Energy-Aware Offloading of Containerized Tasks in Cloud Native V2X Networks

Abstract

In cloud-native environments, executing vehicle-to-everything (V2X) tasks in edge nodes close to users significantly reduces service end-to-end latency. Containerization further reduces resource and time consumption, and, subsequently, application latency. Since edge nodes are typically resource and energy-constrained, optimizing offloading decisions and managing edge energy consumption is crucial. However, the offloading of containerized tasks has not been thoroughly explored from a practical implementation perspective. This paper proposes an optimization framework for energy-aware offloading of V2X tasks implemented as Kubernetes pods. A weighted utility function is derived based on cumulative pod response time, and an edge-to-cloud offloading decision algorithm (ECODA) is proposed. The system's energy cost model is derived, and a closed-loop repeated reward-based mechanism for CPU adjustment is presented. An energy-aware (EA)-ECODA is proposed to solve the offloading optimization problem while adjusting CPU usage according to energy considerations. Simulations show that ECODA and EA-ECODA outperform first-in, first-served (FIFS) and EA-FIFS in terms of utility, average pod response time, and resource usage, with low computational complexity. Additionally, a real testbed evaluation of a vulnerable road user application demonstrates that ECODA outperforms Kubernetes vertical scaling in terms of service-level delay. Moreover, EA-ECODA significantly improves energy usage utility.