2024-03-29T14:17:52Z
https://zenodo.org/oai2d
oai:zenodo.org:8430233
2023-10-11T09:06:21Z
user-splab
software
Spillner, Josef
2023-10-11
<p>This is an exemplary command-line client for using the Calcite framework to parse SQL instructions and queries and hold resulting data structures in memory. The client's capabilities include the creation of tables and views, loading user-defined<br>
functions (scalar UDFs, UDAFs, UDTFs), populating tables, and running queries. The client can in particular toggle between JDBC-proxied and direct Calcite-internal object access according to a defined interface type; although JDBC offers limited insight into the query execution plan.<br>
</p>
https://doi.org/10.5281/zenodo.8430233
oai:zenodo.org:8430233
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.8430232
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
query optimisation
query placement
'Databaseless' Calcite Client
info:eu-repo/semantics/other
oai:zenodo.org:5650816
2021-11-06T13:48:39Z
user-serverless
user-splab
software
Spillner, Josef
2021-11-06
<p>This is the code + experiments/reproducibility repository for the paper: «Self-balancing Architectures based on Liquid Functions across Computing Continuums» presented at the DML-ICC workshop at the 14th IEEE/ACM UCC 2021.<br>
<br>
It is in *very* rough form, though.<br>
</p>
https://doi.org/10.5281/zenodo.5650816
oai:zenodo.org:5650816
eng
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.5650815
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
continuum computing
cloud functions
distributed systems
serverless computing
CoRFu: Continuum-Ready Functions
info:eu-repo/semantics/other
oai:zenodo.org:7234227
2022-10-21T14:26:53Z
user-splab
software
Spillner, Josef
2022-10-21
<p>This is a first snapshot of tools helping in precomputing connections between stops within one public transport network or even across networks.<br>
<br>
For the respective documentation and execution instructions, consult the more detailed README files in the subfolders.<br>
</p>
https://doi.org/10.5281/zenodo.7234227
oai:zenodo.org:7234227
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.7234226
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
digital mobility
gtfs
connection inquiry
ZPIT - Zurich Precomputed Inquiry of Timetables
info:eu-repo/semantics/other
oai:zenodo.org:4095501
2020-10-18T00:27:25Z
user-serverless
user-splab
software
Spillner, Josef
Boruta, Daiana
2020-10-16
<p>This is a software and dataset snapshot of https://github.com/serviceprototypinglab/lambda-docker-measurements, accompanying the system demonstration «Memory Autotuning for Cloud Functions» presented at ESSCA @ XP 2020, and updated for generating the graphs for the presentation of «Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning» at WoSC6 @ Middleware 2020.</p>
Minor changes compared to the previous version. These scripts have been used as basis for the measurements and profiling/autotuning works published in https://zenodo.org/record/4095481.
https://doi.org/10.5281/zenodo.4095501
oai:zenodo.org:4095501
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.3911303
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
WoSC6, Sixth International Workshop on Serverless Computing 2020
docker
memory tracing
plotting
cost estimation
Measurements of time and memory costs for docker containers in the AWS Lambda pricing model
info:eu-repo/semantics/other
oai:zenodo.org:4584221
2021-03-05T12:27:22Z
user-splab
software
Hass, Daniel
Spillner, Josef
2021-03-05
<p>This code and data archive contains:<br>
<br>
1. A snapshot of version 1.0.0 of the 'paper-m2ec2021' branch of the Continuum<br>
Deployer. Further development is expected to happen here:<br>
https://github.com/serviceprototypinglab/continuum-deployer<br>
<br>
2. Experiment scripts, including reference results, for reproducibility of the<br>
numbers and figures in the M2EC/AINA 2021 paper 'Interactive Application<br>
Deployment Planning for Heterogeneous Computing Continuums'.</p>
https://doi.org/10.5281/zenodo.4584221
oai:zenodo.org:4584221
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4584220
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
AINA, 35th International Conference on Advanced Information Networking and Applications, Toronto, Canada, May 2021
deployment
matchmaking
placement
continuum computing
Interactive Application Deployment Planning for Heterogeneous Computing Continuums
info:eu-repo/semantics/other
oai:zenodo.org:7919472
2023-05-10T14:26:57Z
user-splab
software
Spillner, Josef
2023-05-10
<p>This folder contains SLASH, the research prototype implementation of<br>
Serverless Apache Spark Hub.<br>
<br>
To make it run, install the dependencies for OpenStack access, e.g.:<br>
% sudo apt-get install python3-novaclient<br>
Then, proceed with the script run.sh in the deploy folder.<br>
Finally, use 'import slash' in your Spark Python scripts or Jupyter<br>
notebooks.<br>
<br>
SLASH consists of three components: The Python module 'slash' which<br>
augments 'pyspark' magically with reactive autoscaling capabilities, the<br>
'slashhub' which collects on-demand scaling actions to calculate<br>
