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 results delivery due to pending interest table (PIT) timer expiration resulting in unsolicited packet drops. Moreover, voluminous data transmission in resource-constrained environment may prevent the consumer from receiving timely results. 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 the YOLO and convolutional neural networks (CNN) and places it between the caching and forwarding module of NDN. The ST extracts and forwards semantically meaningful information, thereby 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.

This paper presents the design and implementation of a multilayered microservices-centric in-network computation framework tailored for the Internet of Vehicular Networks. The proposed framework integrates a vehicular network layer, Edge computing layer, and Cloud to enhance network efficiency and minimize latency for time-critical vehicular applications. In the proposed work, modifications were made to the NDNsim codebase, integrating the C++ Boost Asio library to facilitate NDNsim-Edge communication for designated microservice computation. in additions, a microservice-centric driver assistance application is also emulated in Dotnet core with microservices deployed on various Edge and cloud servers. Moreover, Restful APIs have been developed, allowing the NDNsim layer, physical Edge layer, and Cloud stations to offload a microservice-centric computation request and retrieve the corresponding compute outcomes. Furthermore, the Entity framework is utilized to ensure proper tracking and management of the requests while an HTML-based user interface is developed for a visual representation of the request pattern. Extensive testbed experimentation revealed that the proposed framework highly optimized bandwidth consumption, reduces latency and amplifies the computed results delivery ratio compared with the conventional monolithic system.

The advancement of vehicular onboard units (OBUs) has led to compute-intensive and delay-sensitive vehicular applications. Undeniably edge-assisted static roadside computing terminal (sRCT) offers immediate computations, a surge of smart vehicles and intensive computation requests during crowded hours may overload the sRCT, leading to performance degradation and intolerable delays. Therefore, to facilitate proximate computations and achieve ultra-low latency, this article envisions a Consortium of mobile vehicular Fog, Edge, and Cloud (CFEC) an ultra-low latency microservices-centric in-network computing framework for vehicular Named Data networks (VNDN). CFEC develops a fog-profiler-assisted mobile vehicular fog based on vehicles’ mobility patterns and available resource characteristics to ensure reliable computation offloading and reverse-path stability in a dynamic vehicular environment. Furthermore, CFEC introduces an intermediary ZTMC controller that effectively filters out underutilized sRCTs and routes computation requests to nearby, filtered sRCTs, thus minimizing transmission time and accelerating computations even during crowded hours. Simulations results revealed that CFEC significantly reduces computational satisfaction delays by up to 32.5%, 48.5%, and 31.9%, 51.025% against varying interest and node rates, respectively while in extreme traffic conditions, CFEC achieved an impressive computation satisfaction ratio of around 85% compared with benchmark schemes.