Environmental monitoring using wireless sensors plays a key role in detecting hotspots or over-cooling conditions in a data center (DC). Despite a myriad of Data Center Wireless Sensor Network (DCWSN) solutions in literature, their adoption in DCs is scarce due to four challenges: low reliability, short battery lifetime, lack of adaptability, and labour intensive deployment. The main objective of this research is to address these challenges in our specifically designed hierarchical DCWSN, called Low Energy Monitoring Network (LEMoNet). LEMoNet is a two-tier protocol, which features Bluetooth Low Energy (BLE) for sensors communication in the first tier. It leverages multi-gateway packet reception in its second tier to mitigate the unreliability of BLE. The protocol has been experimentally validated in a small DC and evaluated by simulations in a midsize DC. However, since the main application of DCWSNs is in colocation and large DCs, an affordable and fast approach is still required to assess LEMoNet in large scale. As the first contribution, we develop an analytical model to characterize its scalability and energy efficiency in a given network topology. The accuracy of the model is validated through extensive event-driven simulations. Evaluation results show that LEMoNet can achieve high reliability in a network of 4800 nodes at a duty cycle of 15s. To achieve the network adaptability, we introduce and design SoftBLE, a Software-Defined Networking (SDN) based framework that provides controllability to the network. It takes advantages of advanced control knobs recently available in BLE protocol stacks. SoftBLE is complemented by two orchestration algorithms to optimize gateway and sensor parameters based on run-time measurements. Evaluation results from both an experimental testbed and a large-scale simulation study show that using SoftBLE, sensors consume 70% less power in data collection compared to those in baseline approaches while achieving the Packet Reception Rate (PRR) no less than 99.9%. One of its main steps of DCWSN commissioning is sensor localization, which is labour-intensive if is driven manually. To streamline the process, we devise a novel approach for automated sensor mapping. Since Radio Frequency (RF) alone is not a reliable data source for sensor localization in harsh and multi-path rich environments such as a DCs, we investigate using non-RF alternatives. Thermal Piloting is a classification model to correlate temperature sensor measurements with the expected thermal values at their locations. It achieves an average localization error of 0.64 meters in a modular DC testbed. The idea is further improved by a multimodal approach that incorporates pairwise Received Signal Strength (RSS) measurements of RF signals. The problem is formulated as Weighted Graph Matching (WGM) between an analytical graph and an experimental graph. A parallel algorithm is proposed to find heuristic solutions to this NP-hard problem, which is 30% more accurate than the baselines. The evaluation in a modular DC testbed shows that the localization errors using multi-modality are less than one-third of that of using thermal data alone. / Thesis / Candidate in Philosophy

With the rapid growth of servers and applications spurred by the Internet economy, power consumption in today's data centers is reaching unsustainable limits. This has led to an imminent financial, technical and environmental crisis that is impacting the society at large. Hence, it has become critically important that power consumption be efficiently managed in these computing power-houses of today. In this work, we revisit the issue of adaptive power and performance management of data center server platforms. Traditional data center servers are statically configured and always over-provisioned to be able to handle peak load. We transform these statically configured data center servers to clairvoyant entities that can sense changes in the workload and dynamically scale in capacity to adapt to the requirements of the workload. The over-provisioned server capacity is transitioned to low-power states and they remain in those states for as long as the performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to performance constraints. This is formulated as a performance-per-watt optimization problem and solved using analytical power and performance models. Coarse-grained optimizations at the platform-level are refined by local optimizations at the devices-level namely - the processor & memory subsystems. Our adaptive interleaving technique for memory power management yielded about 48.8% (26.7 kJ) energy savings compared to traditional techniques measured at 4.5%. Our adaptive platform power and performance management technique demonstrated 56.25% energy savings for memory-intensive workload, 63.75% savings for processor-intensive workload and 47.5% savings for a mixed workload while maintaining platform performance within given acceptable thresholds.

