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dc.contributorQuiñones Moreno, Eduardo
dc.contributorMoretó Planas, Miquel
dc.contributorSerrano, María A.
dc.contributor.authorSabaté Creixell, Eudald
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2020-09-11T22:21:31Z
dc.date.available2020-09-11T22:21:31Z
dc.date.issued2020-06-22
dc.identifier.urihttp://hdl.handle.net/2117/328693
dc.description.abstractEdge computing is a distributed computing paradigm that shifts the computation capabilities close to the data sources. This new paradigm, coupled with the use of parallel embedded processor architectures, is becoming a very promising solution for time-sensitive distributed applications used in Internet of Things and large Cyber-Physical Systems (e.g., those used in smart cities) to alleviate the pressure on centralized solutions. However, the distribution and heterogeneity nature of the edge computing complicates the response-time analysis on these type of applications. This thesis addresses this challenge by proposing a new Directed Acyclic Graph (DAG)-task based system model to characterize: (1) the distribution nature of applications executed on the edge; and (2) the heterogeneous computation and network communication capabilities of edge computing platforms. Based on this system model, this work presents five different scheduling strategies: four sub-optimal but tractable heuristics and an optimal but costly approach based on a mixed integer linear programming (MILP), that minimize the overall response time of distributed time-sensitive applications. To address both issues, and as a proof of concept, we use COMPSs, a framework composed of a task-based programming model and a runtime used to program and efficiently distribute time-sensitive applications across the compute continuum. However, COMPSs is agnostic of time-sensitive applications, hence in this work we extend it to consider the dynamic scheduling based on the proposed scheduling strategies. Our results show that our scheduling heuristics outperform current scheduling solutions, while providing an average and upper-bound execution time comparable to the optimal one provided by the MILP allocation approach.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshReal-time data processing
dc.subject.lcshElectronic data processing -- Distributed processing
dc.subject.otheranàlisi de planificació
dc.subject.otheredge computing
dc.subject.othersistemes distribuïts
dc.subject.otheranàlisi de temps
dc.subject.otherILP
dc.subject.otherschedulability analysis
dc.subject.otheredge computing
dc.subject.otherdistributed systems
dc.subject.othertiming analysis
dc.titleScheduling strategies for time-sensitive distributed applications on edge computing
dc.typeMaster thesis
dc.subject.lemacTemps real (Informàtica)
dc.subject.lemacProcessament distribuït de dades
dc.identifier.slug153037
dc.rights.accessOpen Access
dc.date.updated2020-07-06T04:00:41Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)


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