GRU and EdgeQ-Learning based Traffic Prediction and Scaling of SFC

Abstract

Services in today's network produce a heavy amount of traffic, and all this traffic might have to pass through a Service Function Chain (SFC). The service provider must place these SFCs in the appropriate locations, and scale them as the request to the service increases. Appropriate placement and scaling of these SFCs will ensure Service Level Agreement (SLA) in terms of throughput, end-to-end delay, or successful serving of the requests. SFC placement and scaling are correlated tasks, however need to be modeled separately. For placing SFCs we require to consider server resources such as memory, CPU, bandwidth, and server locations. On the other hand for scaling, we need to consider resource utilization of VNFs as extra matrixes. Scaling in and out would require an additional cost and time to deploy new VNFs, hence it is important to predict VNF resource requirements in advance and prepare the resources for incoming traffic needs. In this paper we predicted an incoming resource demand using Recurrent Neural Network (RNN) based Gated Recurrent Unit (GRU) model. We predict the resource demands 2 min in advance and deploy the VNFs using the EdgeQ-Leaning model. We call this integrated algorithm as GRU-EdgeQL. To validate our approach we compared it with threshold and random algorithms. We implemented our algorithm in the OpenStack testbed. Our validation reveals that GRU based prediction and EdgeQ-Learning based placement does not only meet the Service Level Agreement (SLA) requirement by reducing the overall latency and SLA violation but also reduces the number of scaling operations thereby reducing the overall scaling cost. ? 2021 IEEE.