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Abstract Not Provided / Thesis / Master of Science (MSc)

Deteriorating quantity and quality of wetland ecosystem services is a major challenge for the conservation of the Lawuba wetland: socioeconomically the most important wetland area in Swaziland. In response, this study was designed to assess local dependent communities’ factual knowledge of the benefits and threats to the wetland, and their attitudes towards its conservation. In addition, the study employed environmental valuation techniques to estimate the annual economic value of the wetland’s fibre provisioning services and four notions of resource rent associated with the harvested fibre: rent on fibre consumed on site as a final product; and rent on fibre transported for 90 kilometres to Manzini market where it is sold, either as a final product or used as an intermediate input in the production of handicrafts. The fibre ecosystem service was specifically selected on account of its socioeconomic significance. Value of the fibre provisioning service was estimated using market price-based methods, while the magnitude of the different notions of resource rent was estimated using the net price method. A random sample of 63 respondents was used to provide data on the benefits, threats, attitudes, and annual economic value which households attach to the harvested fibre. This sample also provided data used to compute the resource rent associated with fibre harvested and consumed on-site. A random sample of 5 respondents provided data used to compute the resource rent on fibre transported and sold in Manzini as a final consumption good. Finally, a random sample of 5 respondents provided data used to compute the resource rent on fibre manufactured at Lawuba and sold in Manzini. Households had high levels of knowledge of the benefits and threats to the Lawuba wetland. They also had positive attitudes towards its conservation. Chi-square and ANOVA tests rejected the null hypothesis of no association between household: (i) knowledge of the benefits derived from the wetland and income (F = 12.67, p = 0.000), (ii) knowledge of the threats endangering the wetland and education (χ2 = 38.474, p = 0.000), (iii) knowledge of the threats endangering the wetland and income (F = 7.25, p = 0.000), (iv) attitudes towards its conservation and income (F = 13.320, p = 0.000) and (v) attitudes towards its conservation and gender (χ2 = 11.854, p = 0.003). The value of fibre provisioning services was estimated at between US $20,310 and US $32,673 per annum, which translates to US $70 per capita per annum. Magnitude of the resource rent increased along the value chain as theory would predict. It was estimated at US $1.92 (for fibre harvested and consumed on site), US $2.27 (for fibre sold at Manzini as a final product), and US $18 (for fibre manufactured at Lawuba and sold in Manzini). Inasmuch as the study established a positive resource rent, no institutions currently exist for rent capture and appropriate re-investment to support sustainable wetland conservation. The study thus recommends the need to set up suitable resource management institutions. / Dissertation (MSc Agric)--University of Pretoria, 2014. / gm2014 / Agricultural Economics, Extension and Rural Development / unrestricted

Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Exacerbating such costs, data centers are often over-provisioned to avoid costly outages associated with the potential overloading of electrical circuitry. However, such over provisioning is often unnecessary since a data center rarely operates at its maximum capacity. It is imperative that we realize effective strategies to control the power consumption of the server and improve the energy efficiency of data centers. Adding to the problem is the inability of the servers to exhibit energy proportionality which diminishes the overall energy efficiency of the data center. Therefore in this dissertation, we investigate whether it is possible to achieve energy proportionality at the server- and cluster-level by efficient power and resource provisioning. Towards this end, we provide a thorough analysis of energy proportionality at the server and cluster-level and provide insight into the power saving opportunity and mechanisms to improve energy proportionality. Specifically, we make the following contribution at the server-level using enterprise-class workloads. We analyze the average power consumption of the full system as well as the subsystems and describe the energy proportionality of these components, characterize the instantaneous power profile of enterprise-class workloads using the on-chip energy meters, design a runtime system based on a load prediction model and an optimization framework to set the appropriate power constraints to meet specific performance targets and then present the effects of our runtime system on energy proportionality, average power, performance and instantaneous power consumption of enterprise applications. We then make the following contributions at the cluster-level. Using data serving, web searching and data caching as our representative workloads, we first analyze the component-level power distribution on a cluster. Second, we characterize how these workloads utilize the cluster. Third, we analyze the potential of power provisioning techniques (i.e., active low-power, turbo and idle low-power modes) to improve the energy proportionality. We then describe the ability of active low-power modes to provide trade-offs in power and latency. Finally, we compare and contrast power provisioning and resource provisioning techniques. This thesis sheds light on mechanisms to tune the power provisioned for a system under strict performance targets and opportunities to improve energy proportionality and instantaneous power consumption via efficient power and resource provisioning at the server- and cluster-level. / Ph. D.

