Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

Cette thèse engagée à partir des enjeux de la transformation du paysage international de l’éducation supérieure et ses implications au niveau local et national de l’organisation des universités. Basé dans une approche néo-rationaliste des interactions entre acteurs et les systèmes d’enseignement supérieure, le concept gigogne d’arènes académiques et les processus de couplage/découplage, l’analyse se focalise sur le cas d’étude du développement, implantation et ajustement d’un dispositif de calcul des services d’enseignant-chercheur. La méthodologie est basée sur une observation participante de longue durée, l’action-recherche et la méthode d’étude de cas. L’analyse quantitative relative à la transformation de la structure et de la performance des enseignants-chercheurs avec les accomplissements de l’université est combinée avec l’analyse qualitative de données collectées par des entretiens individuels et de groupes conduits avec des enseignants-chercheurs, des membres de la direction de l’université ainsi qu’avec des experts de l’enseignement supérieur malaisien et international. Les analyses conduites démontrent – au niveau de l’organisation universitaire – la capacité de négociation des académiques et du management par rapport à leurs rôles et conditions d’avancement de carrière est central dans l’instrumentation du dispositif de calcul des services. Au niveau national, les analyses soulignent la complémentarité entre institutions d’éducation supérieure privées et publiques. Au niveau international, les analyses soulignent la capacité d’ajustement du système universitaire et des politiques malaisiennes relatives à l’enseignement supérieur et à la recherche pour la régulation de leurs positions dans l’arène académique internationale. / This thesis is based on issues concerning the changing landscape of higher education globally and its implication at the local and national levels of the organization of universities. Drawn from the neo-rationalism perspective on the interactions between actors and higher education systems, the ‘gigogne’ concept of the academic arenas and coupling / decoupling processes, the analysis focuses on the case study of the development, implementation and adjustment of academic workload model. The methodology applied is based on long-term participant observation, action research and case study approaches. It is mixing quantitative analysis of the transformation of the academics’ performance and structure with the university’s achievement. On the other hand, this quantitative approach is complimented with the qualitative analysis of data gathered through formal interviews and focus groups, conducted with academics, senior management of the university and as well as experts of the higher education system in Malaysia and globally.Conclusions from the findings demonstrate – at the organizational level of the university - the capability of negotiation of the academics and senior management with regards to their roles and conditions of career advancement is central in the instrumentation of the academic workload system. At the national level, it highlights the complementary action between public and private higher education institutions. At the international level, it stresses on the capability of adjustment of the university organization system and the Malaysian higher education policy to redefine itself in regulating its position at the arenas of international bodies in higher education.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

The aim of the study: to determine and compare the low frequency fatigue of the legs’ muscles of 12–13 years old boys during the isometric and eccentric-concentric physical exercises. The objectives of the study: 1. To determine the exertion of low frequency fatigue during isometric exercises in boys. 2. To determine the exertion of low frequency fatigue during eccentric-concentric exercises in boys. 3. To compare the exertion of low frequency fatigue among isometric and eccentric-concentric exercises in boys. Subjects. Healthy boys (n = 6) aged 12–13 (12,4 ± 0,6) who do not participate in sports regularly but attend the classes of physical education twice a week. The rest period between the studies was three weeks. The tension was set up at 50 % MVS level. After that the values of the contractions and relaxations of the muscles were registered as follows: 1. The strength of muscle contraction caused by a single electric stimulus (Pt); 2. The strength of muscle contraction (P) caused by the following regiments of electrostimulation: 7 Hz (P7), 10 Hz (P10), 15 Hz (P15), 20 Hz (P20), and 100 Hz (P100) (the duration of the stimulation – 1 s, and the rest intervals between stimulations – 5 s); 3. Maximal voluntary strength (MVS) of muscle contractions (three trials every 3 minutes); 4. The workout (10 minutes of slow pedaling on the ergometer: the heart rate contractions from 110 to 130 times per minute); 5. Isometric workload: 18 times, each lasts 15 s, 40 % MVS contraction... [to full text]

The concept of home care nursing workload has not been widely studied and no evidence was found that an analysis of the concept had been undertaken. Consequently, there was a knowledge gap regarding the definition and attributes for the concept of home care nursing workload as it is currently experienced. To address that gap, a descriptive, three-phase, mixed methods (quantitative and qualitative) study was conducted. In Phase One, Rodgers’ (2000) evolutionary method was used to analyze the concept of home care nursing workload based on the empirical literature. Phase Two was situated within the naturalistic inquiry paradigm and involved observation of ten home care registered nurses during their visits to 61 patients. In Phase three a questionnaire was administered to validate the draft definition and attributes for the concept of home care nursing workload. It was completed by 88 home care nursing experts from clinical practice, education, management and research. Qualitative findings were analyzed using inductive content analysis. Quantitative data were analyzed descriptively using SPSS. Data triangulation was used extensively within and between the study phases. Of 14 attributes in the phase three draft concept definition, respondents assigned the highest level of relevance to the attribute of cognitive effort and the lowest to physical effort. The final definition contained 20 attributes and includes the following excerpt: “Home care nursing workload is the totality of the cognitive, emotional and physical effort home care nurses expend to meet the expectations of all stakeholders in providing holistic, outcome directed and patient/family focused care within the context of a short or long-term therapeutic relationship.” Respondents reported high levels of agreement with the accuracy and completeness of the definition and the majority indicated the definition would be useful or very useful in their day-to-day work. The comprehensive concept exemplar that emerged from the study includes each of the identified attributes. The study findings provided evidence of the complexity and challenge inherent in quantitatively measuring home care nursing workload. Accordingly, implications of the findings are shared for the management and monitoring of workload and associated outcomes, as well as for nursing practice, education and research.

