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Access Type

WSU Access

Date of Award

January 2019

Degree Type


Degree Name



Industrial and Manufacturing Engineering

First Advisor

Kai . Yang

Second Advisor

Daniel . Grosu


Mobile Edge Computing (MEC) is the latest technology introduced with the aim of

reducing the response time of mobile applications by allowing them to perform their computation

at the edge of the network. Efficient resource allocation on edge servers is one

of the main challenges in edge computing. Due to the mobility of users, a poor allocation

might impose high execution costs. Computation offloading in edge computing has to

consider several issues that were not present in the data-center or cloud computing settings.

After the initial placement, mobile users may move to different locations. Therefore, an optimal

offloading decision made at the time of receiving a request may not remain optimal

for the whole duration of user’s application execution. In addition to this, the availability

of servers’ resources may change over time. Therefore, an efficient offloading algorithm

must be adaptive to this dynamic setting. When it comes to practice, there are lots of

uncertainties in the network that make optimal decision making demanding.

In this dissertation, we develop stochastic programing methods for resource management

in edge computing systems. We aim at developing novel models which consider

important nondeterministic parameters in edge computing systems, such as task arrivals,

mobility of users, and resource requirements of mobile applications. Another concentration

of this research is to design efficient solution algorithms which are able to give optimal

on near optimal solutions in a reasonable amount of time.

We address a very important problem in the management of MEC systems, that is, the

problem of finding an efficient application placement on the edge servers such that the cost

of execution is minimized. We develop a multi-stage stochastic programming model for

the application placement problem in edge computing systems and design a novel parallel

greedy algorithm based on the Sample Average Approximation (SAA) method to solve it.

In our experimental analysis, we use the random walk method to model the mobility of

users. We also model the problem of energy-aware application placement in edge computing

systems as a multi-stage stochastic program, where the objective is to maximize

the QoS of the system while taking into account the limited energy budget of the edge

servers. To solve the problem, we employ a parallel SAA algorithm, and conduct an extensive

experimental analysis to evaluate the performance of the proposed algorithm using

real-world trace data for the mobility of users. We also propose a risk-based optimization

method based on chance-constrained programming for application placement in MEC systems,

where the objective is to maximize the total QoS of the system. In this model the

resource requirements of applications is assumed to be a nondeterministic parameter. We

use SAA to solve the chance-constrained program, and develop a learning-based algorithm

to solve the SAA model.

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