Service Migration in Edge Computing Environments for Connected Autonomous Vehicles

Abstract

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

Latency-Aware and Proactive Service Placement for Edge Computing

“…Operating within smart cities, autonomous cars continuously exchange vital information with their external environment, supporting the latency-sensitive embedded applications, particularly the decision-making system, in responding to incoming data. The vast data generated by the cars' embedded sensors may exceed the processing capabilities of the onboard systems, leading to challenges in meeting low-latency requirements [11].…”

“…Autonomous vehicles, equipped with extensive sensor arrays, generate large volumes of real-time data during their journeys, which may exceed the capabilities of their onboard systems to process promptly under low-latency conditions [11]. To address this challenge, one viable scenario involves offloading specific computational tasks associated with vehicle control to external edge nodes.…”

“…Given the substantial data volumes the autonomous vehicle generates, which on-board systems may struggle to analyze within low-latency constraints [11], exploring alternative strategies for relocating other modules while ensuring continuous functionality warrants consideration.…”

“…Authors in [11] propose a service migration and resource manager for Vehicular Edge Computing that ensures the necessary vehicle resources. Authors in [15] introduce an intelligent service migration and resource management algorithm in edge-enabled environments that considers the latency constraint.…”

“…It is unclear whether they address recovery through degradation prediction. Authors in [11] and [15] tackle network dynamicity and guarantee recovery but do not address the mobility of end-users and edge nodes. This paper distinguishes itself by assuming network dynamicity, focusing on QoS latency degradation, and addressing the mobility of end-users and edge nodes, including recovery through degradation prediction.…”