reactive autoscaling in conjunction with predictive ones, and the<br>
'slashjobserver' which executes the calendar scheduling and forecasting.<br>
<br>
A calendar file is expected as 'calendar' with dates or date ranges per<br>
line. Furthermore, the functionality is determined by environment<br>
variables: OS_* (see openstackclient.py for a list) and SLASH pointing<br>
'slash' to the node running 'slashhub' on port 11111.</p>
https://doi.org/10.5281/zenodo.7919472
oai:zenodo.org:7919472
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.7897069
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SLASH - Serverless Apache Spark Hub
info:eu-repo/semantics/other
oai:zenodo.org:1311795
2020-01-24T19:24:48Z
user-splab
openaire_data
Serhiienko, Oleksii
Spillner, Josef
2018-07-13
<p>This repository contains exemplary results from using the CMP² (Comparing Cloud Management Platforms) testbed on CloudcheckR, ManageIQ, MistIO, Boto and Libcloud. The results give insight into the performance of multi-cloud middleware. All experiments were conducted as research in education linked to the Cloud Accounting and Billing research initiative at Service Prototyping Lab, Zurich University of Applied Sciences, Switzerland. Apart from the raw data in JSON format, generated graphs are also included.</p>
<p> </p>
https://doi.org/10.5281/zenodo.1311795
oai:zenodo.org:1311795
eng
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.1311794
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cloud accounting
multi-cloud
cloud management
Cloud management platform evaluation data generated by CMP²
info:eu-repo/semantics/other
oai:zenodo.org:4124659
2020-10-26T12:26:59Z
user-splab
openaire_data
Spillner, Josef
2020-10-24
<p>Machine-readable dataset to determine the best compression settings for a given goal such as "fastest compression" or "fastest search over compressed data". Graph model (JSON + auto-derived Dot format) along with CSV measurement results of 13 compression tools and 30 tool-configuration-search combinations.</p>
<p>Associated publication: Josef Spillner, «Comparison and Model of Compression Techniques for Smart Cloud Log File Handling», CCCI 2020.</p>
<p>This dataset also contains a snapshot of the associated software prototypes.<br>
</p>
https://doi.org/10.5281/zenodo.4124659
oai:zenodo.org:4124659
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4053734
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CompressGraph: Knowledge about compression algorithms, tools, formats, ratios and runtime behaviour
info:eu-repo/semantics/other
oai:zenodo.org:4547809
2021-02-18T12:27:25Z
user-splab
software
Spillner, Josef
2021-02-18
<p>EPOSS - Educational programming and observation support structure<br>
-----------------------------------------------------------------<br>
<br>
With EPOSS, students and lecturers are supported in programming physical<br>
'things' in different education settings - offline, hybrid or online.<br>
The EPOSS architecture allows for explicitly programming movement<br>
commands or autoprogramming (injecting) replayed movements into moving<br>
'things' and observing the effects.<br>
<br>
Although conceptually generic, the EPOSS implementation is currently<br>
focused on<br>
(i) EV3 robots as 'things'<br>
(ii) education in traffic engineering with appropriate scenarios<br>
(iii) Linux as educator operating system; students can use any system<br>
<br>
How to start:<br>
- read the CSEDU'21 paper to get all background information<br>
- look at the architecture diagram<br>
- prepare environment by following instructions in rpycloudsetup.txt<br>
- boot up a 'thing' and make it accessible on the network<br>
- optional: add a corresponding hostname to /etc/hosts<br>
- adjust the hostname/address in localstart.sh and run the script<br>
- try out the manoevres after substituting rpyc with rpycloud<br>
</p>
https://doi.org/10.5281/zenodo.4547809
oai:zenodo.org:4547809
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4547808
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
online teaching
cyber-physical system
programming
cloud robotics
Educational programming and observation support structure
info:eu-repo/semantics/other
oai:zenodo.org:3517806
2020-01-24T19:24:39Z
user-splab
openaire_data
Spillner, Josef
Boruta, Daiana
2019-10-24
<p>Systematic database of literature around Kubernetes and closely related topics. Retrieved through keyword search from DBLP and manual additions. Covering 2017-2019. Insights into technologies and trends. Evolving through open community curation.</p>
https://doi.org/10.5281/zenodo.3517806
oai:zenodo.org:3517806
eng
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.3517805
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
kubernetes
container platform
Kubernetes Literature Dataset
info:eu-repo/semantics/other
oai:zenodo.org:4531795
2021-04-15T17:32:06Z
user-splab
openaire_data
Gkikopoulos, Panagiotis
Schiavoni, Valerio
Spillner, Josef
2021-02-10
<p>Docker images are commonly used to package, distribute and deploy complex cloud-native applications in containerised form. A container engine executes these applications with separated privileges according to namespaces. Recent studies have investigated security vulnerabilities and runtime characteristics of Docker images. <br>
