Under CopyrightGowtham, VarunVarunGowthamKeil, OliverOliverKeilYeole, AniketAniketYeoleSchreiner, FlorianFlorianSchreinerTschöke, SimonSimonTschökeWillner, AlexanderAlexanderWillner2022-03-1530.11.20212021https://publica.fraunhofer.de/handle/publica/41316210.1109/DS-RT52167.2021.9576132The distributed Cloud Computing paradigm is continuously being adopted within the industrial automation domain. The most distinguishing feature of these Edge Clouds relates to their ability to provide low-latency and even hard realtime services. As infrastructure deployments can be rather heterogeneous in their nature, service providers require precise means for estimating end-to-end application latency behavior, in order to know performance boundaries that can be met for defining certain Service Level Agreements (SLAs). Although network performance tools exist for many years, mechanisms for assessing hard real-time performance of applications in distributed Edge Cloud environments have not been considered extensively yet. Therefore, we use a built-in feature of the Linux Kernel, the extended Berkeley Packet Filter (eBPF), to measure delays between targeted endpoints in the kernel stack, that enable the user to gain deeper and more accurate measurements of events as compared to generalized approaches (as accurate as eBPF / the time-stamping facility from the kernel). As a result, the real-time behavior of particular Edge Cloud deployments, including its hosted applications, can be profiled in detail by end-users as well as service-providers. Within our evaluation we have monitored a cyclic transmission of packets with a scheduled delay of under 190 ms and measured a round trip time under 2 ms. Future work include profiling the real-time behavior of potentially hosted time-critical applications, such as virtual Programmable Logic Controllers (vPLCs), over real-time networks, such Time Sensitive Networking (TSN); the extension towards dynamically configured real-time networks; and finally its application to future organic, self-optimizing Ultra-Reliable Low-Latency Communication 6G core networks. The distributed Cloud Computing paradigm is continuously being adopted within the industrial automation domain. The most distinguishing feature of these Edge Clouds relates to their ability to provide low-latency and even hard realtime services. As infrastructure deployments can be rather heterogeneous in their nature, service providers require precise means for estimating end-to-end application latency behavior, in order to know performance boundaries that can be met for defining certain Service Level Agreements (SLAs). Although network performance tools exist for many years, mechanisms for assessing hard real-time performance of applications in distributed Edge Cloud environments have not been considered extensively yet. Therefore, we use a built-in feature of the Linux Kernel, the extended Berkeley Packet Filter (eBPF), to measure delays between targeted endpoints in the kernel stack, that enable the user to gain deeper and more accurate measurements of events as compared to generalized approaches (as accurate as eBPF / the time-stamping facility from the kernel). As a result, the real-time behavior of particular Edge Cloud deployments, including its hosted applications, can be profiled in detail by end-users as well as service-providers. Within our evaluation we have monitored a cyclic transmission of packets with a scheduled delay of under 190 ms and measured a round trip time under 2 ms. Future work include profiling the real-time behavior of potentially hosted time-critical applications, such as virtual Programmable Logic Controllers (vPLCs), over real-time networks, such Time Sensitive Networking (TSN); the extension towards dynamically configured real-time networks; and finally its application to future organic, self-optimizing Ultra-Reliable Low-Latency Communication 6G core networks.eneBPFedge computingIoTIIoTIndustry 4.0NFVPLCTSNvPLC004conference paper