techniques, such as cohort size, is available, preventing the application of these meta-analysis techniques. Therefore, we came up with an agreement metric that equally weights the agreement of the reported ranking and the agreement of the reported percentage values. It combines the relative difference between the reported percentages of both studies and the order of the reported popularities of the answer options. We categorize scores in the range [0.8, 1] as very high agreement, [0.6,0.6[ as high agreement, [0.4, 0.6[ as medium agreement, [0.2, 0.4[ as low agreement, and [0, 0.2[ as very low agreement. We acknowledge that these categories are somewhat arbitrary, however, based on a manual inspection of the results, they do seem to capture the level of agreement between the individual studies quite well. Our replication package includes <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">the mapped data</a> alongside the resulting scores to enable a manual inspection of the degree of agreement. The script that implements the calculation of our score is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/corroboration_analysis.py">publically available</a>. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>scipy</code>. The script does not require any input and assumes that the file <code>Comparison Mappings.xlsx</code> is located in the same folder. The script can be executed as follows:</p> <pre><code>python3 corroboration_analysis.py </code></pre> <p>Further, the script to generate all figures shown in the chapter <em>Comparison Analysis</em> is the final piece of our replication package: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/barcharts.py">link</a>. It uses the python packages <code>numpy</code> and <code>matplotlib</code>. It does not require any input and can be executed as follows:</p> <pre><code>python3 barcharts.py </code></pre> <p>If you have any questions about our study or require any additional information/data please contact the first author.</p>
SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing
<p>This upload contains the software prototype, data, analysis scripts, and replication scripts for the paper "SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing" (ACM/IFIP Middleware 2021).</p> <p>With our artifact we provide the following components:</p> <ul> <li> <p><code>serverless-benchmarks</code> - source code of the benchmark suite</p> </li> <li> <p><code>data</code> - benchmarking results obtained for the paper</p> </li> <li> <p><code>analysis</code> - Python plotting and analysis scripts used for data analysis</p> </li> <li> <p><code>experiments</code> - scripts helping to reproduce the experiments</p> </li> <li> <p><code>docker</code> - compressed Docker images that were used for our experiments.</p> </li> </ul> <p>Our data has been obtained in January 2020, July and August 2020, and November 2020.</p>
Experiments data for "Reference Architecture for serverless Machine Learning"
<p>Data collected during experiments in my bachelor thesis.</p>
JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
Open the record for dataset details and reuse information.
Updated JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
Open the record for dataset details and reuse information.
A comparison of compiler strategies for serverless functions written in Kotlin - dataset
<p>The result from the reaseach one for the thesis paper "A comparison of compiler strategies for serverless functions written in Kotlin".</p>
ScienceDex guides
Understand access before you commit
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.