Virtual Network Functions Migration Cost: from Identification to Prediction

Abstract

The advent of the function virtualization concept, especially that of network functions, leads to important benefits for future networks. Although the orchestration of virtualized functions presents gains for network operators and clients alike, the overhead for moving functions has not been thoroughly explored so far, especially considering functions virtualized by using container technologies. In this work, we investigate orchestration costs associated with the migration of containerized virtual functions. To this end, we first perform a systematic literature review on state-of-the-art virtual function migration costs, electing time and data transferred as so. We then use a well-known container platform (LXD) to perform several orchestration experiments in a controlled environment. By analyzing the container migration process in smaller complementary steps, and designing experiments to evaluate them individually, a pattern for migration costs is observed. Linear regression is then used to derive a prediction model for the necessary time and data transferring for performing a container migration. To assess the predictor’s accuracy, we present a cloud computing use case where the predictor is deployed. Results indicate that predictions can be accurate within reasonable range, and therefore orchestration algorithms may be improved by accounting for similar prediction models when determining the migration of one or more virtualized functions.