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Vehicular edge computing relies on the computational capabilities of interconnected edge devices to manage incoming requests from vehicles. This offloading process enhances the speed and efficiency of data handling, ultimately boosting the safety, performance, and reliability of connected vehicles. While previous studies have concentrated on processor characteristics, they often overlook the significance of the connecting components. Limited memory and storage resources on edge devices pose challenges, particularly in the context of deep learning, where these limitations can significantly affect performance. The impact of memory contention has not been thoroughly explored, especially regarding perception-based tasks. In our analysis, we identified three distinct behaviors of memory contention, each interacting differently with other resources. Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding 2849%, while activation layers showed a rise of 1173.34%. Through our characterization efforts, we can model workload behavior on edge devices according to their configuration and the demands of the tasks. This allows us to quantify the effects of memory contention. To our knowledge, this study is the first to characterize the influence of memory on vehicular edge computational workloads, with a strong emphasis on memory dynamics and DNN layers. 

Microservices have emerged as a strong architecture for large-scale, distributed systems in the context of cloud computing and containerization. However, the size and complexity of microservice systems have strained current access control mechanisms. Intricate dependency structures, such as multi-hop dependency chains, go uncaptured by existing access control mechanisms and leave microservice deployments open to adversarial actions and influence. This work introduces CloudCover, an access control mechanism and enforcement framework for microservices. CloudCover provides holistic, deployment-wide analysis of microservice operations and behaviors. It implements a verificationin-the-loop access control approach, mitigating multi-hop microservice threats through control-flow integrity checks. We evaluate these domain-relevant multi-hop threats and CloudCover under existing, real-world scenarios such as Istio’s opensource microservice example and under theoretic and synthetic network loads of 10,000 requests per second. Our results show that CloudCover is appropriate for use in real deployments, requiring no microservice code changes by administrators