分布式可编程数据平面的sfc迁移方案

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Xiaoquan Zhang;Lin Cui;Fung Po Tso;Yuhui Deng;Zhetao Li;Weijia Jia
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引用次数: 0

摘要

业务功能链(sfc)是在当今的isp和数据中心网络中提供特定服务以满足运营商需求的网络功能序列。为了提高sfc的性能,可编程数据平面被用来利用其低延迟和高性能的数据包处理。然而,sfc需要适应需求和属性变化等动态变化。因此,迁移sfc的能力是必不可少的。不幸的是,在分布式可编程数据平面中迁移sfc是具有挑战性的,因为存在性能下降的风险,并且无法满足sfc要求和交换机中的资源限制。本文提出了Monte算法,它提供了一种在分布式可编程数据平面上有效的sfc迁移方案。基于分布式数据平面中交换机的资源限制和sfc的属性约束,我们建立了一个新的整数规划模型来表示迁移过程。此外,通过深入分析交换机流水线中的资源分配情况,设计了sfc迁移算法,优化迁移成本。Monte已经在P4软件交换机(Bmv2)和硬件交换机(Intel Tofino ASIC)上实现。广泛的评价结果表明,Monte的迁移成本比现有部署方案平均降低94.03%,可以有效节约管道资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monte: SFCs Migration Scheme in the Distributed Programmable Data Plane
Service function chains (SFCs) are sequences of network functions that provide specific services to meet operators’ needs in today's ISPs and datacenter networks. To improve the performance of SFCs, programmable data planes are used to leverage their low latency and high performance packet processing. However, SFCs need to be adaptable to dynamics such as changes in requirements and attributes. Therefore, the ability to migrate SFCs is essential. Unfortunately, migrating SFCs in distributed programmable data planes is challenging due to the risk of degraded performance and failure to meet SFCs requirements and resource constraints in switches. In this paper, we propose Monte, which provides an effective SFCs migration scheme in distributed programmable data planes. We build a novel integer programming model to represent the migration process with constraints on resource limitations of switches and SFCs attributes in the distributed data plane. Additionally, an SFCs migration algorithm is designed to optimize the migration cost by deeply analyzing resource allocation in the switch pipeline. Monte has been implemented on both P4 software switches (Bmv2) and hardware switches (Intel Tofino ASIC). Extensive evaluation results show that the migration cost in Monte is 94.03% lower on average than the state-of-the-art deployment scheme, and Monte can effectively save pipeline resources.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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