Sonata

Gupta, Arpit; Harrison, Rob; Canini, Marco; Feamster, Nick; Rexford, Jennifer; Willinger, Walter

doi:10.1145/3230543.3230555

Cited by 195 publications

(33 citation statements)

References 30 publications

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“…More details on this will be discussed in Section 3. A recent approach proposed with Sonata [49,50] stands in between the two families of solutions described above. The idea is to achieve both good performance and expressiveness by designing a unifying framework that takes advantage of PISA platforms and stream processors together.…”

Section: Higher-level Frameworkmentioning

confidence: 99%

Data Stream Processing for Packet-Level Analytics

Fais

Lettieri

Procissi

et al. 2021

Sensors

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One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep the network running smoothly. Implementing this mechanism requires the analysis of packet streams in a real-time (or close to) fashion. In a softwarized network context, Stream Processing Systems (SPSs) can be adopted for this purpose. Recent solutions based on traditional SPSs, such as Storm and Flink, can support the definition of general complex queries, but they show poor performance at scale. To handle input data rates in the order of gigabits per seconds, programmable switch platforms are typically used, although they offer limited expressiveness. With the proposed approach, we intend to offer high performance and expressive power in a unified framework by solely relying on SPSs for multicores. Captured packets are translated into a proper tuple format, and network monitoring queries are applied to tuple streams. Packet analysis tasks are expressed as streaming pipelines, running on general-purpose programmable network devices, and a second stage of elaboration can process aggregated statistics from different devices. Experiments carried out with an example monitoring application show that the system is able to handle realistic traffic at a 10 Gb/s speed. The same application scales almost up to 20 Gb/s speed thanks to the simple optimizations of the underlying framework. Hence, the approach proves to be viable and calls for the investigation of more extensive optimizations to support more complex elaborations and higher data rates.

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Section: Higher-level Frameworkmentioning

confidence: 99%

Data Stream Processing for Packet-Level Analytics

Fais

Lettieri

Procissi

et al. 2021

Sensors

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“…In-switch acceleration. Previous works suggested in-switch acceleration for general-purpose applications such as key-value caches [16,29], replicated key-value stores [15], query processing [11] and aggregations [40,44]. SwiShmem can be useful for such generalpurpose applications too.…”

Section: Related Workmentioning

confidence: 99%

SwiShmem

Zeno

Ports

Nelson

et al. 2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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Programmable switches provide an appealing platform for running network functions (NFs), such as NATs, firewalls, and DDoS detectors, entirely in data plane, at staggering multi-Tbps processing rates. However, to be used in real deployments with a complex multi-switch topology, one NF instance must be deployed on each switch, which together act as a single logical NF. This requirement poses significant challenges in particular for stateful NFs, due to the need to manage distributed shared NF state among the switches. While considered a solved problem in classical distributed systems, data-plane state sharing requires addressing several unique challenges: high data rate, limited switch memory, and packet loss. We present the design of SwiShmem, the first distributed shared state management layer for data-plane P4 programs, which facilitates the implementation of stateful distributed NFs on programmable switches. We first analyze the access patterns and consistency requirements of popular NFs that lend themselves for inswitch execution, and then discuss the design and implementation options while highlighting open research questions.

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“…Building a framework for deploying microservices within the networking domain comes with challenges, including a limited set of resources, sharing resources to facilitate existing networking functionality, and limited infrastructure access. Consequently, most application designers are unaware of how to utilize this high-speed infrastructure and benefit from low-latency application execution, limiting performance improvements to only those experts capable of designing custom solutions (Gupta et al, 2018; Jepsen et al, 2018; Kohler et al, 2018; Xiong and Zilberman, 2019; Zheng and Zilberman, 2021). The microservice framework also needs to support 1) service isolation, 2) elasticity, 3) stream and service scheduling, and 4) dynamic service deployment.…”

Section: Introductionmentioning

confidence: 99%

INDIANA—In-Network Distributed Infrastructure for Advanced Network Applications

Ossen

Musser

Dalessandro

et al. 2023

The International Journal of High Performance Computing Applica

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Data volumes are exploding as sensors proliferate and become more capable. Edge computing is envisioned as a path to distribute processing and reduce latency. Many models of Edge computing consider small devices running conventional software. Our model includes a more lightweight execution engine for network microservices and a network scheduling framework to configure network processing elements to process streams and direct the appropriate traffic to them. In this article, we describe INDIANA, a complete framework for in-network microservices. We will describe how the two components-the INDIANA network Processing Element (InPE) and the Flange Network Operating System (NOS)-work together to achieve effective in-network processing to improve performance in edge to cloud environments. Our processing elements provide lightweight compute units optimized for efficient stream processing. These elements are customizable and vary in sophistication and resource consumption. The Flange NOS provides first-class flow based reasoning to drive function placement, network configuration, and load balancing that can respond dynamically to network conditions. We describe design considerations and discuss our approach and implementations. We evaluate the performance of stream processing and examine the performance of several exemplar applications on networks of increasing scale and complexity.

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Sonata

Cited by 195 publications

References 30 publications

Data Stream Processing for Packet-Level Analytics

Data Stream Processing for Packet-Level Analytics

SwiShmem

INDIANA—In-Network Distributed Infrastructure for Advanced Network Applications

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