Since the turn of the century, the proliferation of virtualization and cloud computing
has led to an increase in data centres and consequently an increase in power consumption for computing. Today, approximately 2% of global energy consumption is
attributed to data centres alone. As a result, optimizing power usage effectiveness
in enterprise data centres has become a laudable goal and a critical requirement in
IT operations all over the world. While a significant body of research exists to measure, monitor, and control the “greenness” level of hardware components, significant
research is needed to relate hardware energy consumption to energy consumption
stemming from (software) program execution. In this dissertation, we argue that
the true energy cost of program execution must focus on the digital ecosystem within
which a particular software program is executed. We investigate the interplay between
energy consumption, task scheduling and execution decision making using dynamic
runtime models of digital ecosystems based on the execution context of software.
Single instances of software applications are no longer confined to a single device or machine. Instead software commonly interacts with resources and services
outside of its own hardware unit. The scope of this interaction defines the application’s digital ecosystem. Smartphones interact with cloud resources; cloud resources
include databases, specialized compute or storage clouds, specialized hardware and
virtual machines (VMs). Combining processes of varying complexity with varying
resource allocations produces different energy consumption levels. The challenge is
to investigate the variability of software process orchestration based on a power consumption framework to accrue and optimize energy savings in digital ecosystems.
The contributions of this dissertation include: i) an adaptive energy consumption
framework; ii) self-adaptive energy management systems based on this framework;
iii) deployment mechanisms for applications to use this framework; iv) models at
runtime models for self-adaptive energy management systems. Our ultimate goal is
to develop smart, self-adaptive, green computing techniques, such as adaptive job
scheduling and resource provisioning, to reduce overall power consumption in data
centres, on individual devices (e.g., mobile, desktop, laptop or server), and in digital
ecosystems. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/8868 |
| Date | 15 December 2017 |
| Creators | Bergen, Andreas Christoph |
| Contributors | Coady, Yvonne, Müller, Hausi A. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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