Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

Fatty acids and stable isotopes as indicators of early-life feeding and potential maternal resource dependency in the bull shark Carcharhinus leucas

The degree of reliance of newborn sharks on energy reserves from maternal resource allocation and the timescales over which these animals develop foraging skills are critical factors towards understanding the ecological role of top predators in marine ecosystems. We used muscle tissue stable carbon isotopic composition and fatty acid analysis of bull sharks Carcharhinus leucas to investigate early-life feeding ecology in conjunction with maternal resource dependency. Values of δ13C of some young-of-the-year sharks were highly enriched, reflecting inputs from the marine-based diet and foraging locations of their mothers. This group of sharks also contained high levels of the 20:3ω9 fatty acid, which accumulates during periods of essential fatty acid deficiency, suggesting inadequate or undeveloped foraging skills and possible reliance on maternal provisioning. A loss of maternal signal in δ13C values occurred at a length of approximately 100 cm, with muscle tissue δ13C values reflecting a transition from more freshwater/estuarine-based diets to marine-based diets with increasing length. Similarly, fatty acids from sharks >100 cm indicated no signs of essential fatty acid deficiency, implying adequate foraging. By combining stable carbon isotopes and fatty acids, our results provided important constraints on the timing of the loss of maternal isotopic signal and the development of foraging skills in relation to shark size and imply that molecular markers such as fatty acids are useful for the determination of maternal resource dependency.

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Fair and QoS-oriented resource management in heterogeneous networks

In this paper, a heterogeneous network composed of femtocells deployed within a macrocell network is considered, and a quality-of-service (QoS)-oriented fairness metric which captures important characteristics of tiered network architectures is proposed. Using homogeneous Poisson processes, the sum capacities in such networks are expressed in closed form for co-channel, dedicated channel, and hybrid resource allocation methods. Then a resource splitting strategy that simultaneously considers capacity maximization, fairness constraints, and QoS constraints is proposed. Detailed computer simulations utilizing 3GPP simulation assumptions show that a hybrid allocation strategy with a well-designed resource split ratio enjoys the best cell-edge user performance, with minimal degradation in the sum throughput of macrocell users when compared with that of co-channel operation.

Décentralisation et allocation des ressources : les détours de la municipalisation du système de santé brésilien.

Dans cette thèse, nous avons analysé le déroulement d’un processus de municipalisation du système de santé, effectué au Rio Grande do Norte (RN), un des états fédérés du nord-est du Brésil. En tenant compte des contextes historiques d’implantation, nous avons centré notre attention sur la contribution des acteurs impliqués dans ce processus, spécialement dans l’allocation des ressources financières du système. Les croyances, perceptions, attentes, représentations, connaissances, intérêts, l’ensemble des facteurs qui contribuent à la constitution des capacités cognitives de ces acteurs, favorise la réflexivité sur leurs actions et la définition de stratégies diverses de façon à poursuivre leurs objectifs dans le système de santé. Ils sont vus ainsi comme des agents compétents et réflexifs, capables de s’approprier des propriétés structurelles du système de santé (règles et ressources), de façon à prendre position dans l’espace social de ce système pour favoriser le changement ou la permanence du statu quo. Au cours du processus de structuration du Système unique de santé brésilien, le SUS, la municipalisation a été l’axe le plus développé d’un projet de réforme de la santé. Face aux contraintes contextuelles et de la dynamique complexe des espaces sociaux de la santé, les acteurs réformistes n’ont pas pu suivre le chemin de l’utopie idéalisée; quelques détours ont été parcourus. Au RN, la municipalisation de la santé a constitué un processus très complexe où la triade centralisation/décentralisation/recentralisation a suivi son cours au milieu de négociations, de conflits, d’alliances, de disputes, de coopérations, de compétitions. Malgré les contraintes des contextes successifs, des propriétés structurelles du système et des dynamiques sociales dans le système de santé, quelques changements sont intervenus : la construction de leaderships collectifs; l’émergence d’une culture de négociation; la création des structures et des espaces sociaux du système, favorisant les rencontres des acteurs dans chaque municipalité et au niveau de l’état fédéré; un apprentissage collectif sur le processus de structuration du SUS; une grande croissance des services de première ligne permettant d’envisager une inversion de tendance du modèle de prestation des services; les premiers pas vers la rupture avec la culture bureaucratique du système. Le SUS reste prisonnier de quelques enjeux institutionnalisés dans ce système de santé : la dépendance du secteur privé et de quelques groupes de professionnels; le financement insuffisant et instable; la situation des ressources humaines. Les changements arrivés sont convergents, incrémentiels, lents; ils résultent d’actions normatives, délibérées, formalisées. Elles aussi sont issues de l’inattendu, de l’informel, du paradoxe; quelques-unes plus localisées, d’autres plus généralisées, pour une courte ou une plus longue durée.