The operation and maintenance phase of a complex technical system may deal with strategicdecisions for asset retirement and end-of-life management. When a fleet of aircraft reachesthe retirement phase, the operation of remaining fleet should still be kept at a defined level ofavailability. Obviously, the provisioning of spares is a key issue to support the maintenanceand operation of the remaining fleet. The best practice within the aviation industry is to re-usethe spares of retired aircraft to support the operational fleet. This is referred to parting-out.The purpose of the research conducted for this thesis has been to develop decision supportmethodologies, models and tools for the management of a sustainable part-out-based sparesprovisioning for an aircraft fleet during its retirement period. The proposed methodology willbe used to support the retirement process of aircraft fleet and enhance the organisation’scapability of making efficient and cost-effective decisions concerning the re-use of spare partsduring the retirement period. To achieve the purpose of this research, literature studies, casestudies, algorithm development and simulations have been conducted. Empirical data havebeen collected through document studies, interviews, and the perusal of archival records fromSaab Support and Services AB. The data analysis performed for this research has been basedon theories and methodologies within reliability analysis, cost modelling, spares forecasting,stock provisioning and decision making, in combination with the best practices implementedby the aviation industry for the end-of-life management and retirement of aircraft.In the present thesis, part-out-based spares provisioning (PBSP) program is proposed to utiliseretired aircraft units effectively as spare parts. The proposed approach is illustrated andverified through a case study performed on the “Saab-105” military aircraft fleet withinSwedish air force fleet. A PBSP programme is proposed, associated management activitiesare described, the key decision criteria are presented, and a functional framework for aneffective PBSP is suggested. The proposed PBSP program provides a foundation for furthermeasures and tasks to be performed within the retirement period, such as terminatingmaintenance contracts, discarding internal maintenance capabilities, reviewing stocks, scalingdown administrative processes (e.g. spares procurement and obsolescence monitoring), etc.An important part of the PBSP programme is the reliability analysis of multiple repairableunits, and this has been investigated, using parametric and non-parametric reliabilityapproaches. The aim is to identify a practical approach for estimation of the future sparedemand at fleet level. Furthermore, a set of computational models and search algorithm havebeen developed for the identification of applicable termination times, of both the parting-outprocess and the maintenance and repair actions performed on the units. This includestermination of the parting-out process (PO), the sending of parted-out units directly to storage(POS), and repair actions performed on the units received at the repair shops owing tocorrective (CM) and preventive (PM) maintenance, as well as the parted-out units that need tobe repaired (POM). The feasible termination alternatives are compared with regard to theirrespective costs and the most cost-effective solutions are identified.The results of the research study show that a PBSP programme can yield large reductions inmaintenance and spares procurement costs, while supporting operation of existing fleet athighest required availability. It also contributes positively to implement a green supply chainduring the retirement phase. The methodology and approaches introduced within the thesiscan be applied in other civil applications, such as energy, mining, process industry andtransportation sectors.

The rate of data growth in many domains is straining our ability to manage and analyze it. Consequently, we see the emergence of computing systems that attempt to efficiently process data-intensive applications or I/O bound applications with large data. Cloud computing offers “infinite” resources on demand, and on a pay-as-you-go basis. As a result, it has gained interest for large-scale data processing. Given this supposedly infinite resource set, we need a provisioning process to determine appropriate resources for data processing or workload execution. We observe that the prevalent data processing architectures do not usually employ provisioning techniques available in a public cloud, and existing provisioning techniques have largely ignored data-intensive applications in public clouds. In this thesis, we take a step towards bridging the gap between existing data processing approaches and the provisioning techniques available in a public cloud, such that the monetary cost of executing data-intensive workloads is minimized. We formulate the problem of provisioning and include constructs to exploit a cloud’s elasticity to include any number of resources to host a multi-tenant database system prior to execution. The provisioning is modeled as a search problem, and we use standard search heuristics to solve it. We propose a novel framework for resource provisioning in a cloud environment. Our framework allows pluggable cost and performance models. We instantiate the framework by developing various search algorithms, cost and performance models to support the search for an effective resource configuration. We consider data-intensive workloads that consist of transactional, analytical or mixed workloads for evaluation, and access multiple database tenants. The workloads are based on standard TPC benchmarks. In addition, the user preferences on response time or throughput are expressed as constraints. Our propositions and their results are validated in a real public cloud, namely the Amazon cloud. The evaluation supports our claim that the framework is an effective tool for provisioning database workloads in a public cloud with minimal dollar cost. / Thesis (Ph.D, Computing) -- Queen's University, 2013-11-30 19:30:39.427

Big data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.

Atualmente o acesso a um ambiente de computação em nuvem é fornecido sob demanda, o que permite que provedores ofereçam serviços de forma elástica aos clientes. Embora a nuvem permita uma abstração do comportamento da infraestrutura (lógica e física) dos provedores de serviços, nem sempre é possível oferecer serviços aos clientes de modo que os provedores consigam cumprir adequadamente os acordos de níveis de serviços. Para permitir o cumprimento desses contratos, provedores de serviços precisam de mecanismos que envolvam algoritmos de balanceamento de carga, com o objetivo de fornecer uma distribuição eficiente da carga para os recursos disponíveis. Entretanto, os trabalhos presentes na literatura não abordam de forma adequada o problema de equacionamento de recursos em função das necessidades dos clientes, pois consideram em sua maioria um conjunto limitado de objetivos a serem analisados e cumpridos. Neste contexto, o objetivo deste projeto de mestrado foi desenvolver e avaliar algoritmos disponíveis na literatura que abordem a otimização combinatória para o provisionamento de recursos computacionais, buscando otimizar o uso eficiente da infraestrutura e cumprir os acordos de nível de serviço definidos entre clientes e provedores. / Currently the access to a cloud computing environment is provided on demand, which allows providers to offer elasticity services to customers. Although the cloud allows an abstraction of the infrastructure behavior of the service providers (logical and physical), the fulfillment of the Service Level Agreements (SLAs) is challenging, because according with the demand and system configuration, the providers cannot ensure the customers requirements. There is a necessity of mechanisms that consider load balancing algorithms to provide an efficient load distribution in the available resources. However, the papers available in the literature do not efficiently address the problem of resource equation considering the customers requirements, because they consider a limited set of objectives to be analysed and fulfilled. Therefore, this master project aims to develop and evaluate algorithms available in the literature that address the combinatorial optimization and the multi-objective approach to handle the computational resources during the execution time, trying to optimize the efficient use of the resources available in the infrastructure and fulfill the service level agreements defined between the clients and providers.

