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12 results for “task allocation”
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Data_Two sources of task prioritization: The interplay of effector-based and task order-based capacity allocation in the PRP paradigm
<p>Data of 'Two sources of task prioritization: The interplay of effector-based and task order-based capacity allocation in the PRP paradigm' Hoffmann, Pieczykolan, Koch, & Huestegge, comparing RT data and error rates of oculomotor, vocal, and manual responses in a PRP setting</p>
Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework
<p>These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.<br> <br>We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].<br> <br>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.<br> <br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.<br>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.</p>
Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".
<p>This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper:</p> <p>Ponti, M., Seredko, A. <a href="http://doi.org/10.1057/s41599-022-01049-z">Human-machine-learning integration and task allocation in citizen science.</a> <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z</p> <p>The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.</p> <p> </p> <p> </p>
Multi-agent multi-mode composite task allocation problem (MACTA) instances
<p><strong>Contents</strong></p> <p>This repository contains all the instances of Multi-agent multi-mode composite task allocation problem (MACTA) used in the AAMAS paper [Picard, 2023].</p> <p>Instances are provided as json files (one per instance), following the provided schema (see <code>macta_schema.json</code>). Files are contained in subdirectories following the <code><n>_modes/<m>_requests_per_user/instance_<i>.json</code> pattern, where <code><n></code> is the number of modes per requests (1 or 5), <code><m></code> is the number of requests per user (between 2 and 30 by step of 2), and <code><i></code> is the instance number.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed with the support of the French government in the context of the "Programme d'Invertissements d'Avenir", namely by the BPI PSPC LiChIE project (Lion Chaine Image Elargie), coordinated by Airbus Defence and Space.</p> <p><strong>References</strong></p> <p>Picard, G. (2023). Multi-agent consensus-based bundle allocation for multi-mode composite tasks. In <em>International Conference on Autonomous Agents and Multiagent Systems (AAMAS-23)</em>. IFAAMAS.</p>
Rysunki dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains the figures for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Wyniki symulacji dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains simulation results for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
APPENDIX A: Allocated Task During first, final Meet-up session and in-between those session
<p>Allocated Task During first, final Meet-up session and in-between those session</p>
Rysunki dla Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints
<p>This resource contains the figures for Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints</p>
The Effect of Prioritization of Attentional Allocation on Postural-suprapostural Tasking
ClinicalTrials.gov study NCT02206347. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Molecular mechanisms of task allocation in workers of the red imported fire ant, Solenopsis invicta
GEO Series GSE229201. Solenopsis invicta. 12 samples. Type: Expression profiling by high throughput sequencing.
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