Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)

Menth, Michael; Mostafaei, Habib; Merling, Daniel; Häberle, Marco

doi:10.3390/fi11070159

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…• Reconfigurability: the parser and the processing logic can be redefined in the field. Variations [54][55][56][57][58][59][60][61][62] Collectors and Solutions [63][64][65][66][67] Congestion Control [68][69][70][71][72][73][74][75][76] Measurements AQM [99][100][101][102][103][104][105][106][107][108][109] QoS and TM [110][111][112][113][114] Multicast [115][116][117] Load Balancing [118][119][120][121][122][123][124][125]…”

Section: P4 Languagementioning

confidence: 99%

An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

2021

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Traditionally, the data plane has been designed with fixed functions to forward packets using a small set of protocols. This closed-design paradigm has limited the capability of the switches to proprietary implementations which are hard-coded by vendors, inducing a lengthy, costly, and inflexible process. Recently, data plane programmability has attracted significant attention from both the research community and the industry, permitting operators and programmers in general to run customized packet processing functions. This open-design paradigm is paving the way for an unprecedented wave of innovation and experimentation by reducing the time of designing, testing, and adopting new protocols; enabling a customized, top-down approach to develop network applications; providing granular visibility of packet events defined by the programmer; reducing complexity and enhancing resource utilization of the programmable switches; and drastically improving the performance of applications that are offloaded to the data plane. Despite the impressive advantages of programmable data plane switches and their importance in modern networks, the literature has been missing a comprehensive survey. To this end, this paper provides a background encompassing an overview of the evolution of networks from legacy to programmable, describing the essentials of programmable switches, and summarizing their advantages over Softwaredefined Networking (SDN) and legacy devices. The paper then presents a unique, comprehensive taxonomy of applications developed with P4 language; surveying, classifying, and analyzing more than 200 articles; discussing challenges and considerations; and presenting future perspectives and open research issues.

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Section: P4 Languagementioning

confidence: 99%

An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

2021

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“…Especially, P4 has been greatly studied to process packets in communication networks with the goal of enhancing their performance. On the one hand, [ 11 , 12 , 13 , 14 ] researched the measurement of network statistics using data plane programming with P4 language. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Ref. [ 11 ] developed a congestion management mechanism that monitors the user activity and accordingly prevents users from monopolizing the network. Authors in [ 12 , 14 ] proposed a solution in multiple platforms (P4, FPGA, GPU, CPU, multi-core CPU, and OVS) for network measurement reports in real time, based on elastic (instead of static) sketches in order to dynamically allocate memory for elephant flows.…”

Section: Related Workmentioning

confidence: 99%

Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes

Atutxa

Franco

Sasiain

et al. 2021

Sensors

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Industrial networks are introducing Internet of Things (IoT) technologies in their manufacturing processes in order to enhance existing methods and obtain smarter, greener and more effective processes. Global predictions forecast a massive widespread of IoT technology in industrial sectors in the near future. However, these innovations face several challenges, such as achieving short response times in case of time-critical applications. Concepts like in-network computing or edge computing can provide adequate communication quality for these industrial environments, and data plane programming has been proved as a useful mechanism for their implementation. Specifically, P4 language is used for the definition of the behavior of programmable switches and network elements. This paper presents a solution for industrial IoT (IIoT) network communications to reduce response times using in-network computing through data plane programming and P4. Our solution processes Message Queuing Telemetry Transport (MQTT) packets sent by a sensor in the data plane and generates an alarm in case of exceeding a threshold in the measured value. The implementation has been tested in an experimental facility, using a Netronome SmartNIC as a P4 programmable network device. Response times are reduced by 74% while processing, and delay introduced by the P4 network processing is insignificant.

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“…Much research has studied how to use in-network rate computations to support congestion control (e.g., XCP [55] and RCP [27]), queue management (e.g., CoDel [85] and AIFO [121]), or load balancing (e.g., CONGA [4]). P4QCN [30], P4-CoDel [61], and P4-ABC [78] are P4 implementations of specific protocols that require floating point support -currently unavailable in switch hardware. Sharma et al proposed a library that applies approximation to work around this limitation [102].…”

Section: Related Workmentioning

confidence: 99%

Unlocking the Power of Inline Floating-Point Operations on Programmable Switches

Yang¹,

Alama²,

Sapio³

et al. 2021

Preprint

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1 The advent of switches with programmable dataplanes has enabled the rapid development of new network functionality, as well as providing a platform for acceleration of a broad range of application-level functionality. However, existing switch hardware was not designed with application acceleration in mind, and thus applications requiring operations or datatypes not used in traditional network protocols must resort to expensive workarounds. Applications involving floating point data, including distributed training for machine learning and distributed query processing, are key examples.In this paper, we propose FPISA, a floating point representation designed to work efficiently in programmable switches. We first implement FPISA on an Intel Tofino switch, but find that it has limitations that impact throughput and accuracy. We then propose hardware changes to address these limitations based on the open-source Banzai switch architecture, and synthesize them in a 15-nm standard-cell library to demonstrate their feasibility. Finally, we use FPISA to implement accelerators for training for machine learning and for query processing, and evaluate their performance on a switch implementing our changes using emulation. We find that FPISA allows distributed training to use 25-75% fewer CPU cores and provide up to 85.9% better throughput in a CPU-constrained environment than SwitchML. For distributed query processing with floating point data, FPISA enables up to 2.7× better throughput than Spark.

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Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)

Cited by 14 publications

References 20 publications

An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes

Unlocking the Power of Inline Floating-Point Operations on Programmable Switches

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