SNF: Synthesizing high performance NFV service chains

Katsikas, Georgios P.; Enguehard, Marcel; Kuźniar, Maciej; Maguire, Gerald Q.; Kostić, Dejan

doi:10.7287/peerj.preprints.2477

Cited by 3 publications

(4 citation statements)

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“…And now, chaining multiple CNFs/VNFs via virtual network I/O comes under question, because inter-CPU core packet transfer obviously degrades performance [45]. Synthesizing network functions into a single CNF/VNF [46] is a promising approach for pursing hardware-level performance, and IOVTee can be integrated into synthesized NFs as shown in Fig. 2 (b).…”

Section: Iovtee Architecturementioning

confidence: 99%

Software Physical/Virtual Rx Queue Mapping Toward High-Performance Containerized Networking

Kawashima

2021

IEEE Trans. Netw. Serv. Manage.

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Softwarization of Network Functions (NFs) accelerates automated deployment and management of services on nextgen networks. Combining flexibility and high-performance is a vital requirement for Network Functions Virtualisation (NFV); however, many studies have demonstrated that containerization or virtualization of NFs severely degrades the fundamental efficiency of packet forwarding. Virtual network I/O, a mechanism of packet transferring between a guest and the host, has been seen as the performance bottleneck in the PVP (Physical-Virtual-Physical) datapath, and one of the main causes of this deterioration is packet copy between them. Various techniques, such as zero-copy, pass-through, and hardware offloading, have been examined to alleviate the performance overhead. However, existing designs and implementations incur pragmatic issues, such as compatibility, manageability, and insufficient quality of performance. We propose yet another design and implementation of zero-copy/pass-through acceleration (named IOVTee) to resolve real-world problems as well as to enhance the forwarding efficiency. IOVTee takes advantage of pre-processing of virtual switches with achieving zero-copy on the receive (Rx) path. The pluggable style of IOVTee for vhost-user (the de-facto virtual network I/O) enables our approach to be transparent to both containers/VMs and virtual switches. In this paper, we explain the heart of IOVTee, a fully software-based Rx queue mapping mechanism (between physical and virtual) that enables a concept of Virtual DMA Write-through (to the NF). Our evaluation results showed that applying IOVTee to vhost-user drastically increased efficiency of packet forwarding in the PVP datapath (by 45% and 98% for traffic of 64-byte and 1514-byte packets respectively).

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Section: Iovtee Architecturementioning

confidence: 99%

Software Physical/Virtual Rx Queue Mapping Toward High-Performance Containerized Networking

Kawashima

2021

IEEE Trans. Netw. Serv. Manage.

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“…In Figure 2, each flag is a classification step usually handled by reading the corresponding packet field(s) to determine the next VNF for the packet. Following the various paths of the graph (as proposed by OpenBox [5] and SNF [4]), one can build a flow table encoding each encountered traffic class as a rule. Table I, shows such a flow table for the example in Figure 2.…”

Section: A Traffic Classesmentioning

confidence: 99%

“…Previous work such as SNF [4] and OpenBox [5] synthesize traffic classifiers. At best, systems such as µNF [6] remove redundant and identical processing modules, but an HTTP classification module cannot benefit from, e.g.…”

Section: Introductionmentioning

confidence: 99%

Combined Stateful Classification and Session Splicing for High-Speed NFV Service Chaining

Barbette

Soldani

Mathy

2021

IEEE/ACM Trans. Networking

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“…For NFV implementation and VNF placement, there exist off-the-shelf solutions. Firstly, for NFV implementation, OpenBox [5] and SNF [6] synthesize multiple VNFs into one equivalent VNF to improve the performance. By contrast, ResQ [7] optimizes the implementation by restricting VNFs on independent physical equipment and balancing cache.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing. Some latency-sensitive applications, such as cloud video, online game, and augmented reality, take advantage of the MEC system to provide fast and stable services. Several new network techniques, including the implementation of NFV (Network Function Virtualization), the placement of VNF (Virtual Network Function) and the scheduling of SFC (Service Function Chain), should be considered to be applied in the MEC system. In this paper, we focus on the research about the scheduling of SFC in the NFV enabled MEC system and propose a solution accordingly. First, we make reasonable assumptions on the settings of MEC systems and model the SFC scheduling problem into a flexible job-shop scheduling problem. Since minimizing the latency can significantly improve the quality of service (QoS) and increase the revenue of Internet Service Providers, our optimization goal is to minimize the overall scheduling latency. To solve this optimization problem, a deep reinforcement learning based algorithm DQS is proposed. DQS can detect the variation of the MEC system's environment and perform adaptive scheduling for SFC requests. As the results of the simulation indicate, DQS works better than the other off-the-shelf algorithms in two key indexes: overall scheduling latency and average resource usage. Moreover, DQS can shorten the decision time and schedule SFCs stably with high performance. It is suitable to be extended to an online scheduling algorithm.

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SNF: Synthesizing high performance NFV service chains

Cited by 3 publications

References 0 publications

Software Physical/Virtual Rx Queue Mapping Toward High-Performance Containerized Networking

Software Physical/Virtual Rx Queue Mapping Toward High-Performance Containerized Networking

Combined Stateful Classification and Session Splicing for High-Speed NFV Service Chaining

Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

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