A Research of Resource Scheduling Algorithm Based on Hadoop

With the development of electronic devices, more and more mobile clients are connected to the Internet and they generate massive data every day. We live in an age of “Big Data”, and every day we generate hundreds of million magnitude data. By analyzing the data and making prediction, we can carry out better development plan. Unfortunately, traditional computation framework cannot meet the demand, so the Hadoop would be put forward. First the paper introduces the background and development status of Hadoop, compares the MapReduce in Hadoop 1.0 and YARN in Hadoop 2.0, and analyzes the advantages and disadvantages of them. Because the resource management module is the core role of YARN, so next the paper would research about the resource allocation module including the resource management, resource allocation algorithm, resource preemption model and the whole resource scheduling process from applying resource to finishing allocation. Also it would introduce the FIFO Scheduler, Capacity Scheduler, and Fair Scheduler and compare them. The main work has been done in this paper is researching and analyzing the Dominant Resource Fair algorithm of YARN, putting forward a maximum resource utilization algorithm based on Dominant Resource Fair algorithm. The paper also provides a suggestion to improve the unreasonable facts in resource preemption model. Emphasizing “fairness” during resource allocation is the core concept of Dominant Resource Fair algorithm of YARM. Because the cluster is multiple users and multiple resources, so the user’s resource request is multiple too. The DRF algorithm would divide the user’s resources into dominant resource and normal resource. For a user, the dominant resource is the one whose share is highest among all the request resources, others are normal resource. The DRF algorithm requires the dominant resource share of each user being equal. But for these cases where different users’ dominant resource amount differs greatly, emphasizing “fairness” is not suitable and can’t promote the resource utilization of the cluster. By analyzing these cases, this thesis puts forward a new allocation algorithm based on DRF. The new algorithm takes the “fairness” into consideration but not the main principle. Maximizing the resource utilization is the main principle and goal of the new algorithm. According to comparing the result of the DRF and new algorithm based on DRF, we found that the new algorithm has more high resource utilization than DRF. The last part of the thesis is to install the environment of YARN and use the Scheduler Load Simulator (SLS) to simulate the cluster environment.

Décentralisation et allocation des ressources : les détours de la municipalisation du système de santé brésilien

