A proactive auto-scaling scheme with latency guarantees for multi-tenant NFV cloud

Abstract

Network Functions Virtualization (NFV) is a promising technology to provide packet processing services. However, dynamic capacity provisioning to meet different tenants’ time-varying demands under service-level agreements is still a challenge for NFV service providers. The existing works generally perform the scaling-in/out actions for separate service chains and cannot promise a guarantee for the total processing time. Therefore, we propose Palm, a proactive auto-scaling framework to minimize the resource consumption while enforcing latency guarantees for multiple intersecting service chains. We first leverage the classed Jackson network model to analyze the packet processing latency. Then, a log-linear Poisson auto-regression method is employed to predict each tenant’s packet arrival rate. Based on the prediction result, we perform the capacity adjustment actions and update the flow forwarding policies. We formulate the capacity provisioning task as a nonlinear integer programming problem and propose an evolution based algorithm to tackle it. To simplify the auto-scaling problem, we develop an adaptive algorithm to divide the NFV cloud into persistent and temporary layers. Our comprehensive experiments show that Palm achieves a steady low-latency performance at a lower cost compared with the state-of-the-art dynamic scaling strategies.