These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints. We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), 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, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].References:[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245. [2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/. [3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.

These datasets are released under the 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.11031941