Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus --- a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by ~4x and the energy consumption by ~77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.@en

Unikernels are a new lightweight virtualization technology born as an alternative to virtual machines and containers. Geared towards service provisioning for the Internet of Things (IoT) and edge computing, they offer extremely small memory footprint and strong isolation properties. However, the unikernels ecosystem is still in its infancy and lacks quintessential functionalities found in more well-established virtualization technologies. For example, stateful migration is a highly desired feature for mobile edge services in distributed environments which is not yet supported by unikernels. This is one of the shortcomings preventing us from reaping the full benefits of unikernels outside of stateless applications. In this work, we aim bridging this gap with MirageManager: A ready-to-deploy unikernel migration system enabling lossless migration supported by a function-level, application logic check-pointing library of our design. Our evaluation results show that MirageManager is able to lower the service downtime by ∼80%, and drastically reduce the state transfer data by almost ∼100% when comparing against Podman. Additionally, MirageManager also beats Podman, a container-based engine, in parallel service migration across constrained edge networks reducing the overall migration time by up to ∼6x. @en

For road safety, detecting and reacting efficiently to road hazards is crucial and yet challenging due to practical restrictions such as limited data availability, which relies on network support. Moreover, from a system perspective we lack a computational model capable of providing to vehicles reliable and real-time assessment of the road context. As autonomous vehicles become widespread, the safety issues are further aggravated by the gap between cloud, roadside infrastructure and road users in terms of communication latency, software-hardware compatibility and data interoperability. To tackle this, we present ECCO: an orchestration framework that enables edge-cloud collaborative computing for road context assessment. ECCO can create on-demand task execution pipelines spanning multiple, potentially resource-constrained edge-nodes with the smart IoT infrastructure support. Our prototype lays the groundwork to support new services, which can use more efficiently the road infrastructure and deliver safety-critical applications for road users.

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Detecting and reacting efficiently to road condition hazards are challenging given practical restrictions such as limited data availability and lack of infrastructure support. In this paper, we present an edge-cloud chaining solution that bridges the cloud and road infrastructures to enhance road safety. We exploit the roadside infrastructure (e.g., smart lampposts) to form a processing chain at the edge nodes and transmit the essential context to approaching vehicles providing what we refer as road fingerprinting. We approach the problem from two angles: first we focus on semantically defining how an execution pipeline spanning edge and cloud is composed, then we design, implement and evaluate a working prototype based on our assumptions. In addition, we present experimental insights and outline open challenges for next steps.

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Augmented reality has been exploited in manifold fields but is yet to be used at its full potential. With the massive diffusion of smart devices, opportunities to build immersive human-computer interfaces are continually expanding. In this study, we conceptualize a virtual factory: an interactive, dynamic, holographic abstraction of the physical machines deployed in a factory. Through our prototype implementation, we conducted a user-study driven evaluation of holographic interfaces compared to traditional interfaces, highlighting its pros and cons. Our study shows that the majority of the participants found holographic manipulation more attractive and natural to interact with. However, current performance characteristics of head-mounted displays must be improved to be applied in production.@en