Joshua Adkins

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-12

January 17, 2023

Cloud computing revolutionized the ease with which we can build, deploy, and scale distributed computing services. These advances, however, have not extended to the physically distributed and resource-constrained computers deployed throughout the world to collect data, and their resource constraints have thus far confined them to function as inefficient, fixed-purpose data forwarders. Breaking these distributed sensors free of their resource-constraints by including them in a dynamic, programmable, distributed system will not only enable easier deployment and scaling of applications relying on their data, but it will also give us the ability to collect and process never-before-seen data and discover new ways sensing the world around us.

We enable this vision in two parts. First we present a signpost-based platform which eases the building and deployment of sensors by providing the core services and hardware necessary for them to function. Next we explore the benefits of, and build a resource manager to form these resource-constrained sensors into a compute cluster akin to those found in the cloud. This enables multiple users to simultaneous program a cluster of sensors and quickly iterate on their programs through an application framework which abstracts away the details of scheduling and task distribution. By forming these sensors into a multiprogrammable cluster, we enable them to be accessed as a shared sensing utility rather than as a collection of individual nodes.

Advisors: John Wawrzynek and Prabal Dutta

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BibTeX citation:

@phdthesis{Adkins:EECS-2023-12,
    Author= {Adkins, Joshua},
    Title= {Resource-Constrained Sensing as a Shared Utility},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {Jan},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-12.html},
    Number= {UCB/EECS-2023-12},
    Abstract= {Cloud computing revolutionized the ease with which we can build, deploy, and scale distributed computing services. These advances, however, have not extended to the physically distributed and resource-constrained computers deployed throughout the world to collect data, and their resource constraints have thus far confined them to function as inefficient, fixed-purpose data forwarders. Breaking these distributed sensors free of their resource-constraints by including them in a dynamic, programmable, distributed system will not only enable easier deployment and scaling of applications relying on their data, but it will also give us the ability to collect and process never-before-seen data and discover new ways sensing the world around us.

We enable this vision in two parts. First we present a signpost-based platform which eases the building and deployment of sensors by providing the core services and hardware necessary for them to function. Next we explore the benefits of, and build a resource manager to form these resource-constrained sensors into a compute cluster akin to those found in the cloud. This enables multiple users to simultaneous program a cluster of sensors and quickly iterate on their programs through an application framework which abstracts away the details of scheduling and task distribution. By forming these sensors into a multiprogrammable cluster, we enable them to be accessed as a shared sensing utility rather than as a collection of individual nodes.},
}

EndNote citation:

%0 Thesis
%A Adkins, Joshua 
%T Resource-Constrained Sensing as a Shared Utility
%I EECS Department, University of California, Berkeley
%D 2023
%8 January 17
%@ UCB/EECS-2023-12
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-12.html
%F Adkins:EECS-2023-12