Articles and Blogs from the Living Edge Lab
A Ladder to the Shoulders of GiantsMuch of today’s scientific research depends on digital laboratories – computing hardware and software that process the experimental data gathered by researchers. Yet, hardware and software evolve at a rapid pace and reproducing the results from a past experiment done even a couple of years ago is a tenuous proposition. Unless the original processing environment is maintained intact, even the original researchers may not be able to reproduce the exact results. And, the difficulty in creating an identical processing environment may preclude independent researchers from verifying the results of their peers. Our recent paper, Towards Reproducible Execution of Closed-Source Applications from Internet Archives, recounts the history of our ten year effort, known as Project Olive, to address the problem of processing environment archiving in service of reproducibility. It also discusses our recent work in this area which expands the problem of archiving to the problem of archive access – making it easy for a researcher to access and run the processing environment while still protecting the software and data from unauthorized use.
Safekeeping Faces at the EdgeAs so often happens, we can create technology far faster than our social and legal systems can adapt. And, as is so often necessary, we must adapt technology to meet compelling societal needs as they emerge. For the ethical questions raised by face recognition, a more direct intervention to align with emerging norms and policies will likely be required. But, there are legitimate uses for the implicated technologies. Interventions shouldn’t excessively impede these legitimate uses. We started the Silent Witness project at the Living Edge Lab in anticipation of these requirements. We believe that, while norms and policies will vary around the world, four computer vision technology principles will be common.
Easing the Pain of Creating AnywhereThe Covid-19 pandemic forced most members of the university community to work from their homes and other locations with highly variable last-mile connectivity. Since access to campus was limited, they also became more dependent on Virtual Andrew to perform their academic and administrative work. The change from on-campus LAN-access to off-campus WAN access exposed limitations of VDI as a remote service. These limitations led to a three-way collaboration between VMware, the CMU Living Edge Lab, and CMU Computing Service to investigate how Edge Computing could enable a better VDI experience. Our recently published technical report, Deploying Edge-based Virtual Desktop Infrastructure, details this collaboration (so far).
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 NetworkIn 2017 to support our edge computing research, we built our first private LTE network. Working with Crown Castle and DT, we created a small 4G LTE network using an experimental Band 42 3.5GHz license. The network had three outdoor sites and provided limited coverage near the Carnegie Mellon University campus. While we were able to get better latencies than from commercial carriers, the number and types of commercially available devices for that frequency band, limited coverage, and limited support for the equipment made it difficult to use for much of our edge-native application research. Fast forward to late-2020. By this time, unlicensed GAA CBRS spectrum was available and commercial 4G LTE network and user equipment for this frequency band was increasingly available. We began a plan with our partners to upgrade the existing network to a modern, supportable network in the CBRS band, covering a broader geographic area with lower latency and greater throughput.
Articles and Blogs