- author = "Mahmoud Abdulwahab K. Bokhari",
- title = "Genetic Improvement of Software for Energy Efficiency in Noisy and Fragmented Eco-Systems",
- school = "School of Computer Science, University of Adelaide",
- year = "2020",
- address = "Australia",
- month = "7 " # dec,
- keywords = "genetic algorithms, genetic programming, genetic improvement, search based software engineering, SBSE, genetic improvement of software, non-functional properties, energy efficiency, battery optimisation, deep parameter optimisation, mobile computing, Android, validation approach, R-3 validation approch",
-
URL = "
http://hdl.handle.net/2440/130174",
-
URL = "
https://digital.library.adelaide.edu.au/dspace/bitstream/2440/130174/1/Bokhari2020_PhD.pdf",
- size = "166 pages",
-
abstract = "Software has made its way to every aspect of our daily
life. Users of smart devices expect almost continuous
availability and uninterrupted service. However, such
devices operate on restricted energy resources. As
energy efficiency of software is relatively a new
concern for software practitioners, there is a lack of
knowledge and tools to support the development of
energy efficient software. Optimising the energy
consumption of software requires measuring or
estimating its energy use and then optimising it.
Generalised models of energy behaviour suffer from
heterogeneous and fragmented eco-systems (i.e. diverse
hardware and operating systems). The nature of such
optimisation environments favours in-vivo optimisation
which provides the ground-truth for energy behaviour of
an application on a given platform. One key challenge
in in-vivo energy optimisation is noisy energy
readings. This is because complete isolation of the
effects of software optimisation is simply infeasible,
owing to random and systematic noise from the
platform.
we explore in-vivo optimisation using Genetic Improvement of Software (GI) for energy efficiency in noisy and fragmented eco-systems.
First, we document expected and unexpected technical challenges and their solutions when conducting energy optimisation experiments. This can be used as guidelines for software practitioners when conducting energy related experiments.
Second, we demonstrate the technical feasibility of in-vivo energy optimisation using GI on smart devices. We implement a new approach for mitigating noisy readings based on simple code rewrite.
Third, we propose a new conceptual framework to determine the minimum number of samples required to show significant differences between software variants competing in tournaments. We demonstrate that the number of samples can vary drastically between different platforms as well as from one point of time to another within a single platform. It is crucial to take into consideration these observations when optimising in the wild or across several devices in a control environment.
Finally, we implement a new validation approach for energy optimisation experiments. Through experiments, we demonstrate that the current validation approaches can mislead software practitioners to draw wrong conclusions. Our approach outperforms the current validation techniques in terms of specificity and sensitivity in distinguishing differences between validation solutions.",
- notes = "Supervisor: Markus Wagner Co-Supervisor: Bradley Alexander",
