Ali Saman Tosun’s research while affiliated with University of North Carolina at Pembroke and other places

Edge computing is emerging as a key enabler of low-latency, high-efficiency processing for the Internet of Things (IoT) and other real-time applications. To support these demands, containerization has gained traction in edge computing due to its lightweight virtualization and efficient resource management. However, there is currently no established framework to leverage both containers and unikernels on edge devices for optimized IoT deployments. This paper proposes a hybrid edge system design that leverages container and unikernel technologies to optimize resource utilization based on application complexity. Containers are employed for resource-intensive applications, e.g., computer vision, providing faster processing, flexibility, and ease of deployment. In contrast, unikernels are used for lightweight applications, offering enhanced resource performance with minimal overhead. Our system design also incorporates container orchestration to efficiently manage multiple instances across the edge efficiently, ensuring scalability and reliability. We demonstrate our hybrid approach's performance and efficiency advantages through real-world computer vision and data science applications on ARM-powered edge device. Our results demonstrate that this hybrid approach improves resource utilization and reduces latency compared to traditional virtualized solutions. This work provides insights into optimizing edge infrastructures, enabling more efficient and specialized deployment strategies for diverse application workloads.

The rapid expansion of IoT devices and their real-time applications have driven a growing need for edge computing. To meet this need, efficient and secure solutions are required for running such applications on resource-constrained devices with limited power, CPU, and memory. Unikernel, with its minimalistic design and application-specific approach, offers a promising alternative to traditional virtualization and container technologies in these environments. The existing research does not thoroughly examine the feasibility of using unikernel for edge computing. This paper investigates the potential of unikernel for ARM-powered edge computing by evaluating the performance and efficiency of three prominent unikernel systems such as OSv, Nanos, and Unikraft against Docker container. We experiment with real-world edge computing applications and utilize key metrics such as boot time, execution time, memory usage, CPU overhead, and network performance to determine how unikernel performs under the constraints of edge devices. Our findings reveal the potential advantages of unikernel in terms of reduced resource consumption and faster startup times while highlighting areas where they may need further optimization for edge deployment. This study provides valuable insights for researchers and practitioners considering unikernel as a lightweight, efficient solution for edge computing on ARM architectures.

In the evolving tech landscape, various container technologies coexist and offer compelling advantages in quickly deploying applications and efficiently utilizing resources on edge devices. Despite the potential benefits of containerization in general, limited research has explored how various container technologies perform in specific domains. In response, this paper provides an extensive evaluation of container technologies (e.g., RunC, LXC, Containerd, Docker, Podman, and Singularity) in the context of OpenCV-based computer vision applications on ARM-based edge devices. Experiments verify that the performance of containerized computer vision applications is comparable to that of non-containerized ones. While the performance is roughly equivalent across all container runtimes/engines, Docker consistently demonstrates superior efficiency for computer vision applications on ARM-based edge devices. These insights contribute to bridge the existing gap to the integration of containers in IoT and ARM-based edge computing scenarios.

IoT edge computing is a network design model that captures and processes data at the network edge. The results are forwarded to a cloud service or, if additional processing is needed, a middle tier. By processing data at the edge and middle tier, edge networks achieve better load-balancing and improve performance; however, traditional edge network deployments represent a rigid participation model. Edge networks require physical access to an IoT device and often lock the device to a single edge network. These constraints make it difficult to construct the ideal network, as they reject IoT devices deployed at the network edge but not owned by the network administrator. Our goal is to remove these limitations by creating a network protocol that supports broader participation of IoT devices, cryptographically secures network data, and improves network performance by increasing captured data at the network edge. The protocol is named Snap to symbolize the ease of self assembly. Our experimental research focuses on temperature stability and the cycle efficiency of an HVAC system by utilizing a Snap network to combine two existing edge networks and increase the number of temperature measurement points. The additional measurement points improved the efficiency of the HVAC cycle strategy by increasing the square footage of measured building space. The additional temperature capture points supported an adjustment to the HVAC cycle strategy which resulted in reducing the disparity between the requested temperature and the resulting temperatures. Snap networks support a broader range of IoT sensors leading to increased measurement density, sample rate frequency, and coverage of the network edge.