SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Abstract

Network function virtualization is a promising technology for providing personalized services via agile service function chains (SFCs). Flexible SFC orchestration and rational resource allocation are pivotal for improving the SFC's quality of service (QoS). However, the requirements for computational load and resources have frequently been changing. Consequently, static resource allocation can result in resource insufficiency when SFCs turn busy and resource waste due to resource overplus when SFCs are idle. Since a dynamic resource allocation is necessary, the existing dynamic resource allocation methods' responses have often been delayed. This paper proposes an SFC active reconfiguration mechanism (SARM) based on computational load and resource demand. The SARM predicts nodes' computation loads and SFCs' resource demands and uses these predictions to estimate future QoS and develop the SFC reconfiguration strategy. The SARM considers multiple factors and applies a heuristic algorithm to achieve the tradeoff between migration cost and QoS preservation. The experiments demonstrate that the SARM can effectively predict the nodes' load and the resource demand of SFCs. In addition, the SARM can successfully identify the SFCs to reconfigure and reduce the QoS maintenance costs. The simulation results indicate that the average delays of the SFCs can be reduced by at least 26%.