In contrast, little is known about the extent of hardware-dependent features in these images such as processor-specific trusted execution environments, graphics acceleration or extension boards. <br>
This problem can be generalised to missing knowledge about the extent of any hardware-specific instructions within the images that may require elevated privileges.<br>
In this study, we contribute to increasing this knowledge by a systematic long-term analysis of a sample of Docker images concerning their use of hardware-specific features, including those for virtualisation, acceleration and security. <br>
We contribute a Docker registry metadata collector along with augmented metadata covering one-year long of top (i.e., official images and those from the same developers) Docker Hub images, also releasing those as open dataset. <br>
Moreover, we provide a heuristic hardware dependency analysis framework and a hardware-aware Docker executor that gives early warnings upon missing hardware dependencies instead of leading to silent or untimely failures. <br>
We demonstrate the usefulness of our work for heterogeneous cloud and fog computing environments.</p>
https://doi.org/10.5281/zenodo.4531795
oai:zenodo.org:4531795
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4531794
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Heterogeneous Hardware Support in Docker Images
info:eu-repo/semantics/other
oai:zenodo.org:8069916
2023-06-25T14:27:10Z
user-splab
software
Panagiotis Gkikopoulos
2023-06-22
<p>VDX is a generic vote definition specification. It aims to be a simple way to define the behavior of voting software.</p>
https://doi.org/10.5281/zenodo.8069916
oai:zenodo.org:8069916
Zenodo
https://github.com/EcePanos/vdx
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.8069915
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
voting
VDX: Voting Definition eXtended
info:eu-repo/semantics/other
oai:zenodo.org:10453208
2024-01-03T06:50:42Z
user-splab
Gkikopoulos, Panagiotis
Spillner, Josef
Sakman, Cihan
Ojha, Ranjan
2024-01-03
<p>This public tabular dataset along with associated Jupyter notebooks provides aggregated measurements of sensors (light/noise/motion, smart camera-based counter, bluetooth device presence) in university rooms, and the ability to inquire about the likelihood of occupancy at a desired reservation time. It is meant to contribute to smarter room reservation systems based on room atmosphere models, and therefore to the digitalisation in facility management.</p>
https://doi.org/10.5281/zenodo.10453208
oai:zenodo.org:10453208
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.10453207
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Roomatch Model - Increasing building utilisation by smart room finding and matchmaking
info:eu-repo/semantics/other
oai:zenodo.org:4550471
2021-04-15T17:32:08Z
user-splab
openaire_data
Gkikopoulos, Panagiotis
Schiavoni, Valerio
Spillner, Josef
2021-02-19
<p>Docker images are used to distribute and deploy cloud-native applications in containerised form. A container engine runs them with separated privileges according to namespaces. Recent studies have investigated security vulnerabilities and runtime characteristics of Docker images. In contrast, little is known about the extent of hardware-dependent features in them such as processor-specific trusted execution environments, graphics acceleration or extension boards. This problem can be generalised to missing knowledge about the extent of any hardware-bound instructions within the images that may require elevated privileges.<br>
We first conduct a systematic one-year evolution analysis of a sample of Docker images concerning their use of hardware-specific features. To improve the state of technology, we contribute novel tools to manage such images. Our heuristic hardware dependency detector and a hardware-aware Docker executor <em>hdocker</em> give early warnings upon missing dependencies instead of leading to silent or untimely failures. Our dataset and tools are released to the research community.</p>
<p><em>Accompanying paper: P. Gkikopoulos, V. Schiavoni, J. Spillner, «Analysis and Improvement of Heterogeneous Hardware Support in Docker Images», 21st International Conference on Distributed Applications and Interoperable Systems (DAIS 2021).</em></p>
https://doi.org/10.5281/zenodo.4550471
oai:zenodo.org:4550471
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4531794
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Heterogeneous Hardware Support in Docker Images
info:eu-repo/semantics/other
oai:zenodo.org:1252820
2020-01-24T19:24:30Z
user-serverless
user-splab
openaire_data
Leitner, Philipp
Wittern, Erik
Spillner, Josef
Hummer, Waldemar
2018-05-25
<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>
The study is currently under review and not yet published. We have uploaded a preprint to PeerJ: https://peerj.com/articles/?q=function-as-a-service
https://doi.org/10.5281/zenodo.1252820
oai:zenodo.org:1252820
eng
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1252819
