Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Aditya Khune
M.S. Final
Mar 20, 2017, 3:30 pm - 5:00 pm
ECE Conference Room
Abstract: Offloading mobile computations is an
innovative technique that is being
explored by researchers for reducing
energy consumption in mobile devices
and for achieving better application
response time. Offloading refers to
the act of transferring computations
from a mobile device to servers in the
cloud. There are many challenges in
this domain that are not dealt with
effectively yet, and thus offloading is
far from being adopted in the design
of current mobile architectures. We
believe that there is a need to verify
the effectiveness of computation
offloading in terms of both response
time and energy consumption, to
highlight its potential in real
smartphone applications. The effect
of varying network technologies such
as 3G, 4G, and Wi-Fi on the
performance of offloading systems is
also a major concern that needs to be
addressed. In this thesis, we study the
behavior of a set of real smartphone
applications, in both local and offload
processing modes. Our experiments
identify the advantages and
disadvantages of offloading for
various mobile networks. Further, we
propose a middleware framework that
uses Reinforcement Learning to make
reward-based offloading decisions
effectively. Our framework allows a
smartphone to consider suitable
contextual information to determine
when it makes sense to offload, and
to select between available networks
(3G, 4G, or Wi-Fi) when offloading
mode is active. We tested our
framework in both simulated and real
environments, across various
applications, to demonstrate how
energy consumption can be
minimized in mobile systems that are
capable of supporting offloading.
Adviser: Dr. Sudeep Pasricha
Co-Adviser: N/A
Non-ECE Member: Dr. Bob Gesumaria
Member 3: Dr. Anura Jayasumana
Addional Members: N/A
contact for more info
Program of Study:
CS 545
CS 581 A1
ECE 531
ECE 554
ECE 561
ECE 572
Grad 511
Math 560