Availability-Aware Resource Allocation for Containerized Network Functions

Abstract

Deploying virtual network functions (VNFs) such as WAN accelerators, network address translators (NATs) and 5G functions at the network edge (NE) can significantly reduce the experienced latency of delay-ultrasensitive applications (e.g., autonomous vehicles and Internet of things). Nonetheless, a major challenge to their anticipated large-scale deployment is the ability to efficiently allocate and manage the scarce NE resources hosting these functions. In this thesis, we describe a novel containerized infrastructure manager (cIM) that extends current managers, such as Kubernetes, with the necessary building blocks to provide an accurate yet elastic resource allocation service to containerized VNFs at scale. The proposed cIM treats the main modules of the VNFs, i.e., the containerized VNF components (cNFCs), as atomic special-purpose functions that can be rapidly deployed to form complex network services. The main component of the proposed cIM, the resource reservation manager (RRM), employs concepts of risk pooling in the insurance industry to accurately reserve the needed resources for the hosting containers. More precisely, to meet anticipated cNFCs demand fluctuation, the RRM accurately reserves a quota of additional resources that are shared by the containerized functions collected together in clusters. The reserved quota of resources ensures the desired availability level of the cNFCs without over-provisioning the scarce resources of the NE. The RRM considers three different situations namely that of a cNFC instance, a cluster of cNFCs or multiple cNFC clusters sharing the reserved resources. Different allocation approaches are then presented for each of these three situations. Simulation experiments are conducted to evaluate the performance of our reservation schemes from different aspects. The corresponding experimental results demonstrate that our proposed cIM can significantly improve the performance of the cNFCs and guarantee their desired availability with minimal resource reservation. Optimal allocation solutions of the resource pools are further proposed considering the desired availability level and the limit of resource pools. The evaluation results demonstrate that our optimization models and solutions obtain the best performance of relevant testing parameters, e.g., availability.