Promoting Systematic Practices for Designing and Developing Edge Computing Applications via Middleware Abstractions and Performance Estimation

Simple item page
dc.contributor.authorDantas Cruz, Brenoen
dc.contributor.committeechairTilevich, Elien
dc.contributor.committeememberServant Cortes, Francisco Javieren
dc.contributor.committeememberMcCrickard, D. Scotten
dc.contributor.committeememberMatilla Montalvo, Rafaelen
dc.contributor.committeememberMeng, Naen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2021-04-10T08:00:18Zen
dc.date.available2021-04-10T08:00:18Zen
dc.date.issued2021-04-09en
dc.description.abstractMobile, IoT, and wearable devices have been transitioning from passive consumers to active generators of massive amounts of user-generated data. Edge-based processing eliminates network bottlenecks and improves data privacy. However, developing edge applications remains hard, with developers often have to employ ad-hoc software development practices to meet their requirements. By doing so, developers introduce low-level and hard-to-maintain code to the codebase, which is error-prone, expensive to maintain, and vulnerable in terms of security. The thesis of this research is that modular middleware abstractions, exemplar use cases, and ML-based performance estimation can make the design and development of edge applications more systematic. To prove this thesis, this dissertation comprises of three research thrusts: (1) understand the characteristics of edge-based applications, in terms of their runtime, architecture, and performance; (2) provide exemplary use cases to support the development of edge-based application; (3) innovate in the realm of middleware to address the unique challenges of edge-based data transfer and processing. We provide programming support and performance estimation methodologies to help edge-based application developers improve their software development practices. This dissertation is based on three conference papers, presented at MOBILESoft 2018, VTC 2020, and IEEE SMDS 2020.en
dc.description.abstractgeneralMobile, IoT, and wearable devices are generating massive volumes of user data. Processing this data can reveal valuable insights. For example, a wearable device collecting its user's vitals can use the collected data to provide health advice. Typically the collected data is sent to some remote computing resources for processing. However, due to the vastly increasing volumes of such data, it becomes infeasible to efficiently transfer it over the network. Edge computing is an emerging system architecture that employs nearby devices for processing and can be used to alleviate the aforementioned data transfer problem. However, it remains hard to design and develop edge computing applications, making it a task reserved for expert developers. This dissertation is concerned with democratizing the development of edge applications, so the task would become accessible for regular developers. The overriding idea is to make the design and implementation of edge applications more systematic by means of programming support, exemplary use cases, and methodologies.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:29645en
dc.identifier.urihttp://hdl.handle.net/10919/103010en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEdge Computingen
dc.subjectProgramming Modelen
dc.subjectData Sharingen
dc.subjectAndroiden
dc.subjectPerformance Estimationen
dc.subjectMachine learningen
dc.titlePromoting Systematic Practices for Designing and Developing Edge Computing Applications via Middleware Abstractions and Performance Estimationen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dantas_Cruz_B_D_2021.pdf
Size:
10.88 MB
Format:
Adobe Portable Document Format
Download

Collections

Doctoral Dissertations