A Near Optimal Reliable Composition Approach for Geo-Distributed Latency-Sensitive Service Chains
A Near Optimal Reliable Composition Approach for Geo-Distributed Latency-Sensitive Service Chains
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DOI：
10.1109/infocom.2019.8737498
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发表时间：
2019-04
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影响因子：
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通讯作者：
D. Chemodanov;P. Calyam;Flavio Esposito
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文献类型：
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作者：
D. Chemodanov;P. Calyam;Flavio Esposito
传统上，网络功能虚拟化使用服务功能链接(SFC)来放置服务功能，并将它们与相应的流分配链接在一起。随着边缘计算和物联网的出现，需要一组可停用的延迟敏感型SFC来支持地理分布的云基础架构中的应用。然而，在这种情况下，最优的SFC组合变成了NP-Hard整数多商品链流动(MCCF)问题，该问题没有已知的近似保证。本文针对地理分布的云基础设施，提出了一种实用且接近最优的SFC组合方法，该方法还考虑了端到端网络的服务质量约束，如延迟、丢包等。具体地，我们提出了一种新的Metapath组合变量方法，对于美国Tier-1($300节点)和区域($600节点)基础设施提供商的实际规模的整数MCCF问题，该方法平均达到99%的最优性且耗时数秒。为了确保可靠性，我们将SFC与容量机会限制和备份策略组合在一起。通过使用包含具有挑战性的灾难事件条件的跟踪驱动模拟，我们发现我们的解决方案包含的SFC数量是最先进的网络虚拟化方法的两倍。
Traditionally, Network Function Virtualization uses Service Function Chaining (SFC) to place service functions and chain them with corresponding flows allocation. With the advent of Edge computing and IoT, a retiable composition of latency-sensitive SFCs is needed to support applications in geo-distributed cloud infrastructures. However, the optimal SFC composition in this case becomes the NP-hard integer multi-commodity-chain flow (MCCF) problem that has no known approximation guarantees. In this paper, we present a novel practical and near optimal SFC composition approach for geo-distributed cloud infrastructures that also admits end-to-end network QoS constraints such as latency, packet loss, etc. Specifically, we propose a novel metapath composite variable approach that reaches 99% optimality on average and takes seconds for practically sized integer MCCF problems of US Tier-1 ($\sim$300 nodes) and regional ($\sim$600 nodes) infrastructure providers’ topologies. To ensure reliability, we compose SFCs with capacity chance-constraints and backup policies. Using trace-driven simulations comprising of challenging disaster-incident conditions, we show that our solution composes twice as many SFCs than the state-of-the-art network virtualization methods.