Performance benchmarking of state-of-the-art software switches for NFV

Zhang, Tianzhu; Linguaglossa, Leonardo; Giaccone, Paolo; Iannone, Luigi; Roberts, James W.

doi:10.1016/j.comnet.2021.107861

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…BESS is a representative NFV device such that it has been widely used by advanced NFV techniques, such as NFP [7]. The reason we chose BESS is that it has high performance in more SFC scenarios [33]. Our experiments are conducted on a testbed comprising three servers, each of which has twelve 2.3 GHz CPU cores, 128 GB RAM, and a two‐port 40 Gbps NIC.…”

Section: Discussionmentioning

confidence: 99%

Enabling low‐latency service function chains by merging duplicate match operations

et al. 2022

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Service function chains (SFCs) built by network functions (NFs) are usually required to be low‐latency, especially in some tight latency scenarios such as Distributed Denial of Service (DDoS) defense. However, different NFs in SFCs are likely to perform identical operations of matching specific packet header fields (e.g., source IP address). Such a processing redundancy inevitably increases the overall end‐to‐end processing latency of SFCs, which in turn affects network management applications in their decision‐making process of handling short‐lived network events (e.g., microbursts). To address this problem, in this paper, we propose a novel NFV framework, DMO, that aims to eliminate those identical and redundant match operations among different NFs in input SFCs while maintaining the original SFC semantics of packet processing. To achieve this goal, our design proposes a semantic‐preserving mechanism that merges duplicate match operations between different NFs and also preserves original SFC semantics. Also, to avoid potential conflicts among NF rules at runtime, DMO further offers another mechanism that resolves unnecessary conflicts among different NF rules by assigning reasonable priorities to these rules. We have implemented a prototype of DMO. Our experiments indicate that DMO achieves 26.7%–67.3% latency reduction for real‐world SFCs.

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Section: Discussionmentioning

confidence: 99%

Enabling low‐latency service function chains by merging duplicate match operations

et al. 2022

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“…Thus, a research direction can be analyzing the application requirement and devices capabilities to provide directions on an appropriate selection of the network element for a particular application. Starting points can be the studies in [13], [177]. While [177] explores the design spaces of seven state-of-the-art software switches and compares their performance under specific application of routing NFV traffic, [13] have a more general view.…”

Section: E Generic Research Directionsmentioning

confidence: 99%

“…Starting points can be the studies in [13], [177]. While [177] explores the design spaces of seven state-of-the-art software switches and compares their performance under specific application of routing NFV traffic, [13] have a more general view. Indeed, [13] gives some guidelines to implement several applications based on a few application features i.e., number of operations per packet, required storage per packet processing, and output size after packet processing.…”

Section: E Generic Research Directionsmentioning

confidence: 99%

A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

Kianpisheh

Taleb

2023

IEEE Commun. Surv. Tutorials

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In comparison with cloud computing, edge computing offers processing at locations closer to end devices and reduces the user experienced latency. The new recent paradigm of innetwork computing employs programmable network elements to compute on the path and prior to traffic reaching the edge or cloud servers. It advances common edge/cloud server based computing through proposing line rate processing capabilities at closer locations to the end devices. This paper discusses use cases, enabler technologies and protocols for in-network computing. According to our study, considering programmable data plane as an enabler technology, potential in-network computing applications are in-network analytics, in-network caching, innetwork security, and in-network coordination. There are also technology specific applications of in-network computing in the scopes of cloud computing, edge computing, 5G/6G, and NFV. In this survey, the state of the art, in the framework of the proposed categorization, is reviewed. Furthermore, comparisons are provided in terms of a set of proposed criteria which assess the methods from the aspects of methodology, main results, as well as application-specific criteria. Finally, we discuss lessons learned and highlight some potential research directions.

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“…Network functions virtualization (NFV) [1] addresses this issue by transforming network functions into virtualized network functions (VNF) [2] and deploying them on general‐purpose hardware platforms [3, 4]. NFV provides customized network services via service function chain (SFC) [5], introduces a new network function service business processing architecture, and enhances service flexibility and scalability [6]. SFC in NFV chains service requests to process network services by directing traffic to corresponding network functional nodes for processing through sequence‐ordered VNFs in a user‐issued SFC request (SFCR) [7].…”

Section: Introductionmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang,

Tian,

Zhang

et al. 2024

IET Communications

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Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

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Performance benchmarking of state-of-the-art software switches for NFV

Cited by 13 publications

References 8 publications

Enabling low‐latency service function chains by merging duplicate match operations

Enabling low‐latency service function chains by merging duplicate match operations

A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

An improved resource allocation method for mapping service function chains based on A3C

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