Tocze, K., Schmitt, N., Kargén, U., Aral, A., & Brandic, I. (2022). Edge Workload Trace Gathering and Analysis for Benchmarking. In 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC) (pp. 34–41). IEEE. https://doi.org/10.1109/ICFEC54809.2022.00012
The emerging field of edge computing is suffering from a lack of representative data to evaluate rapidly introduced new algorithms or techniques. That is a critical issue as this complex paradigm has numerous different use cases which translate into a highly diverse set of workload types.In this work, within the context of the edge computing activity of SPEC RG Cloud, we continue working towards an edge benchmark by defining high-level workload classes as well as collecting and analyzing traces for three real-world edge applications, which, according to the existing literature, are the representatives of those classes. Moreover, we propose a practical and generic methodology for workload definition and gathering. The traces and gathering tool are provided open-source.In the analysis of the collected workloads, we detect discrepancies between the literature and the traces obtained, thus highlighting the need for a continuing effort into gathering and providing data from real applications, which can be done using the proposed trace gathering methodology. Additionally, we discuss various insights and future directions that rise to the surface through our analysis.
Laufzeitkontrolle in Multi-Clouds: Y 904-N31 (FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF)) Nachhaltige Wasserwirtschaft durch IoT-gesteuerte KI: I 5201-N (FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF))