Articles and Blogs from the Living Edge Lab
Hiding the Devilishness of DetailAs edge computing emerges, it introduces new complexity. Now, a user’s proximity to edge computing resources matter. And, edge computing economics and market structure mean that the edge computing provider and the resources it provides can be quite variable over time and space. For the edge-native application provider, finding the “right” edge computing node or cloudlet to serve a specific user for a specific session can be non-trivial. Here at the Living Edge Lab, we believe that this complexity calls for a new abstraction at the boundary between edge-native application providers and edge computing infrastructure providers. Our Sinfonia project, led by Jan Harkes in collaboration with Meta and ARM, focuses on the development and evaluation of a framework for this abstraction.
Nudging the Planets to AlignAt the Living Edge Lab, we’d like to have the benefits of both remote working and serendipity and believe that technology could enable this. Our recent concept paper, Balancing Privacy and Serendipity in Cyberspace, with Nigel Davies of Lancaster University and Nina Taft of Google, from this year’s HotMobile Conference explores technology approaches to bringing serendipitous chance encounters to the virtual workplace. Our question: “Can we create technology-mediated serendipity for coworkers who are not collocated?” The specific use case we examine is generating chance encounters between coworkers who are at different physical workplaces other than at-home
Opening Edge Computing’s Black BoxWe want a mobile network with round-trip latencies less than 20ms. The lower we can get, the more edge-native applications become feasible. In our first step toward continual improvement in round-trip latency, we launched the Network Latency Segmentation Project led by students Sophie Smith and Ishan Darwhekar. They implemented network measurement probes at various points in the network – at the user equipment (UE), between the Radio Access Network (RAN) and the Evolved Packet Core (EPC) and the between the EPC and the Cloudlet to measure the latency of the network at each segment.
Accelerator Calculus at the EdgeThis work investigates the use of an image decoding accelerator embedded in an edge storage system used for image analytics. Edge cameras can generate massive amounts of visual data that can’t be economically stored in an uncompressed form at the edge or fully transmitted to a backend cloud. However, city-scale video analytics requires application access to decoded data from large datasets of encoded, compressed stored images or videos at the edge.
Throwing a Softball at Search and RescuePhD student Haithem Turki of the Carnegie Mellon University Living Edge Lab has taken the search and rescue scenario as the motivation for his research work on Neural Radiance Fields or “NeRFs”, first developed at UC Berkeley in 2020. NeRFs are the newest technique for 3D scene capture and rendering. They use a type of neural network known as a multilayer perceptron (MLP) to represent the scene as a function. Once a NeRF has been trained from the input data, it embodies the 3D information contained in the scene. The rendering stage extracts the NeRF information and displays it to the user from a specific viewpoint.
The Quest for Lower Latency: Announcing the New Living Edge Lab Wireless Network
Articles and Blogs