Auto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of workload is dynamically changed because of the variation in service demand over time. The predominant auto-scaling approaches use scaling rules (threshold-based reactive approach) or scaling policies (schedule-based proactive approach) to adapt resources and meet the performance requirements of each VNF. The reactive approaches can significantly degrade the VNF performance for improper reconfiguration or variation of auto-scaling rules. Conversely, the proactive approaches dynamically adjust the scaling policies according to the workload variation. These approaches rely on accurate workload predictive.​