Energy adaptive digital ecosystems
| dc.contributor.author | Bergen, Andreas Christoph | |
| dc.contributor.supervisor | Coady, Yvonne | |
| dc.contributor.supervisor | Müller, Hausi A. | |
| dc.date.accessioned | 2017-12-15T18:40:14Z | |
| dc.date.available | 2017-12-15T18:40:14Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017-12-15 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy Ph.D. | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/8868 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Green Computing | en_US |
| dc.title | Energy adaptive digital ecosystems | en_US |
| dc.type | Thesis | en_US |