Clearly established conservation priorities are urgently required for taxa and ecosystems in critical need of conservation. This helps to identify and document taxa most in need of conservation attention, and provides an index of the state of degeneration of biodiversity. Including as much relevant information as possible in a prioritisation assessment will deliver the most accurate classification, yet these variables should not overly complicate the prioritisation process. Conservation assessments depend not just on the taxon’s susceptibility to threat (i.e. risk of extinction, or Red List assessments), but also the conservation value, irreplaceability and nature and intensity of the threats. Research into the value and applicability of conservation prioritisation tools at a regional scale, allowed for the assessment of the extinction risk as well as subsequent priority ranking of South African mammals. At the outset research was directed towards investigating South African mammals in accordance with their respective regional and global World Conservation Union (IUCN) Red List and Red Data Book assessments. The regional Red List assessment drastically improved local knowledge of the current extinction risk of various mammals, and identified 57 marine and terrestrial mammals to be highly threatened. Up to date regional extinction risk assessments, allowed for the investigation of whether a human activity threat index derived from six human activity variables across South Africa could be used to highlight mammals threatened with extinction while also being exposed to high human activity. Evidence indicated various threatened and lower risk mammals were exposed to high human activity throughout their range, pointing to high potential threat and future increase in extinction risk. For relevant prioritisation to take place, components of vulnerability (IUCN Red List assessments, and occupancy data), irreplaceability (endemism and taxonomic distinctiveness), and threat measures (body mass and human density in a taxa distributional range) was introduced into relational priority assessment which allowed for a simplified approach in determining conservation priorities for taxa under various region-specific conditions. The use of different sets of information clearly affected the priority rankings. South African Chiroptera and Carnivora was used as a case study to addresses whether a simple measure of taxonomic diversity can be used as a proxy for different measures of phylogenetic diversity in determining regional conservation priority of taxa, when such information is limited. Evidence does suggest that the utilisation of the simple taxonomic diversity measure may provide the appropriate information on evolutionary diversity. Two theoretical concepts were proposed to address some potential shortcomings in the conservation prioritisation arena. The Orange List method offers a system to identify “species [or taxa] of high national importance or of high conservation value” (South African National Environmental Management: Biodiversity Act 2004). In turn the Green Data List essentially represents a radical shift in the traditional approach to the management of both threatened and invasive taxa. Throughout this thesis, evidence do point to smaller mammals being of high conservation concern in South Africa, with the members from the Orders Rodentia, Chiroptera and Insectivora being constantly identified as high conservation priority. Apart from contributing to our current understanding of the conservation importance/priority of South Africa mammals, this current thesis has resulted in a robust understanding of various assessment techniques. / Thesis (DPhil (Zoology))--University of Pretoria, 2007. / Zoology and Entomology / unrestricted

Some of today’s most popular digital platforms do not have any environmental policies. The growing volume of their data consumption contributes to an increase in environmental emissions. Young adults between the ages of 16-25 are the most frequent users of these platforms. However, previous studies also show that of all age groups, 16-25s also have the highest level of concern for climate change. The purpose of this study is thus to research if the age group 16-25 are aware of the phenomenon ’green data’ and that both their own and the general data consumption has an environmental impact. Furthermore the purpose of the study is to research if and how a marketingcampaign could impact this age group to alter their digital choices to platforms and apps with less environmental impact.  A quantitative survey revealed negative results on both counts. Additionally, to explore if a marketingcampaign could alter the age groups digital choices, an intervention was conducted as an Instagram prototype. The prototype was measured qualitatively through two focus groups, one exposed to the marketing campaign and one control. Although the intervention raised awareness among the exposed group, the results suggested one campaign alone would not be sufficient. Additionally, the results revealed that lack of availability of alternative platforms, platform choices amongst peers, and convenience, mitigated the effectiveness of the marketing campaign. Further research may prove valuable in investigating variables that neutralise the mitigating factors’ effects.