Dans cette thèse, nous avons analysé le déroulement d’un processus de municipalisation du système de santé, effectué au Rio Grande do Norte (RN), un des états fédérés du nord-est du Brésil. En tenant compte des contextes historiques d’implantation, nous avons centré notre attention sur la contribution des acteurs impliqués dans ce processus, spécialement dans l’allocation des ressources financières du système. Les croyances, perceptions, attentes, représentations, connaissances, intérêts, l’ensemble des facteurs qui contribuent à la constitution des capacités cognitives de ces acteurs, favorise la réflexivité sur leurs actions et la définition de stratégies diverses de façon à poursuivre leurs objectifs dans le système de santé. Ils sont vus ainsi comme des agents compétents et réflexifs, capables de s’approprier des propriétés structurelles du système de santé (règles et ressources), de façon à prendre position dans l’espace social de ce système pour favoriser le changement ou la permanence du statu quo. Au cours du processus de structuration du Système unique de santé brésilien, le SUS, la municipalisation a été l’axe le plus développé d’un projet de réforme de la santé. Face aux contraintes contextuelles et de la dynamique complexe des espaces sociaux de la santé, les acteurs réformistes n’ont pas pu suivre le chemin de l’utopie idéalisée; quelques détours ont été parcourus. Au RN, la municipalisation de la santé a constitué un processus très complexe où la triade centralisation/décentralisation/recentralisation a suivi son cours au milieu de négociations, de conflits, d’alliances, de disputes, de coopérations, de compétitions. Malgré les contraintes des contextes successifs, des propriétés structurelles du système et des dynamiques sociales dans le système de santé, quelques changements sont intervenus : la construction de leaderships collectifs; l’émergence d’une culture de négociation; la création des structures et des espaces sociaux du système, favorisant les rencontres des acteurs dans chaque municipalité et au niveau de l’état fédéré; un apprentissage collectif sur le processus de structuration du SUS; une grande croissance des services de première ligne permettant d’envisager une inversion de tendance du modèle de prestation des services; les premiers pas vers la rupture avec la culture bureaucratique du système. Le SUS reste prisonnier de quelques enjeux institutionnalisés dans ce système de santé : la dépendance du secteur privé et de quelques groupes de professionnels; le financement insuffisant et instable; la situation des ressources humaines. Les changements arrivés sont convergents, incrémentiels, lents; ils résultent d’actions normatives, délibérées, formalisées. Elles aussi sont issues de l’inattendu, de l’informel, du paradoxe; quelques-unes plus localisées, d’autres plus généralisées, pour une courte ou une plus longue durée.

Water Demand and Allocation in the Mara River Basin, Kenya/Tanzania in the Face of Land Use Dynamics and Climate Variability

The Mara River Basin (MRB) is endowed with pristine biodiversity, socio-cultural heritage and natural resources. The purpose of my study is to develop and apply an integrated water resource allocation framework for the MRB based on the hydrological processes, water demand and economic factors. The basin was partitioned into twelve sub-basins and the rainfall runoff processes was modeled using the Soil and Water Assessment Tool (SWAT) after satisfactory Nash-Sutcliff efficiency of 0.68 for calibration and 0.43 for validation at Mara Mines station. The impact and uncertainty of climate change on the hydrology of the MRB was assessed using SWAT and three scenarios of statistically downscaled outputs from twenty Global Circulation Models. Results predicted the wet season getting more wet and the dry season getting drier, with a general increasing trend of annual rainfall through 2050. Three blocks of water demand (environmental, normal and flood) were estimated from consumptive water use by human, wildlife, livestock, tourism, irrigation and industry. Water demand projections suggest human consumption is expected to surpass irrigation as the highest water demand sector by 2030. Monthly volume of water was estimated in three blocks of current minimum reliability, reserve (>95%), normal (80–95%) and flood (40%) for more than 5 months in a year. The assessment of water price and marginal productivity showed that current water use hardly responds to a change in price or productivity of water. Finally, a water allocation model was developed and applied to investigate the optimum monthly allocation among sectors and sub-basins by maximizing the use value and hydrological reliability of water. Model results demonstrated that the status on reserve and normal volumes can be improved to ‘low’ or ‘moderate’ by updating the existing reliability to meet prevailing demand. Flow volumes and rates for four scenarios of reliability were presented. Results showed that the water allocation framework can be used as comprehensive tool in the management of MRB, and possibly be extended similar watersheds.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Reflexão bioética sobre a priorização e o racionamento de cuidados de saúde: entre a utilidade social e a eqüidade

Sendo as necessidades de saúde mais amplas que os recursos disponíveis, escolhas têm de ser feitas. Disto resulta ser preciso que se estabeleçam limites, critérios e parâmetros para priorizar o que vai ser ofertado e a quem os serviços e os cuidados de saúde serão oferecidos. Discute-se alternativas éticas para a priorização e racionamento de cuidados de saúde, enfocando os princípios da eqüidade e da utilidade social.