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
serverless computing
faas
software engineering
software development
empirical research
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
info:eu-repo/semantics/other
oai:zenodo.org:10723056
2024-02-28T14:55:17Z
user-splab
software
Gkikopoulos, Panagiotis
Spillner, Josef
Schiavoni, Valerio
2024-02-28
<p>This repository contains the code and data in order to reproduce the results, including figures, of the article: <a href="https://ieeexplore.ieee.org/document/9681269">Decentralised Data Quality Control in Ground Truth Production for Autonomic Decisions</a>, by P. Gkikopoulos (Zurich University of Applied Sciences and University of Neuchâtel), J. Spillner (Zurich University of Applied Sciences) and V. Schiavoni (University of Neuchâtel), published in IEEE TPDS - Transactions on Parallel and Distributed Systems, vol. 3, issue 10, October 2022. In particular, it contains a reference implementation for the Data-Centric Consensus (DCC) protocol that aims to raise confidence in data-driven decision making as well as data-centric AI.</p>
<p>The code is collaboratively maintained through Git by Zurich University of Applied Sciences and University of Neuchâtel, with future improvements to the voting/DCC algorithm expected, while the snapshot relevant for the article is also long-term archived on Zenodo.</p>
<p>The archived version on Zenodo is available at: <a href="https://doi.org/10.5281/zenodo.5835875">https://doi.org/10.5281/zenodo.5835875</a></p>
<p>The collaboratively maintained code repository is at: <a href="https://github.com/serviceprototypinglab/dcc">https://github.com/serviceprototypinglab/dcc</a></p>
<p><strong>Abstract</strong></p>
<p>Autonomic decision-making based on rules and metrics is inevitably on the rise in distributed software systems. Often, the metrics are acquired from system observations such as static checks and runtime traces. To avoid bias propagation and hence reduce wrong decisions in increasingly autonomous systems due to poor observation data quality, multiple independent observers can exchange their findings and produce a majority-accepted, complete and outlier-cleaned ground truth in the form of consensus-supported metrics.</p>
<p>This repository contains the code, data and scripts to produce ground truth with data-centric consensus voting for more reliable decision making processes. It can be used to computationally reproduce the two key results, the multi-plot figures 6 and 7 of the TPDS article. While the article puts these results into a broader context, this repository contains all technical information to verify the correctness and performance of the proposed and implemented algorithms.</p>
<p><strong>Artifacts Overview</strong></p>
<p>All artifact code is contained in the following two independent directories, each of which contains a detailed sub-README file for further information:</p>
<ul>
<li>Performance evaluation (directory performance_evaluation): Includes the prototype DCC tool and associated packaging and documentation used in our performance evaluation. It also contains a reference to the test dataset that can be used to measure performance, and to a synthetic dataset generator. This generator approximates the infrastructure used to obtain the performance data presented in the paper (Fig. 6). The performance evaluation is conducted in containerised form.</li>
<li>Error injection experiment (directory error_injection_experiment): Includes a standalone script that compares 3 voting algorithms in different error injection configurations. A plotting script used to plot the results and documentation is included (Fig. 7). The error injection runs through a Python virtual environment without containers.</li>
</ul>
<p>Hence, Fig. 7 can be precisely reproduced, whereas Fig. 6 can be approximately reproduced. The differences for Fig. 6 are the absence of networked invocations (which require a custom infrastructure setup), the omission of averaging across 10-fold computation (to save compute time), and the 'natural' performance differences due to different CPU architectures.</p>
<p><strong>Requirements and Dependencies</strong></p>
<p>In total, around 1 GB of disk space is needed to set up the container image and the Python virtual environment. The additional capacity required by data and experiments is negligible. The experiments can execute on a researcher notebook or workstation; any single-core machine with approximately 3 GHz core and at least 4 GB of memory should be fine. All experiments run local, whereas a network connection is required for the initial setup.</p>
<p>The assumption is that Linux environment from around 2020-23 is used. The code has been tested in particular on Ubuntu 20.10 (groovy; although the containerised performance experiment uses an older base image) and on Debian 12 (bookworm). All Python packages are installed through Pip so that only few system packages are required (python3-pip python3-virtualenv docker.io).</p>
<p>Ensure that the python command is available in the system path and points to python3. On Ubuntu, this can be achieved by installing the 'python-is-python3' package.</p>
