Fast Packet Processing: A Survey

Cerović, Danilo; Piccolo, Valentin Del; Amamou, Ahmed; Haddadou, Kamel; Pujolle, Guy

doi:10.1109/comst.2018.2851072

Cited by 47 publications

(26 citation statements)

References 79 publications

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“…Many network operators/providers have shifted toward pure software solutions that can be run on commodity off-the-shelf (COTS) servers, aka commodity hardware, to (i) avoid proprietary inflexible hardware middleboxes and to (ii) reduce capital expense (CAPEX) & operating expense (OPEX). These efforts can be classified into three main categories [14,54]:…”

Section: Software Packet Processingmentioning

confidence: 99%

PacketMill: toward per-Core 100-Gbps networking

Farshin

Barbette

Roozbeh

et al. 2021

Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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We present PacketMill, a system for optimizing software packet processing, which (i) introduces a new model to efficiently manage packet metadata and (ii) employs code-optimization techniques to better utilize commodity hardware. PacketMill grinds the whole packet processing stack, from the high-level network function configuration file to the low-level userspace network (specifically DPDK) drivers, to mitigate inefficiencies and produce a customized binary for a given network function. Our evaluation results show that PacketMill increases throughput (up to 36.4 Gbps ś 70%) & reduces latency (up to 101 µs ś 28%) and enables nontrivial packet processing (e.g., router) at ≈100 Gbps, when new packets arrive > 10× faster than main memory access times, while using only one processing core.

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Section: Software Packet Processingmentioning

confidence: 99%

PacketMill: toward per-Core 100-Gbps networking

Farshin

Barbette

Roozbeh

et al. 2021

Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…An interesting future research direction is to examine the latency impact of hardware acceleration modules [56,57] as well as fast packet processing paradigms, e.g. [58][59][60][61], for low-latency network packet processing. Moreover, future research should explore high-availability strategies for private clouds, e.g.…”

Section: Acimentioning

confidence: 99%

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

Heuchert

Rimal

Reisslein

et al. 2021

ACI

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PurposeMajor public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.Design/methodology/approachTo demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.FindingsThrough the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.Originality/valueThe authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

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“…Программные решения по обработке сетевого трафика можно разделить на три основных категории [1][2][3]: 1) низкоуровневые решения, обеспечивающие ввод/вывод трафика: DPDK [4], PF_RING…”

Section: программная обработка трафикаunclassified

High-speed network traffic capturing and processing tools

Larin¹,

Getman²

2021

Proceedings of ISP RAS

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Network stacks currently implemented in operating systems can no longer cope with the packet rates offered by 10 Gbit Ethernet. Thus, frameworks were developed claiming to offer a faster alternative for this demand. These frameworks enable arbitrary packet processing systems to be built from commodity hardware handling a traffic rate of several 10 Gbit interfaces, entering a domain previously only available to custom-built hardware. In this paper, we survey various frameworks for high-performance packet IO and their interaction with a modular frameworks and specialized virtual network functions software for high-speed packet processing. We introduce a model to estimate and assess the performance of these packet processing frameworks. Moreover, we analyze the performance of the most prominent frameworks based on representative measurements in packet capturing scenarios. Therefore, we provide a comparison between them and select the area of applicability.

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Fast Packet Processing: A Survey

Cited by 47 publications

References 79 publications

PacketMill: toward per-Core 100-Gbps networking

PacketMill: toward per-Core 100-Gbps networking

Design of a small-scale and failure-resistant IaaS cloud using OpenStack

High-speed network traffic capturing and processing tools

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