Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework

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. 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].References:[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.[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, "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.

本数据集为一系列合成任务流图，旨在针对遵循边-枢纽-云（edge/hub/cloud）范式的计算环境，评估面向不同结构与规模应用的最优任务分配方法的性能与可扩展性。
本次研究的系统架构为：边缘设备（例如挂载于无人机（unmanned aerial vehicle, UAV）的单板计算机）与枢纽设备（例如笔记本电脑）进行交互，而枢纽设备进一步与计算能力更强的云服务器通信。
本研究的优化目标为在内存、存储、能耗以及任务前置约束下，最小化系统总延迟或总能耗。同时设定部分任务需固定分配至边缘或枢纽设备。
本研究借助免费任务图生成器（Task Graphs For Free, TGFF），生成了18组任务流图：涵盖并行、串行以及混合（并行与串行结合）三种结构，节点数分别为10、100与1000，且具有不同的入度与出度。
在生成任务流图后，我们通过具有代表性的随机数值补充了各类任务参数，包括执行时长、功耗、内存占用、存储占用以及输出数据规模。
更多细节可参见README.txt文件与参考文献[3]。
参考文献：
[1] R. P. Dick、D. L. Rhodes与W. Wolf，“TGFF：免费任务图生成工具”，发表于第六届软硬件协同设计国际研讨会（CODES/CASHE）论文集，1998年，第97-101页，DOI: 10.1109/HSC.1998.666245。
[2] R. P. Dick、D. L. Rhodes与K. Vallerio，“TGFF”，https://robertdick.org/projects/tgff/。
[3] A. Kouloumpris、G. L. Stavrinides、M. K. Michael与T. Theocharides，“边-枢纽-云范式下的任务分配优化框架”，《未来计算机系统（Future Generation Computer Systems）》，第155卷，第354-366页，2024年6月，DOI: 10.1016/j.future.2024.02.005。