<div class="csl-bib-body">
<div class="csl-entry">Rausch, T. (2021). <i>A distributed compute fabric for edge intelligence</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.93560</div>
</div>
-
dc.identifier.uri
https://doi.org/10.34726/hss.2021.93560
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/18309
-
dc.description.abstract
Edge intelligence is a post-cloud computing paradigm, and a consequence of the past decade of developments in Artificial Intelligence (AI), Internet of Things (IoT), and human augmentation. At the intersection of these domains, new applications have emerged that require real-time access to sensor data from the environment, low-latency AI model inferencing, or access to data isolated in edge networks for training AI models, all while operating in highly dynamic and heterogeneous computing environments. These requirements have profound implications on the scale and design of supporting computing platforms that are clearly at odds with the centralized nature of cloud computing. Instead, edge intelligence necessitates a new operational layer that is designed for the characteristics of AI and edge computing systems. This layer weaves both cloud and federated edge resources together using appropriate platform abstractions to form a distributed compute fabric. The main goal of this thesis is to examine the associated challenges, and to provide evidence for the efficacy of the idea. To that end, we develop new system evaluation methodologies and two orthogonal systems: an elastic message-oriented middleware, and a serverless edge computing platform. From a static centralized deployment in the cloud, we bootstrap a network of brokers that diffuse to the edge based on resource availability, and the number of clients and their proximity to edge resources. The system continuously optimizes communication latencies by monitoring client–broker proximity, and reconfiguring connections as necessary. Our serverless platform builds on existing container orchestration systems. The core is a custom scheduler that can make tradeoffs between data and computation movement, and is aware of workload heterogeneity and device capabilities such as GPUs. Furthermore, we demonstrate a method to automatically fine-tune scheduler parameters and optimize high-level operational goals.
en
dc.language
English
-
dc.language.iso
en
-
dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
-
dc.subject
Edge Computing
en
dc.subject
Cloud Computing
en
dc.subject
Artificial Intelligence
en
dc.subject
Serverless Computing
en
dc.subject
Middleware
en
dc.subject
Internet of Things
en
dc.title
A distributed compute fabric for edge intelligence
en
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2021.93560
-
dc.contributor.affiliation
TU Wien, Österreich
-
dc.rights.holder
Thomas Rausch
-
dc.publisher.place
Wien
-
tuw.version
vor
-
tuw.thesisinformation
Technische Universität Wien
-
tuw.publication.orgunit
E194 - Institut für Information Systems Engineering
-
dc.type.qualificationlevel
Doctoral
-
dc.identifier.libraryid
AC16310361
-
dc.description.numberOfPages
187
-
dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0001-5988-9041
-
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
-
tuw.advisor.orcid
0000-0001-6872-8821
-
item.languageiso639-1
en
-
item.mimetype
application/pdf
-
item.openairecristype
http://purl.org/coar/resource_type/c_db06
-
item.fulltext
with Fulltext
-
item.openairetype
doctoral thesis
-
item.grantfulltext
open
-
item.openaccessfulltext
Open Access
-
item.cerifentitytype
Publications
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.orcid
0000-0001-5988-9041
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering