Edge service placement and workload allocation under uncertainty
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Cheng, Jiaming
issued: 2021-09-09T07:00:00Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Edge computing (EC) has emerged as a promising architecture for hosting critical services with stringent latency and performance requirements, challenging to address in traditional cloud computing (CC) systems. EC makes distributed computation and storage resources close to end-users, providing low-latency and high-capacity services. Notable use cases of EC include real-time data analytic services, manufacturing automation, and computational offloading for the Internet of Things. Despite the tremendous potential, EC is still in its infancy stage, and many open problems remain to be solved. This thesis lies in the intersection of operations research and network economics, with a specific focus on developing mathematical models for decision-making and economic analysis of edge-cloud network systems. To support rapid response to incidents in EC, we propose a novel resilience-aware edge service placement and workload allocation model that jointly captures the uncertainty of resource demand and node failures. The salient feature of the proposed model identifies the optimal placement and procurement solutions that can hedge against all uncertain realizations of the traffic demand within an uncertainty set. Hence, it enables service providers to balance the trade-off between the operating cost and service quality while considering demand uncertainty and node failures. Furthermore, by leveraging the column-and-constraint generation (CCG) method, we introduce two iterative algorithms that can converge to an exact optimal solution within a finite number of iterations. We further suggest an affine decision rule (ADR) approximation approach for solving large-scale problem instances in a reasonable time. Extensive numerical results then demonstrate the advantages of the proposed model and solutions.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79693
note: Edge Service Placement and ResourceAllocation Under UncertaintybyJiaming ChengB.Sc., University of Alberta, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2021© Jiaming Cheng 2021The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the dissertation entitled:Edge Service Placement and Resource Allocation Under Uncertaintysubmitted by Jiaming Cheng in partial fulfillment of the requirements forthe degree of Master of Applied Sciencein Electrical and Computer EngineeringExamining Committee:Dr. Vijay K. Bhargava, Professor, Electrical and Computer Engineering, UBCSupervisorDr. Vincent W.S. Wong, Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberDr. Mohammad Shahrad, Assistant Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberiiAbstractEdge computing (EC) has emerged as a promising architecture for hosting critical services withstringent latency and performance requirements, challenging to address in traditional cloudcomputing (CC) systems. EC makes distributed computation and storage resources close toend-users, providing low-latency and high-capacity services. Notable use cases of EC includereal-time data analytic services, manufacturing automation, and computational offloading forthe Internet of Things. Despite the tremendous potential, EC is still in its infancy stage, andmany open problems remain to be solved. This thesis lies in the intersection of operationsresearch and network economics, with a specific focus on developing mathematical models fordecision-making and economic analysis of edge-cloud network systems.To support rapid response to incidents in EC, we propose a novel resilience-aware edge ser-vice placement and worklo…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0401963
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Bhargava, Vijay K., 1948-
type: Text
identifier URI: http://hdl.handle.net/2429/79693