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6
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ShareScore release 0.9.0
Dataset results
6 results for “Function-as-a-Service”
COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms
<p>This dataset release supports the results presented in the paper "COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms" by A. U. Gias and G. Casale, accepted in IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2020.</p> <p>When referring to the dataset please cite the paper above.</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>
Replication Package for "Function-as-a-Service Performance Evaluation: A Multivocal Literature Review"
<p><strong>Paper</strong></p> <p>J. Scheuner and P. Leitner, “Function-as-a-Service Performance Evaluation: A Multivocal Literature Review,” Accepted at the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software">Journal of Systems and Software (JSS)</a>, Preprint: <a href="https://arxiv.org/abs/2004.03276">arXiv:2004.03276</a>.</p> <blockquote> <p>Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies.</p> </blockquote> <pre>@article{scheuner:20-jss, author = {Joel Scheuner and Philipp Leitner}, title = {Function-as-a-Service Performance Evaluation: A Multivocal Literature Review}, journal = {J. Syst. Softw.}, volume = {170}, pages = {110708}, year = {2020}, doi = {10.1016/j.jss.2020.110708} }</pre> <p><strong>Dataset</strong></p> <p>All extracted data originating from academic and grey literature studies are available as machine-readable CSV (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.csv">./data/faas_mlr_raw.csv</a>) and human-readable XLSX (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.xlsx">./data/faas_mlr_raw.xlsx</a>). The Excel file also contains all 700+ comments with guidance, decision rationales, and extra information. It is configured with a filtered view to display only <em>relevant</em> sources but contains the complete data (i.e., including discussion for sources considered to be <em>not relevant</em> in our context).</p> <p>Find further documentation in the README.md or on Github: <a href="https://github.com/joe4dev/faas-performance-mlr/">https://github.com/joe4dev/faas-performance-mlr/</a></p> <p>The latest version is also available online as an interactive Google spreadsheet (GSheet): <a href="https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8">https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8</a></p> <p> </p>
Dataset for a systematic technology review of Function-as-a-Service platforms
<p>Description</p> <p>This dataset comprises the results of analysis of 3 commercial and 7 open source FaaS platforms. The included Excel sheet lists the results grouped into multiple classification categories and dimensions. The data for each platform reside in a separate tab, and for every collected fact there is a corresponding link to the documentation or software repository.</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>
Resources for "Function-as-a-Service Performance Evaluation with Application-level Workflows"
<p><strong>Thesis</strong></p> <p>"Function-as-a-Service Performance Evaluation with Application-level Workflows"</p> <p><strong>Dataset</strong></p> <p>All extracted data from the benchmark runs in each CSP are available as machine-readable CSV.</p>
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