An official website of the United States government
Network Function Virtualization seeks to run high performance middleboxes in a flexible, more configurable software environment. Even with advances such as kernel bypass and zero-copy IO, middlebox platforms still struggle to meet stringent throughput and latency requirements. To achieve line rates as network bandwidths rise, these platforms often must make tradeoffs such as inefficiently dedicating more CPU cores or weakening security and isolation properties. In this paper we explore how advances in programmable “smart NICs” can be leveraged by software middlebox platforms to improve performance, resource efficiency, and security. Our evaluation shows several use cases for smart NICs, which improve performance significantly while reducing resource consumption and providing strong isolation.
more »
« less
AMIS: Programmable Privacy-Preserving Network Measurement for Analysis and Troubleshooting
Network measurement and monitoring are instrumental to network operations, planning and troubleshooting. However, increasing line rates (100+Gbps), changing measurement targets and metrics, privacy concerns, and policy differences across multiple R&E network domains have introduced tremendous challenges in operating such high-speed heterogeneous networks, understanding the traffic patterns, providing for resource optimization, and locating and resolving network issues. There is strong demand for a flexible, high-performance measurement instrument that can empower network operators to achieve the versatile objectives of effective network management and resource provisioning. In this demonstration, we present AMIS: Advanced Measurement Instrument and Services to achieve programmable, flow-granularity and event-driven network measurement, sustain scalable line rates, to meet evolving measurement objectives and to derive knowledge for network advancement.
more »
« less
- Award ID(s):
- 1450937
- PAR ID:
- 10098890
- Date Published:
- Journal Name:
- Integrated network management
- ISSN:
- 1573-0077
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.more » « less
-
Online traffic classification enables critical applications such as network intrusion detection and prevention, providing Quality-of-Service, and real-time IoT analytics. However, with increasing network speeds, it has become extremely challenging to analyze and classify traffic online. In this paper, we present Leo, a system for online traffic classification at multi-terabit line rates. At its core, Leo implements an online machine learning (ML) model for traffic classification, namely the decision tree, in the network switch's data plane. Leo's design is fast (can classify packets at switch's line rate), scalable (can automatically select a resource-efficient design for the class of decision tree models a user wants to support), and runtime programmable (the model can be updated on-the-fly without switch downtime), while achieving high model accuracy. We implement Leo on top of Intel Tofino switches. Our evaluations show that Leo is able to classify traffic at line rate with nominal latency overhead, can scale to model sizes more than twice as large as state-of-the-art data plane ML classification systems, while achieving classification accuracy on-par with an offline traffic classifier.more » « less
-
We experimentally demonstrate a quantum-measurement-based receiver for a range of modulation schemes and alphabet lengths in a telecom C-band. We attain symbol error rates below the shot noise limit for all the studied modulation schemes and the alphabet lengths 4≤M≤16. In doing so, we achieve the record energy sensitivity for telecom receivers. We investigate the trade-off between energy and bandwidth use and its dependence on the alphabet length. We identify the combined (energy and bandwidth) resource efficiency as a figure of merit and experimentally confirm that the quantum-inspired hybrid frequency/phase encoding has the highest combined resource efficiency.more » « less
-
Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU frequency scaling, last-level cache (LLC) allocation, DMA buffer size, and packet batch size. GreenNFV learns the resource scheduling model from the benchmark experiments and takes Service Level Agreements (SLAs) into account to optimize resource usage models based on the different throughput and energy consumption requirements. Our evaluation shows that GreenNFV models achieve high transfer throughput and low energy consumption while satisfying various SLA constraints. Specifically, GreenNFV with Throughput SLA can achieve 4.4× higher throughput and 1.5× better energy efficiency over the baseline settings, whereas GreenNFV with Energy SLA can achieve 3× higher throughput while reducing energy consumption by 50%.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
