Data-driven resource flexing for network functions visualization
Data-driven resource flexing for network functions visualization
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DOI：
10.1145/3230718.3230725
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发表时间：
2018-07
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影响因子：
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通讯作者：
Lianjie Cao;S. Fahmy;P. Sharma;Shandian Zhe
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文献类型：
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作者：
Lianjie Cao;S. Fahmy;P. Sharma;Shandian Zhe
资源弹性是随着工作负载的变化，分配资源按需分配的概念。这是虚拟化网络函数（VNF）的关键优势，而不是其非虚拟化对应物。但是，由于不可预测的工作负载和复杂的VNF处理逻辑，在做出资源弹性决策时，很难平衡及时性和资源效率。在这项工作中，我们为网络函数虚拟化（ENVI）提出了一个利用VNF级功能和基础架构级功能的组合来构建基于神经网络的扩展决策引擎来生成及时的扩展决策。为了适应动态工作负载，我们设计了一种基于窗口的倒带机制，以通过新兴工作负载模式更新神经网络，并实时做出准确的决策。我们使用基于现实世界中的痕迹生成的工作负载对实际VNF（IDS Suricata和Caching代理鱿鱼）进行的实验结果表明，与常用的基于规则的规模规模策略相比，Envi规定在没有违反服务水平的而没有违反服务水平的情况下少了（多达26％）资源。
Resource flexing is the notion of allocating resources on-demand as workload changes. This is a key advantage of Virtualized Network Functions (VNFs) over their non-virtualized counterparts. However, it is difficult to balance the timeliness and resource efficiency when making resource flexing decisions due to unpredictable workloads and complex VNF processing logic. In this work, we propose an Elastic resource flexing system for Network functions VIrtualization (ENVI) that leverages a combination of VNF-level features and infrastructure-level features to construct a neural-network-based scaling decision engine for generating timely scaling decisions. To adapt to dynamic workloads, we design a window-based rewinding mechanism to update the neural network with emerging workload patterns and make accurate decisions in real time. Our experimental results for real VNFs (IDS Suricata and caching proxy Squid) using workloads generated based on real-world traces, show that ENVI provisions significantly fewer (up to 26%) resources without violating service level objectives, compared to commonly used rule-based scaling policies.