Abstract
In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.
Highlights
Introduction and Massimo VillariNumerous Internet of Things (IoT) devices are being deployed in geo-distributed locations far outside traditional computing facilities [1,2]
To observe the costs associated with deploying a File System in Userspace (FUSE) file system, we implemented a simple FUSE application that forwards all operations to an ext4 file system, labeled FUSE passthrough in our experiment
In order to evaluate the throughput of a centralized system, we focus on the bitrate required to run inference on videos in real time and compare against the average bandwidth measured for the satellite connection
Summary
Numerous Internet of Things (IoT) devices are being deployed in geo-distributed locations far outside traditional computing facilities [1,2]. Examples include video surveillance cameras, home security devices, activity trackers, logistic tracking devices, and smart factory equipment. High volume, and heterogeneous data are continuously produced by these devices at an unparalleled scale. A key challenge is to analyze and obtain trusted, timely insight from these data streams. Distributed system architects must carefully consider structuring alternatives to centralized on-premise or public cloud services for data analysis. Moving computations closer to data sources is likely a better option than federating and centralizing all this data [3,4]
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