Online Network Flow Optimization for Multi-Grade Service Chains
Online Network Flow Optimization for Multi-Grade Service Chains
复制标题
DOI：
10.1109/infocom41043.2020.9155341
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Víctor Valls;G. Iosifidis;Geeth de Mel;L. Tassiulas
中科院分区：
文献类型：
--
作者：
Víctor Valls;G. Iosifidis;Geeth de Mel;L. Tassiulas
我们研究了使用多级VNF链的数据分析服务的网络内执行问题。节点托管VNF，为链的每个阶段提供不同且可能随时间变化的增益，我们的目标是最大限度地提高分析性能，同时最大限度地降低数据传输和处理成本。VNF的性能只有在执行后才会显示，因为它依赖于数据或由第三方控制，而服务请求和网络成本也可能随时间而变化。我们设计了一个操作算法，学习，在飞行中，最佳的路由策略和每个链的组成和长度。我们的算法结合了轻量级的采样技术和基于拉格朗日的原始-对偶迭代，使其具有可扩展性并获得可证明的最优性保证。我们使用视频分析服务演示了所提出的算法的性能，并探讨了不同系统参数对其的影响。我们的模型和优化框架可以很容易地扩展到不同类型的网络和服务。
We study the problem of in-network execution of data analytic services using multi-grade VNF chains. The nodes host VNFs offering different and possibly time-varying gains for each stage of the chain, and our goal is to maximize the analytics performance while minimizing the data transfer and processing costs. The VNFs’ performance is revealed only after their execution, since it is data-dependent or controlled by third-parties, while the service requests and network costs might also vary with time. We devise an operation algorithm that learns, on the fly, the optimal routing policy and the composition and length of each chain. Our algorithm combines a lightweight sampling technique and a Lagrange-based primal-dual iteration, allowing it to be scalable and attain provable optimality guarantees. We demonstrate the performance of the proposed algorithm using a video analytics service, and explore how it is affected by different system parameters. Our model and optimization framework is readily extensible to different types of networks and services.