Joshua Adkins

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-12

January 17, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-12.pdf

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