Characterizing Latency Overheads in the Deployment of FPGA Accelerators

Cooke, Ryan A.; Fahmy, Suhaib A.

doi:10.1109/fpl50879.2020.00064

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Currently, abstractions to deploy microservices include several hypervisor and container-specific solutions. However, multiple layers of virtualized physical resources, operating system kernels, and network packet processing immensely limit the infrastructure’s ability to attain sub-millisecond latencies for critical services (Cooke and Fahmy (2020); Jha et al, 2021). Recent research has focused on networking infrastructure components that are underutilized despite being highly available in today’s data centers (Choi et al, 2020; Ibanez et al, 2021).…”

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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Section: Introductionmentioning

confidence: 99%