<p>Other than the software setup, no further dependencies exist. All required data files are included or synthetically generated. However, for the full experiment as described in the paper, a networked environment with distributed Git repositories across SSH accounts would have to be set up manually.</p>
<p><strong>Setup and Cleanup</strong></p>
<p>The setup of the experiments is explained for both parts separately in the sub-README files. If the reproduction script mentioned below is used, no separate setup needs to be conducted. Ensure that the requirements especially concerning disk space are fulfilled and just run `./reproduce-article-figures.sh`!</p>
<p>The generated CSV files, PDF plots, Docker image and Python virtual environment directory can be safely removed after conducting the experiment. Detailed instructions are given in the sub-README files. The reproduction script automates the cleanup as well.</p>
<p><strong>Results Reproduction</strong></p>
<p>To reproduce the TPDS article figures, a convenience script is provided: `reproduce-article-figures.sh`. It combined both artifacts, performance evaluation and error injection, and ensures the setup and cleanup are fully automated. Just running this script (and waiting around 10 minutes) is sufficient to obtain two PDF files which correspond to figures 6 and 7 of the published article. The script is mostly self-explaining but also contains some inline documentation on what it does. Eventually, two files are produced: `fig6-plot.pdf` and `fig7-plot.pdf`.</p>
https://doi.org/10.5281/zenodo.10723056
oai:zenodo.org:10723056
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.5835875
info:eu-repo/semantics/openAccess
Apache License 2.0
http://www.apache.org/licenses/LICENSE-2.0
IEEE Transactions on Parallel and Distributed Systems, (2024-02-28)
consensus
data quality
observation
Data-Centric Consensus code artifacts
info:eu-repo/semantics/other
oai:zenodo.org:10444934
2023-12-30T15:34:44Z
user-splab
software
Gkikopoulos, Panagiotis
Spillner, Josef
Schiavoni, Valerio
2023-12-30
<p>This repository contains the code and data in order to reproduce the results, including figures, of the article: <a href="https://ieeexplore.ieee.org/document/9681269">Decentralised Data Quality Control in Ground Truth Production for Autonomic Decisions</a>, by P. Gkikopoulos (Zurich University of Applied Sciences and University of Neuchâtel), J. Spillner (Zurich University of Applied Sciences) and V. Schiavoni (University of Neuchâtel), published in IEEE TPDS - Transactions on Parallel and Distributed Systems, vol. 3, issue 10, October 2022. In particular, it contains a reference implementation for the Data-Centric Consensus (DCC) protocol that aims to raise confidence in data-driven decision making as well as data-centric AI.</p>
<p>The code is collaboratively maintained through Git by Zurich University of Applied Sciences and University of Neuchâtel, with future improvements to the voting/DCC algorithm expected, while the snapshot relevant for the article is also long-term archived on Zenodo.</p>
<p>The archived version on Zenodo is available at: <a href="https://doi.org/10.5281/zenodo.5835875">https://doi.org/10.5281/zenodo.5835875</a></p>
<p>The collaboratively maintained code repository is at: <a href="https://github.com/serviceprototypinglab/dcc">https://github.com/serviceprototypinglab/dcc</a></p>
<p><strong>Abstract</strong></p>
<p>Autonomic decision-making based on rules and metrics is inevitably on the rise in distributed software systems. Often, the metrics are acquired from system observations such as static checks and runtime traces. To avoid bias propagation and hence reduce wrong decisions in increasingly autonomous systems due to poor observation data quality, multiple independent observers can exchange their findings and produce a majority-accepted, complete and outlier-cleaned ground truth in the form of consensus-supported metrics.</p>
<p>This repository contains the code, data and scripts to produce ground truth with data-centric consensus voting for more reliable decision making processes. It can be used to computationally reproduce the two key results, the multi-plot figures 6 and 7 of the TPDS article. While the article puts these results into a broader context, this repository contains all technical information to verify the correctness and performance of the proposed and implemented algorithms.</p>
https://doi.org/10.5281/zenodo.10444934
oai:zenodo.org:10444934
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.5835875
info:eu-repo/semantics/openAccess
Apache License 2.0
http://www.apache.org/licenses/LICENSE-2.0
IEEE Transactions on Parallel and Distributed Systems, (2023-12-30)
consensus
data quality
observation
Data-Centric Consensus code artifacts
info:eu-repo/semantics/other
oai:zenodo.org:1436432
2020-01-24T19:26:14Z
user-serverless
user-splab
openaire_data
Spillner, Josef
Al-Ameen, Mohammed
2018-09-27
<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. Covering 2016-most of 2018. Evolving through open community curation.</p>
2nd generation dataset, ODP converted to JSON files.
