Ducked Tails: Trimming the Tail Latency of(f) Packet Processing Systems

Gallenmüller, Sebastian; Wiedner, Florian; Naab, Johannes; Carle, Georg

doi:10.23919/cnsm52442.2021.9615532

Cited by 6 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This section describes the critical factors of our low-latency system design. We continue the work presented in previous studies [1][2][3] and present the derived lowlatency system design based on various tuning guides [6][7][8][9]. In particular, we focus our investigation on an updated software stack relying on DPDK 21.11, Debian 11 using Kernel 5.10, and Suricata 7.0.…”

Section: Low-latency System Designmentioning

confidence: 86%

“…Because of the high relevance of interrupt handling on latency [1][2][3]17], we want to investigate the latency impact of the different Linux kernel variants. In this work, we create Linux images with the vanilla, rt, and no-hz kernels being the only difference between them, to ensure comparable results.…”

Section: Background and Related Workmentioning

confidence: 99%

“…2), violating quality of service requirements such as the 1-ms goal of URLLC connections. In the past, we investigated the Snort 3 IPS [1][2][3]. In this work, we focus on the Suricata IPS.…”

Section: Low-latency System Designmentioning

confidence: 99%

“…6.2.2). For comparison, have we compiled the measurements using the same hardware setup as in previous works [1][2][3].…”

Section: Application-specific Techniquesmentioning

confidence: 99%

“…Our setup involves three nodes, the DuT hosting different applications, the LoadGen connected to the DuT via two 10G links, and the Timestamper that monitors both links passively via optical splitters. We kept hardware and software identical to our previous work [1][2][3], to generate easily comparable results. All three nodes use the Intel Xeon D-1518 SoC (4 × 2.2 GHz) and its integrated Intel 10G dual-port X552 NIC.…”

Section: Setupmentioning

confidence: 99%

See 4 more Smart Citations

How Low Can You Go? A Limbo Dance for Low-Latency Network Functions

et al. 2022

Self Cite

View full text Add to dashboard Cite

Throughput is a commonly used performance indicator for networks. However, throughput may be considered insignificant if data is outdated or networks become unpredictable or unreliable. Critical services may even prioritize latency, predictability, and reliability at the expense of throughput to avoid detrimental effects on service operation. Latency, predictability, and reliability are distinct qualities realized in real-time systems. Real-time systems often require additional effort using non-standard interfaces, requiring customized software, or providing low throughput figures. This work picks up the challenge and investigates a single-server network function—a building block for end-to-end low-latency network applications. Assessing reliability and quantifying low latency is equally challenging, as sub-microsecond latency and $$1/10^{5}$$ 1 / 10 5 loss probability leave little room for error. Both, our measurement and the investigated platforms, rely on Linux running on off-the-shelf components. Our paper provides a comprehensive study on the impact of various components on latency and reliability, such as the central processing unit (CPU), the Linux Kernel, the network card, virtualization features, and the networking application itself. We chose Suricata, an intrusion prevention system (IPS), representing a widely deployed, typical network application as our primary subject of investigation.

show abstract

Section: Low-latency System Designmentioning

confidence: 86%

Section: Background and Related Workmentioning

confidence: 99%

Section: Low-latency System Designmentioning

confidence: 99%

“…6.2.2). For comparison, have we compiled the measurements using the same hardware setup as in previous works [1][2][3].…”

Section: Application-specific Techniquesmentioning

confidence: 99%

Section: Setupmentioning

confidence: 99%

See 3 more Smart Citations

How Low Can You Go? A Limbo Dance for Low-Latency Network Functions

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

EnGINE: Flexible Research Infrastructure for Reliable and Scalable Time Sensitive Networks

et al. 2022

View full text Add to dashboard Cite

Self-driving and multimedia systems have common implications: increased demand on network bandwidth and computation nodes. To cope with the current and future challenges, intra-vehicular networks (IVNs) change their layout. They are built around powerful central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it by design lacks support for deterministic behavior, which is crucial for real-time systems. Therefore, the IEEE Time-Sensitive Networking (TSN) task group offers standards introducing low-latency and deterministic communication into Ethernet based networks allowing coexistence of best-effort and real-time traffic. To understand the coexistence challenges, these new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess various topologies, configurations, and data traffic types in IVN setups, we introduce Environment for Generic In-vehicular Networking Experiments—EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility. EnGINE is based on commercial off-the-shelf hardware and uses the flexible Ansible framework for experiment orchestration. This allows us to configure various topologies emulating realistic behavior of IVNs or other time sensitive systems used, e.g., in industrial automation. Obtaining such realism is challenging using simulations. Based on available related work, we further address the challenges found in those networks, especially IVNs. We derive TSN domain framework requirements, provide details on design decisions for the EnGINE, and present results to show its capabilities. The results present relevant network metrics based on collected data. A key focus is on the experiment campaigns realism achieved by real IVNs’ data footage and the OS optimizations to offer real-time behavior. We believe that EnGINE provides the ideal environment for TSN experiments from different domains.

show abstract