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Current solutions mostly focus on how to predict traffic, rather thanobserving traffic characteristics in a specific NFV scenario. So, most of themuse a uniform threshold to scale in/out. In real NFV scenario, each VNF mayserve the one or more flows, and the characteristics of these flows arecompletely different, a uniform threshold used in this scenario is notsuitable, because each VNF has a distinct processing logic depending onincident network traffic and events. Even if certain VNFs share packetprocessing functionality such as packet header analysis, the differences inupper-layer processing and implementation can exhibit unique resource usagepatterns.  We proposes a dynamic threshold scaling mechanism that can tailor thresholdsaccording to each VNF's characteristic. As setting thresholds is a per-VNFtask, and requires a deep understanding of workload trends and the diversity ofeach VNF, so we have added tailor-made features to the traditional dynamicmechanism. Besides, we also reserve resources by predicting workload and add anemergency module to cope with anomaly traffic, that is to say we develop ahybrid scaling policy combining proactive and reactive scaling together.Moreover, the sharp rise of network traffic not only can be caused by largeamount of new incoming flows, but also can be induced by the growing ofexisting flows. If the traffic arises mainly due to the growing of existingflows, then only rerouting new flows can not alleviate the overload quickly andSLAs may be violated \cite{zhang2016co}. The only method to avoid SLAviolations is to migrate flows and associated NF internal states quickly andsafely from existing instances to new scaled instances, so state migration isan important part of the scaling procedure. We achieved the flow migration inscaling process on openNF to guarantee the accuracy and timeline of scaling.

Traffic-aware Threshold Adjustment for NFV Scaling using DDPG