Interdependent microservice offloading and semantic aware results transmission for 6G vehicular edge networks

Microservices-centric in-network computations, coupled with Named Data Networking (NDN) and supported by vehicular edge computing (VEC), offer a promising platform to meet the requirements of vehicular applications. However, a significant obstacle hindering high efficiency is the interdependency of microservices (MS), which may delay the timely delivery of results due to Pending Interest Table (PIT) timer expiration, resulting in unsolicited packet drops. Moreover, voluminous data transmission in a resource-constrained environment may prevent the consumer from receiving results on time. Therefore, to avoid unsolicited computation losses and enable proximate computations, this article envisions interdependent microservices offloading and semantic aware results transmission (iMSoRT) for 6G vehicular edge networks. iMSoRT formulates an efficient strategy that allows traffic controller (Tc) to account for MS interdependencies and allocate the PIT timer based on computational and network resource requirements. Moreover, iMSoRT introduces a live forwarding information base (lFIB) that effectively filters out underutilized RSU-E servers and forwards computation requests to the optimal RSU-E, accelerating processing while minimizing communication costs. Furthermore, iMSoRT develops a Semantic Transformer (ST) that leverages YOLO and Convolutional Neural Networks (CNNs), placing it between the caching and forwarding modules of NDN. The ST extracts and forwards semantically meaningful information, thus reducing network resource utilization and enhancing information exchange efficiency. Simulation results revealed that iMSoRT achieved an impressive compute-hit ratio of over 90%, restrict cloud offloading to 12%, reduced computation delays over 50%, and optimized bandwidth utilization over threefold compared with benchmark schemes.