Network Functions Virtualization (NFV) is a promising technology to provide packet processing services. However, dynamic capacity provisioning to meet different tenants’ time-varying demands under service-level agreements is still a challenge for NFV service providers. The existing works generally perform the scaling-in/out actions for separate service chains and cannot promise a guarantee for the total processing time. Therefore, we propose Palm, a proactive auto-scaling framework to minimize the resource consumption while enforcing latency guarantees for multiple intersecting service chains. We first leverage the classed Jackson network model to analyze the packet processing latency. Then, a log-linear Poisson auto-regression method is employed to predict each tenant’s packet arrival rate. Based on the prediction result, we perform the capacity adjustment actions and update the flow forwarding policies. We formulate the capacity provisioning task as a nonlinear integer programming problem and propose an evolution based algorithm to tackle it. To simplify the auto-scaling problem, we develop an adaptive algorithm to divide the NFV cloud into persistent and temporary layers. Our comprehensive experiments show that Palm achieves a steady low-latency performance at a lower cost compared with the state-of-the-art dynamic scaling strategies.

网络功能虚拟化（NFV）是一种提供数据包处理服务的有前途的技术。但是，根据服务水平协议满足不同租户的时变需求的动态容量配置仍然是NFV服务提供商所面临的挑战。现有工作通常对单独的服务链执行放大/缩小操作，并且不能保证总处理时间。因此，我们建议Palm，一种主动的自动扩展框架，可最大程度地减少资源消耗，同时为多个相交的服务链实施延迟保证。我们首先利用分类的Jackson网络模型来分析数据包处理延迟。然后，采用对数线性泊松自回归方法来预测每个租户的数据包到达率。根据预测结果，我们执行容量调整操作并更新流转发策略。我们将容量提供任务公式化为非线性整数规划问题，并提出了一种基于演化的算法来解决。为了简化自动缩放问题，我们开发了一种自适应算法，将NFV云分为持久层和临时层。我们的综合实验表明，Palm 与最新的动态缩放策略相比，它以较低的成本实现了稳定的低延迟性能。