An Optimization Framework for Edge-to-Cloud Offloading of Kubernetes Pods in V2X Scenarios

Abstract

Vehicle-to-everything V2X applications usually have strict latency requirements that can be difficult to meet in traditional cloud-centric networks. By pushing resources to edge servers located closer to the users, end-to-end latency can be greatly reduced. Task offloading in edge-cloud environments refers to the optimization of which tasks should be offloaded between the edge and cloud. Moreover, the use of containers to virtualize applications can further reduce resource and time consumption and, in turn, the latency of V2X applications. Even though Kubernetes has become the de facto container orchestrator, the offloading of Kubernetes pods has not been previously studied in the literature, to the best of the authors’ knowledge. In this paper, a theoretical optimization framework is proposed for edge-to-cloud offloading of V2X tasks implemented as Kubernetes pods. First, an utility function is derived in terms of the cumulative pod response time, weighted by the priority levels and resource usage requirements of pods. Based on the optimal theoretical solution to this problem under memory and central processing unit (CPU) constraints, an edge-to-cloud offloading decision algorithm (ECODA) is proposed. Numerical simulations demonstrate that ECODA outperforms first-in, first-served (FIFS) in terms of utility, average pod response time, and occupancy of edge resources. Further, ECODA achieves a good trade-off between performance and computational complexity, and therefore it can help achieve strict latency requirements of V2X applications.