A custom processor for protocol-independent packet parsing

Zolfaghari, Hesam; Rossi, Davide; Nurmi, Jari

doi:10.1016/j.micpro.2019.102910

“…P4 is a programming language to access the hardware without the knowledge of the architecture of hardware. P4 is used to modify the packet-forwarding mechanisms of the SDN switches [67]. Initially, P4 was used to write software programs and program hardware switches.…”

Section: P4 (Programming Protocol-independent Packet Processors)mentioning

confidence: 99%

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Imran

¹

,

Ghaffar

²

,

Alshahrani

³

et al. 2021

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

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“…Figure 2 illustrates the high-level architecture of a Flare PsPINbased programmable switch. After a packet is received from any of the switch ports, its headers are processed by a parser [47,48] that, based on configurable matching rules, decides if the packet must be processed by a processing unit (or sent directly to the routing tables unit 1 ), and which function must be executed on the packet. The processing unit can modify the content of each packet, and decide its destination.…”

Section: Switch Architecturementioning

confidence: 99%

Flare: Flexible In-Network Allreduce

De Sensi,

Di Girolamo,

Ashkboos

et al. 2021

Preprint

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The allreduce operation is one of the most commonly used communication routines in distributed applications. To improve its bandwidth and to reduce network traffic, this operation can be accelerated by offloading it to network switches, that aggregate the data received from the hosts, and send them back the aggregated result. However, existing solutions provide limited customization opportunities and might provide suboptimal performance when dealing with custom operators and data types, with sparse data, or when reproducibility of the aggregation is a concern. To deal with these problems, in this work we design a flexible programmable switch by using as a building block PsPIN, a RISC-V architecture implementing the sPIN programming model. We then design, model, and analyze different algorithms for executing the aggregation on this architecture, showing performance improvements compared to state-of-the-art approaches. CCS CONCEPTS• Networks → In-network processing; • Hardware → Networking hardware; • Computer systems organization → Distributed architectures.

show abstract

“…Figure 2 illustrates the high-level architecture of a Flare PsPINbased programmable switch. After a packet is received from any of the switch ports, its headers are processed by a parser [47,48] tables unit 1 ), and which function must be executed on the packet. The processing unit can modify the content of each packet, and decide its destination.…”

Section: Switch Architecturementioning

confidence: 99%

Flare

Sensi

¹

,

Girolamo

²

,

Ashkboos

³

et al. 2021

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

23

0

View full text Add to dashboard Cite

The allreduce operation is one of the most commonly used communication routines in distributed applications. To improve its bandwidth and to reduce network traffic, this operation can be accelerated by offloading it to network switches, that aggregate the data received from the hosts, and send them back the aggregated result. However, existing solutions provide limited customization opportunities and might provide suboptimal performance when dealing with custom operators and data types, with sparse data, or when reproducibility of the aggregation is a concern. To deal with these problems, in this work we design a flexible programmable switch by using as a building block PsPIN, a RISC-V architecture implementing the sPIN programming model. We then design, model, and analyze different algorithms for executing the aggregation on this architecture, showing performance improvements compared to state-of-the-art approaches. CCS CONCEPTS• Networks → In-network processing; • Hardware → Networking hardware; • Computer systems organization → Distributed architectures.

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A custom processor for protocol-independent packet parsing

Cited by 12 publications

References 13 publications

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Flare: Flexible In-Network Allreduce

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