NBA (network balancing act)

Kim, Joongi; Jang, Keon; Lee, Keunhong; Ma, Sangwook; Shim, Junhyun; Moon, Sang Heup

doi:10.1145/2741948.2741969

Cited by 54 publications

(9 citation statements)

References 38 publications

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“…Click's focus was more on the programmability than performance. Over the years, Click in uenced a signi cant body of subsequent research on building modular yet high performance packet processing platforms that employed di erent optimization techniques of their own (e.g., NIC o oading, I/O batching, kernel bypass, FPGA acceleration) to improve packet processing performance and add exibility to VNF composition [142,143,144,119,1,145,146]. However, these proposals are centered around the assumption that a middlebox is a monolithic software.…”

Section: Related Work Modular Packet Processingmentioning

confidence: 99%

Resource Management in Softwarized Networks

Chowdhury¹

2022

NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium

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This thesis consists of material all of which I authored or co-authored: see Statement of Contributions included in the thesis. This is a true copy of the thesis, including any required nal revisions, as accepted by my examiners.I understand that my thesis may be made electronically available to the public.iii Statement of ContributionsThis dissertation includes rst authored peer-reviewed materials that have appeared in journals and conference proceedings published by the Institute of Electrical and Electronics Engineers (IEEE). The IEEE's policy on reuse of published materials in a dissertation is as follows: "The IEEE does not require individuals working on a thesis to obtain a formal reuse license".The following list serves as a declaration of the Versions of Record for works included in this dissertation:

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Section: Related Work Modular Packet Processingmentioning

confidence: 99%

Resource Management in Softwarized Networks

Chowdhury¹

2022

NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium

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“…HyperLoop [54] leverages Remote Direct Memory Access (RDMA) to remove the CPU from the critical path of replicated transactions in storage systems. PacketShader [32], Kargus [40], NBA [56], and APUNet [30] take advantage of inexpensive but powerful GPUs to offload and accelerate packet processing. We envision these works to be future components of Metron, thus extending its offloading abilities.…”

Section: Hardware Offloadingmentioning

confidence: 99%

Metron

Katsikas

Barbette

Kostić

et al. 2020

ACM Trans. Comput. Syst.

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Deployment of 100Gigabit Ethernet (GbE) links challenges the packet processing limits of commodity hardware used for Network Functions Virtualization (NFV). Moreover, realizing chained network functions (i.e., service chains) necessitates the use of multiple CPU cores, or even multiple servers, to process packets from such high speed links. Our system Metron jointly exploits the underlying network and commodity servers’ resources: ( i ) to offload part of the packet processing logic to the network, ( ii ) by using smart tagging to setup and exploit the affinity of traffic classes, and ( iii ) by using tag-based hardware dispatching to carry out the remaining packet processing at the speed of the servers’ cores, with zero inter-core communication. Moreover, Metron transparently integrates, manages, and load balances proprietary “blackboxes” together with Metron service chains. Metron realizes stateful network functions at the speed of 100GbE network cards on a single server, while elastically and rapidly adapting to changing workload volumes. Our experiments demonstrate that Metron service chains can coexist with heterogeneous blackboxes, while still leveraging Metron’s accurate dispatching and load balancing. In summary, Metron has ( i ) 2.75–8× better efficiency, up to ( ii ) 4.7× lower latency, and ( iii ) 7.8× higher throughput than OpenBox, a state-of-the-art NFV system.

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“…There is also work that considers both the CPU and the GPU and how they can coordinate to better serve the needs of the NIDS. Kim et al [17] propose NBA, a framework that abstracts the GPU offloading from the programmer and includes load balancing and batching. Their NIDS implementation is also based on Aho-Corasick.…”

Section: Related Workmentioning

confidence: 99%

Co-evaluation of pattern matching algorithms on IoT devices with embedded GPUs

Stylianopoulos

Kindström

Almgren

et al. 2019

Proceedings of the 35th Annual Computer Security Applications Conference

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Pattern matching is an important building block for many security applications, including Network Intrusion Detection Systems (NIDS). As NIDS grow in functionality and complexity, the time overhead and energy consumption of pattern matching become a significant consideration that limits the deployability of such systems, especially on resource-constrained devices. On the other hand, the emergence of new computing platforms, such as embedded devices with integrated, general-purpose Graphics Processing Units (GPUs), brings new, interesting challenges and opportunities for algorithm design in this setting: how to make use of new architectural features and how to evaluate their effect on algorithm performance. Up to now, work that focuses on pattern matching for such platforms has been limited to specific algorithms in isolation.In this work, we present a systematic and comprehensive benchmark that allows us to co-evaluate both existing and new pattern matching algorithms on heterogeneous devices equipped with embedded GPUs, suitable for medium-to high-level IoT deployments. We evaluate the algorithms on such a heterogeneous device, in close connection with the architectural features of the platform and provide insights on how these features affect the algorithms' behavior. We find that, in our target embedded platform, GPU-based pattern matching algorithms have competitive performance compared to the CPU and consume half as much energy as the CPU-based variants. Based on these insights, we also propose HYBRID, a new pattern matching approach that efficiently combines techniques from existing approaches and outperforms them by 1.4x, across a range of realistic and synthetic data sets. Our benchmark details the effect of various optimizations, thus providing a path forward

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NBA (network balancing act)

Cited by 54 publications

References 38 publications

Resource Management in Softwarized Networks

Resource Management in Softwarized Networks

Metron

Co-evaluation of pattern matching algorithms on IoT devices with embedded GPUs

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