The proliferation of latency-critical and compute-intensive edge applications is driving increases in computing demand and carbon emissions at the edge. To better understand carbon emissions at the edge, we analyze granular carbon intensity traces at intermediate "mesoscales," such as within a single US state or among neighboring countries in Europe, and observe significant variations in carbon intensity at these spatial scales. Importantly, our analysis shows that carbon intensity variations, which are known to occur at large continental scales (e.g., cloud regions), also occur at much finer spatial scales, making it feasible to exploit geographic workload shifting in the edge computing context. Motivated by these findings, we propose CarbonEdge, a carbon-aware framework for edge computing that optimizes the placement of edge workloads across mesoscale edge data centers to reduce carbon emissions while meeting latency SLOs. We implement CarbonEdge and evaluate it on a real edge computing testbed and through large-scale simulations for multiple edge workloads and settings. Our experimental results on a real testbed demonstrate that CarbonEdge can reduce emissions by up to 78.7% for a regional edge deployment in central Europe. Moreover, our CDN-scale experiments show potential savings of 49.5% and 67.8% in the US and Europe, respectively, while limiting the one-way latency increase to less than 5.5 ms.

Fully autonomous flight by low-cost, lightweight commercial off-the-shelf (COTS) drones could transform many use cases involving real-time computer vision. We show how such autonomy can be achieved using edge computing from a flight platform costing less than $800, and composed of a 320 g COTS drone with a 26 g COTS wearable device as payload. In spite of the extreme austerity of this platform and thermal limits on its LTE transmission, the system is able perform tasks such as detecting and then tracking a target. It is also able to visually navigate around obstacles. Such capabilities are only found on heavier and more expensive drones today.

Accurate, up-to-date maps of transient traffic and hazards are invaluable to drivers, city managers, and the emerging class of self-driving vehicles. We present LiveMap, a scalable, automated system for acquiring, curating, and disseminating detailed, continually-updated road conditions in a region. LiveMap leverages in-vehicle cameras, sensors, and processors to crowd-source hazard detection without human intervention. We build a real-time simulation framework that allows a mix of real and simulated components to be tested together at scale. We demonstrate that LiveMap can work well at city scales within the limits of today's cellular network bandwidth. We also show the feasibility of accurate, in-vehicle, computer-vision-based hazard detection.

We propose a non-intrusive approach for monitoring virtual machines (VMs) in the cloud. At the core of this approach is a mechanism for selective real-time monitoring of guest file updates within VM instances. This mechanism is agentless, requiring no guest VM support. It has low virtual I/O overhead, low latency for emitting file updates, and a scalable design. Its central design principle is distributed streaming of file updates inferred from introspected disk sector writes. The mechanism, called DS-VMI, enables many system administration tasks that involve monitoring files to be performed outside VMs.

This article investigates the transient use of free local storage for improving performance in VM-based mobile computing systems implemented as thick clients on host PCs. We use the term TransientPC systems to refer to these types of systems. The solution we propose, called TransPart , uses the higher-performing local storage of host hardware to speed up performance-critical operations. Our solution constructs a virtual storage device on demand (which we call transient storage ) by borrowing free disk blocks from the host’s storage. In this article, we present the design, implementation, and evaluation of a TransPart prototype, which requires no modifications to the software or hardware of a host computer. Experimental results confirm that TransPart offers low overhead and startup cost, while improving user experience.

Transient use of displays by mobile users was prophesied two decades ago. Today, convergence of a range of technologies enable the realization of this vision. For researchers in this space, one key question is where to physically locate the application for which the display has been appropriated. The emergence of cloud and cloudlet computing has increased the range of possible locations. In this paper we focus on understanding the extent to which application location impacts user experience when appropriating displays. We describe a usage model in which public displays can be appropriated to support spontaneous use of interactive applications, present an example architecture based on cloudlets, and explore how application location impacts user experience.

In this paper the motivation, design and application of a distributed blackboard architecture for interactive data visualization is discussed. The main advantages of the architecture are twofold. First, it allows visualization tools to be tightly integrated with simulations. Second, it allows qualitative and quantitative analysis to be combined during the visualization process.