https://doi.org/10.5281/zenodo.1436432
oai:zenodo.org:1436432
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1175423
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
faas
cloud function
lambda
serverless
Serverless Literature Dataset
info:eu-repo/semantics/other
oai:zenodo.org:3911304
2020-10-16T15:20:11Z
user-serverless
user-splab
software
Spillner, Josef
Boruta, Daiana
2020-06-27
<p>This is a software and dataset snapshot of https://github.com/serviceprototypinglab/lambda-docker-measurements, accompanying the system demonstration «Memory Autotuning for Cloud Functions» presented at ESSCA @ XP 2020.</p>
https://doi.org/10.5281/zenodo.3911304
oai:zenodo.org:3911304
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.3911303
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Measurements of time and memory costs for docker containers in the AWS Lambda pricing model
info:eu-repo/semantics/other
oai:zenodo.org:4095481
2020-10-18T00:27:23Z
user-serverless
user-splab
openaire_data
Spillner, Josef
2020-10-16
<p>This dataset contains CSV files with the raw measurements of 4x10 traces as well as scripts and reference data for distinguishing two usage profiles based on raw measurements of 2x20 traces. It accompanies the publication «Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning» presented at WoSC6 @ Middleware 2020.</p>
Reproducing the data requires the scripts published at https://doi.org/10.5281/zenodo.3911303.
https://doi.org/10.5281/zenodo.4095481
oai:zenodo.org:4095481
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.4095480
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
WoSC6, Sixth International Workshop on Serverless Computing 2020
cloud functions
docker
memory tuning
Memory tracing, profiling and autotuning for containerised cloud functions
info:eu-repo/semantics/other
oai:zenodo.org:7859411
2023-04-25T02:26:53Z
user-splab
software
Sakman, Mehmet Cihan
Gkikopoulos, Panagiotis
Martella, Francesco
Villari, Massimo
Spillner, Josef
2023-04-24
<p>This repository contains the proof-of-concept code for the ICSBT 2023 paper 'Indoor Navigation for Personalised Shopping: A Real-Tech Feasibility Study'. In particular, it contains the web prototype for the smart shopping customer perspective.</p>
<p>As outlined in the paper, the prototype will unlikely be useful unless the corresponding cloud infrastructure to connect to ESLs, beacons etc. is present. In other words, it is not supposed to work out of the box, but merely provided for research transparency.</p>
https://doi.org/10.5281/zenodo.7859411
oai:zenodo.org:7859411
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.7859410
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
retail
smart shopping
IoT
Smart personalised shopping web prototype
info:eu-repo/semantics/other
oai:zenodo.org:7869751
2023-04-27T14:26:44Z
user-splab
software
Cvetkovski, Oliver
Field, Carlo
Trinchi, Davide
Spillner, Josef
2023-04-27
<p>This software represents a highly scalable approach with microservices for pandemic management: user registration, appointments for tests and vaccinations, QR code generation and verification, and so forth. It is not intended to be used for actual pandemic management, but rather to study how future pandemic management can be realised at nation-scale.</p>
https://doi.org/10.5281/zenodo.7869751
oai:zenodo.org:7869751
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.7869750
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
pandemic management
e-government
microservices
nation-scale applications
ZVAX - Zurich vaccination and pandemic management software
info:eu-repo/semantics/other
oai:zenodo.org:7288372
2023-12-30T15:37:13Z
user-splab
software
Gkikopoulos, Panagiotis
Spillner, Josef
Schiavoni, Valerio
2022-11-04
