AI-Driven Workload Balancing and Sign Language Interpretation in Vehicular Edge– Cloud Pipelines with Microservices and Containerization
DOI:
https://doi.org/10.15662/IJRAI.2023.0605004Keywords:
AI workload balancing, Vehicular edge-cloud systems, Microservices, Containerization, AI orchestration, Real-time inference, Resource optimization, Edge computing, Intelligent transportation systems, Distributed AI, Sign Language InterpretationAbstract
Vehicular edge-cloud systems are critical for enabling intelligent transportation applications such as autonomous driving, traffic management, and vehicle-to-everything (V2X) communication. These systems face challenges in handling heterogeneous data streams, dynamic workloads, and latency-sensitive tasks across distributed edge and cloud resources. This paper proposes an AI-powered workload balancing framework within microservices-based and containerized pipelines for vehicular edge-cloud environments. The architecture decomposes AI workflows into modular, containerized microservices that can be dynamically orchestrated to optimize resource utilization, reduce processing latency, and maintain service reliability. An AI-driven orchestration engine monitors system performance, predicts workload fluctuations, and automatically redistributes tasks across edge and cloud nodes. Experimental evaluations demonstrate significant improvements in throughput, latency, and overall system efficiency compared to static workload allocation strategies. This research highlights the potential of integrating AI orchestration, microservices, and containerization to enable scalable, resilient, and high-performance vehicular edge-cloud infrastructures.
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