The concept of home care nursing workload has not been widely studied and no evidence was found that an analysis of the concept had been undertaken. Consequently, there was a knowledge gap regarding the definition and attributes for the concept of home care nursing workload as it is currently experienced. To address that gap, a descriptive, three-phase, mixed methods (quantitative and qualitative) study was conducted. In Phase One, Rodgers’ (2000) evolutionary method was used to analyze the concept of home care nursing workload based on the empirical literature. Phase Two was situated within the naturalistic inquiry paradigm and involved observation of ten home care registered nurses during their visits to 61 patients. In Phase three a questionnaire was administered to validate the draft definition and attributes for the concept of home care nursing workload. It was completed by 88 home care nursing experts from clinical practice, education, management and research. Qualitative findings were analyzed using inductive content analysis. Quantitative data were analyzed descriptively using SPSS. Data triangulation was used extensively within and between the study phases. Of 14 attributes in the phase three draft concept definition, respondents assigned the highest level of relevance to the attribute of cognitive effort and the lowest to physical effort. The final definition contained 20 attributes and includes the following excerpt: “Home care nursing workload is the totality of the cognitive, emotional and physical effort home care nurses expend to meet the expectations of all stakeholders in providing holistic, outcome directed and patient/family focused care within the context of a short or long-term therapeutic relationship.” Respondents reported high levels of agreement with the accuracy and completeness of the definition and the majority indicated the definition would be useful or very useful in their day-to-day work. The comprehensive concept exemplar that emerged from the study includes each of the identified attributes. The study findings provided evidence of the complexity and challenge inherent in quantitatively measuring home care nursing workload. Accordingly, implications of the findings are shared for the management and monitoring of workload and associated outcomes, as well as for nursing practice, education and research.

Cloud Computing presents a novel way for businesses to procure their IT needs. Its elasticity and on-demand provisioning enables a shift from capital expenditures to operating expenses, giving businesses the technological agility they need to respond to an ever-changing marketplace. The rapid adoption of Cloud Computing, however, poses a unique challenge to Cloud providers—their already very large electricity bill and carbon footprint will get larger as they expand; managing both costs is therefore essential to their growth. This thesis squarely addresses the above challenge. Recognizing the presence of Cloud data centers in multiple locations and the differences in electricity price and emission intensity among these locations and over time, we develop an optimization framework that couples workload distribution with time-varying signals on electricity price and emission intensity for financial and environmental benefits. The framework is comprised of an optimization model, an aggregate cost function, and 6 scheduling heuristics. To evaluate cost savings, we run simulations with 5 data centers located across North America over a period of 81 days. We use historical data on electricity price, emission intensity, and workload collected from market operators and research data archives. We find that our framework can produce substantial cost savings, especially when workloads are distributed both geographically and temporally—up to 53.35% on electricity cost, or 29.13% on carbon cost, or 51.44% on electricity cost and 13.14% on carbon cost simultaneously.

Thesis (M.A. (Social Work)) -- University of Limpopo, 2017 / The aim of the study was to explore the factors that contribute to occupational stress among the fire-fighters employed by the Polokwane Local Municipality, Capricorn District of Limpopo. The study focused on the following variables: working relationships, work load, nature of work, deadlines pressure and work-home balance. The nature of the study is qualitative. The study data was collected from July to September 2015. The researcher interviewed fire-fighters in Polokwane Local Municipality. An in-depth semi-structured interview was utilised as the data collection method. The instrument used in the study was a semi structured interview schedule. Data was thematically analysed. The study findings showed that working relationships, work-load, nature of work, deadline pressure and work-home balance contributed to occupational stress. The study also found that occupational stress had negative effects on both the fire-fighters and their organisation

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Autonomic management is important in storage systems and the space of autonomics in storage systems is vast. Such autonomic management systems can employ a variety of techniques depending upon the specific problem. In this thesis, we first take an algorithmic approach towards reliability enhancement and then we use learning along with a reactive framework to facilitate storage optimization for applications. We study how the reliability of non-repairable systems can be improved through automatic reconfiguration of their XOR-coded structure. To this regard we propose to increase the fault tolerance of non-repairable systems by reorganizing the system, after a failure is detected, to a new XOR-code with a better fault tolerance. As errors can manifest during reorganization due to whole reads of multiple submodules, our framework takes them in to account and models such errors as based on access intensity (ie.BER-biterrorrate). We present and evaluate the reliability of an example storage system with and without reorganization. Motivated by the critical need for automating various aspects of data management in virtualized data centers, we study the specific problem of automatically implementing Virtual Machine (VM) migration in a dynamic environment according to some pre-set policies. This is a problem that requires automated identification of various workloads and their execution environments running inside virtual machines in a non-intrusive manner. To this end we propose AuM (for Autonomous Manager) that has the capability to learn workloads by aggregating variety of information obtained from network traces of storage protocols. We use state of the art Machine Learning tools, namely Multiple Kernel learning ,to aggregate information and show that AuM is indeed very accurate in identifying work loads, their execution environments and is also successful in following user set policies very closely for the VM migration tasks. Storage infrastructure in large-scale cloud data center environments must support applications with diverse, time-varying data access patterns while observing the quality of service. To meet service level requirements in such heterogeneous application phases, storage management needs to be phase-aware and adaptive ,i.e. ,identify specific storage access patterns of applications as they occur and customize their handling accordingly. We build LoadIQ, an online application phase detector for networked (file and block) storage systems. In a live deployment , LoadIQ analyzes traces and emits phase labels learnt online. Such labels could be used to generate alerts or to trigger phase-specific system tuning.