<p>This repository contains the code and data in order to reproduce the results, including figures, of the article: Decentralised Data Quality Control in Ground Truth Production for Autonomic Decisions, by P. Gkikopoulos (Zurich University of Applied Sciences and University of Neuchâtel), J. Spillner (Zurich University of Applied Sciences) and V. Schiavoni (University of Neuchâtel), accepted for publication at IEEE TPDS - Transactions on Parallel and Distributed Systems, 2022. In particular, it contains a reference implementation for the Data-Centric Consensus (DCC) protocol that aims to raise confidence in data-driven decision making as well as data-centric AI.</p>
<p>The code is maintained through Git by Zurich University of Applied Sciences and University of Neuchâtel, with future improvements to the voting/DCC algorithm expected, while the snapshot relevant for the article is also long-term archived on Zenodo.</p>
<p>The archived version on Zenodo is available at: <a href="https://doi.org/10.5281/zenodo.5835875">https://doi.org/10.5281/zenodo.5835875</a> The collaboratively maintained code repository is at: <a href="https://github.com/serviceprototypinglab/dcc">https://github.com/serviceprototypinglab/dcc</a></p>
<p>All results are contained in the following two directories:</p>
<ul>
<li>
<p>Performance evaluation: Includes the prototype DCC tool and associated documentation used in our performance evaluation and reference to the test dataset that can be used to measure performance. This dataset, was used to obtain the performance data presented in the papaer.</p>
</li>
<li>
<p>Error injection experiment: Includes a standalone script that compares 3 voting algorithms in different error injection configurations. A plotting script used to plot the results and documentation is included.</p>
</li>
</ul>
https://doi.org/10.5281/zenodo.7288372
oai:zenodo.org:7288372
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.5835875
info:eu-repo/semantics/openAccess
Apache License 2.0
http://www.apache.org/licenses/LICENSE-2.0
IEEE Transactions on Parallel and Distributed Systems, (2022-11-04)
consensus
data quality
observation
Data-Centric Consensus code artifacts
info:eu-repo/semantics/other
oai:zenodo.org:5835876
2022-11-04T12:32:11Z
user-splab
software
Gkikopoulos, Panagiotis
Spillner, Josef
Schiavoni, Valerio
2022-01-11
<p>This repository contains the code and data in order to reproduce the results, including figures, of the article: Decentralised Data Quality Control in Ground Truth Production for Autonomic Decisions, by P. Gkikopoulos, J. Spillner and V. Schiavoni, accepted for publication at IEEE TPDS - Transactions on Parallel and Distributed Systems, 2022. In particular, it contains a reference implementation for the Data-Centric Consensus (DCC) protocol that aims to raise confidence in data-driven decision making as well as data-centric AI.</p>
<p>The code is maintained through Git by Zurich University of Applied Sciences and University of Neuchâtel, with future improvements to the voting/DCC algorithm expected, while the snapshot relevant for the article is also long-term archived on Zenodo.</p>
<p>All results are contained in the following two directories:</p>
<ul>
<li>
<p>Performance evaluation: Includes the prototype DCC tool and associated documentation used in our performance evaluation and reference to a test dataset that can be used to measure performance.</p>
</li>
<li>
<p>Error injection experiment: Includes a standalone script that compares 3 voting algorithms in different error injection configurations. A plotting script used to plot the results and documentation is included.</p>
</li>
</ul>
https://doi.org/10.5281/zenodo.5835876
oai:zenodo.org:5835876
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.5835875
info:eu-repo/semantics/openAccess
Apache License 2.0
http://www.apache.org/licenses/LICENSE-2.0
IEEE Transactions on Parallel and Distributed Systems, (2022-01-11)
consensus
data quality
observation
Data-Centric Consensus code artifacts
info:eu-repo/semantics/other
oai:zenodo.org:2649001
2020-01-24T19:26:12Z
user-serverless
user-splab
openaire_data
Spillner, Josef
Al-Ameen, Mohammed
2019-04-23
<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>
3rd generation dataset, described in blog post: https://blog.zhaw.ch/splab/2019/04/22/the-5th-year-of-serverless-computing-research-coverage/ + website http://serverless.research-output.org/.