Scientific workflows are used in many scientific fields to abstract complex computations (tasks) and data or flow dependencies between them. High performance computing (HPC) systems have been widely used for the execution of scientific workflows. Cloud computing has gained popularity by offering users on-demand provisioning of resources and providing the ability to choose from a wide range of possible configurations. To do so, resources are made available in the form of virtual machines (VMs), described as a set of resource characteristics, e.g. amount of CPU and memory. The notion of VMs enables the use of different resource combinations which facilitates the deployment of the applications and the management of the resources. A problem that arises is determining the configuration, such as the number and type of resources, that leads to efficient resource provisioning. For example, allocating a large amount of resources may reduce application execution time however at the expense of increased costs. This thesis investigates the challenges that arise on resource provisioning and task scheduling of scientific workflows and explores ways to address them, developing approaches to improve energy efficiency for scientific workflows and meet the user's objectives, e.g. makespan and monetary cost. The motivation stems from the wide range of options that enable to select cost-efficient configurations and improve resource utilisation. The contributions of this thesis are the following. (i) A survey of the issues arising in resource management in cloud computing; The survey focuses on VM management, cost efficiency and the deployment of scientific workflows. (ii) A performance model to estimate the workflow execution time for a different number of resources based on the workflow structure; The model can be used to estimate the respective user and energy costs in order to determine configurations that lead to efficient resource provisioning and achieve a balance between various conflicting goals. (iii) Two energy-aware scheduling algorithms that maximise the number of completed workflows from an ensemble under energy and budget or deadline constraints; The algorithms address the problem of energy-aware resource provisioning and scheduling for scientific workflow ensembles. (iv) An energy-aware algorithm that selects the frequency to be used for each workflow task in order to achieve energy savings without exceeding the workflow deadline; The algorithm takes into account the different requirements and constraints that arise depending on the workflow and system characteristics. (v) Two cost-based frequency selection algorithms that choose the CPU frequency for each provisioned resource in order to achieve cost-efficient resource configurations for the user and complete the workflow within the deadline; Decision making is based on both the workflow characteristics and the pricing model of the provider.

Atualmente o acesso a um ambiente de computação em nuvem é fornecido sob demanda, o que permite que provedores ofereçam serviços de forma elástica aos clientes. Embora a nuvem permita uma abstração do comportamento da infraestrutura (lógica e física) dos provedores de serviços, nem sempre é possível oferecer serviços aos clientes de modo que os provedores consigam cumprir adequadamente os acordos de níveis de serviços. Para permitir o cumprimento desses contratos, provedores de serviços precisam de mecanismos que envolvam algoritmos de balanceamento de carga, com o objetivo de fornecer uma distribuição eficiente da carga para os recursos disponíveis. Entretanto, os trabalhos presentes na literatura não abordam de forma adequada o problema de equacionamento de recursos em função das necessidades dos clientes, pois consideram em sua maioria um conjunto limitado de objetivos a serem analisados e cumpridos. Neste contexto, o objetivo deste projeto de mestrado foi desenvolver e avaliar algoritmos disponíveis na literatura que abordem a otimização combinatória para o provisionamento de recursos computacionais, buscando otimizar o uso eficiente da infraestrutura e cumprir os acordos de nível de serviço definidos entre clientes e provedores. / Currently the access to a cloud computing environment is provided on demand, which allows providers to offer elasticity services to customers. Although the cloud allows an abstraction of the infrastructure behavior of the service providers (logical and physical), the fulfillment of the Service Level Agreements (SLAs) is challenging, because according with the demand and system configuration, the providers cannot ensure the customers requirements. There is a necessity of mechanisms that consider load balancing algorithms to provide an efficient load distribution in the available resources. However, the papers available in the literature do not efficiently address the problem of resource equation considering the customers requirements, because they consider a limited set of objectives to be analysed and fulfilled. Therefore, this master project aims to develop and evaluate algorithms available in the literature that address the combinatorial optimization and the multi-objective approach to handle the computational resources during the execution time, trying to optimize the efficient use of the resources available in the infrastructure and fulfill the service level agreements defined between the clients and providers.