https://doi.org/10.5281/zenodo.2649001
oai:zenodo.org:2649001
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1175423
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
faas
cloud function
lambda
serverless
Serverless Literature Dataset
info:eu-repo/semantics/other
oai:zenodo.org:3517819
2020-01-24T19:26:12Z
user-serverless
user-splab
openaire_data
Spillner, Josef
Al-Ameen, Mohammed
Boruta, Daiana
2019-10-23
<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>
4th generation dataset. Website: http://serverless.research-output.org/ + See older blog post: https://blog.zhaw.ch/splab/2019/04/22/the-5th-year-of-serverless-computing-research-coverage/
https://doi.org/10.5281/zenodo.3517819
oai:zenodo.org:3517819
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1175423
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
faas
cloud function
lambda
serverless
Serverless Literature Dataset
info:eu-repo/semantics/other
oai:zenodo.org:7897070
2023-05-10T09:18:24Z
user-splab
software
Spillner, Josef
2023-05-04
<p>This folder contains SLASH, the research prototype implementation of<br>
Serverless Apache Spark Hub.</p>
<p>To make it run, install the dependencies for OpenStack access, e.g.:<br>
% sudo apt-get install python3-novaclient</p>
<p>SLASH consists of three components: The Python module 'slash' which<br>
augments 'pyspark' magically with reactive autoscaling capabilities, the<br>
'slashhub' which collects on-demand scaling actions to calculate<br>
reactive autoscaling in conjunction with predictive ones, and the<br>
'slashjobserver' which executes the calendar scheduling and forecasting.</p>
<p>A calendar file is expected as 'calendar' with dates or date ranges per<br>
line. Furthermore, the functionality is determined by environment<br>
variables: OS_* (see openstackclient.py for a list) and SLASH pointing<br>
'slash' to the node running 'slashhub' on port 11111.</p>
https://doi.org/10.5281/zenodo.7897070
oai:zenodo.org:7897070
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.7897069
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SLASH - Serverless Apache Spark Hub
info:eu-repo/semantics/other
oai:zenodo.org:3382127
2020-01-24T19:23:58Z
user-splab
openaire_data
Ilham Qasse
Josef Spillner
2019-08-30
<p>This dataset contains metrics and statistical information on decentralised applications (DApps) and associated smart contracts on blockchains.</p>
<p>The dataset was produced by the 'DApps-Scraping' scripts which are on Github.</p>
<p>The dataset is currently small. As experiments are ongoing, it will be updated soon.</p>
<p>The work was supported by a young scientist mobility grant of the Swiss Leading House MENA.</p>
https://doi.org/10.5281/zenodo.3382127
oai:zenodo.org:3382127
eng
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.3382126
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
blockchain
decentralised application
quality
smart contract
DApps Quality Characteristics Dataset
info:eu-repo/semantics/other
oai:zenodo.org:1175424
2020-01-24T19:26:14Z
user-serverless
user-splab
openaire_data
Spillner, Josef
2018-02-19
<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. Covering 2016-2017.</p>
https://doi.org/10.5281/zenodo.1175424
oai:zenodo.org:1175424
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1175423
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
faas
cloud function
lambda
serverless
Serverless Literature Dataset
info:eu-repo/semantics/other
oai:zenodo.org:4053735
2020-10-24T04:01:44Z
user-splab
openaire_data
Spillner, Josef
2020-09-27
<p>Machine-readable dataset to determine the best compression settings for a given goal such as "fastest compression" or "fastest search over compressed data". Graph model (JSON + auto-derived Dot format) along with CSV measurement results of 13 compression tools and 30 tool-configuration-search combinations.</p>
<p>Associated publication: Josef Spillner, «Comparison and Model of Compression Techniques for Smart Cloud Log File Handling», CCCI 2020</p>
https://doi.org/10.5281/zenodo.4053735
oai:zenodo.org:4053735
Zenodo
https://zenodo.org/communities/splab
https://doi.org/10.5281/zenodo.4053734
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CompressGraph: Knowledge about compression algorithms, tools, formats, ratios and runtime behaviour
info:eu-repo/semantics/other
oai:zenodo.org:1236763
2020-01-24T19:25:57Z
user-serverless
user-splab
openaire_data
Spillner, Josef
2018-04-29
<p>YAML-formatted and timestamped description of Function-as-a-Service (FaaS) service characteristics and constraints such as maximum execution time and pricing. This dataset allows for adaptive software and workflow generation in dynamically evolving Serverless Computing environments. We envision the inclusion of the dataset into code generators, code transformers, workflow schedulers and compatibility modes of open source FaaS runtimes.</p>
<p>Furthermore, due to evidences of evolving values being given by hyperlinks, the dataset will serve as single source of truth about the technological development in the FaaS space.</p>
https://doi.org/10.5281/zenodo.1236763
oai:zenodo.org:1236763
Zenodo
https://zenodo.org/communities/splab
https://zenodo.org/communities/serverless
https://doi.org/10.5281/zenodo.1236762
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
faas
cloud function
serverless
lambda
FaaS Characteristics and Constraints Knowledge Base
info:eu-repo/semantics/other