Surveillance Programme for Healthcare Associated Infections in the State of Sao Paulo, Brazil. Implementation and the first three years` results

Governmental programmes should be developed to collect and analyse data on healthcare associated infections (HAIs). This study describes the healthcare setting and both the implementation and preliminary results of the Programme for Surveillance of Healthcare Associated Infections in the State of Sao Paulo (PSHAISP), Brazil, from 2004 to 2006. Characterisation of the healthcare settings was carried out using a national database. The PSHAISP was implemented using components for acute care hospitals (ACH) or long term care facilities (LTCF). The components for surveillance in ACHs were surgical unit, intensive care unit and high risk nursery. The infections included in the surveillance were surgical site infection in clean surgery, pneumonia, urinary tract infection and device-associated bloodstream infections. Regarding the LTCF component, pneumonia, scabies and gastroenteritis in all inpatients were reported. In the first year of the programme there were 457 participating healthcare settings, representing 51.1% of the hospitals registered in the national database. Data obtained in this study are the initial results and have already been used for education in both surveillance and the prevention of HAI. The results of the PSHAISP show that it is feasible to collect data from a large number of hospitals. This will assist the State of Sao Paulo in assessing the impact of interventions and in resource allocation. (C) 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

Applying semantics to grid middleware

Scheduling parallel and distributed applications efficiently onto grid environments is a difficult task and a great variety of scheduling heuristics has been developed aiming to address this issue. A successful grid resource allocation depends, among other things, on the quality of the available information about software artifacts and grid resources. In this article, we propose a semantic approach to integrate selection of equivalent resources and selection of equivalent software artifacts to improve the scheduling of resources suitable for a given set of application execution requirements. We also describe a prototype implementation of our approach based on the Integrade grid middleware and experimental results that illustrate its benefits. Copyright (C) 2009 John Wiley & Sons, Ltd.

Dissociation between skin conductance orienting and secondary task reaction time: Time course with a visual discrimination task

A dissociation between two putative measures of resource allocation skin conductance responding, and secondary task reaction time (RT), has been observed during auditory discrimination tasks. Four experiments investigated the time course of the dissociation effect with a visual discrimination task. participants were presented with circles and ellipses and instructed to count the number of longer-than-usual presentations of one shape (task-relevant) and to ignore presentations of the other shape (task-irrelevant). Concurrent with this task, participants made a speeded motor response to an auditory probe. Experiment 1 showed that skin conductance responses were larger during task-relevant stimuli than during task-irrelevant stimuli, whereas RT to probes presented at 150 ms following shape onset was slower during task-irrelevant stimuli. Experiments 2 to 4 found slower RT during task-irrelevant stimuli at probes presented at 300 ms before shape onset until 150 ms following shape onset. At probes presented 3,000 and 4,000 ms following shape onset probe RT was slower during task-relevant stimuli. The similarities between the observed time course and the so-called psychological refractory period (PRF) effect are discussed.

Discriminating between task-relevant and task-irrelevant stimuli - The effects on autonomic orienting and secondary task reaction time

The effect that the difficulty of the discrimination between task-relevant and task-irrelevant stimuli has on the relationship between skin conductance orienting and secondary task reaction time (RT) was examined. Participants (N = 72) counted the number of longer-than-usual presentations of one shape (task-relevant) and ignored presentations of another shape (task-irrelevant). The difficulty of discriminating between the two shapes varied across three groups (low, medium, and high difficulty). Simultaneous with the primary counting task, participants performed a secondary RT task to acoustic probes presented 50, 150, and 2000 ms following shape onset. Skin conductance orienting was larger, and secondary RT at the 2000 ms probe position was slower during task-relevant shapes than during task-irrelevant shapes in the low-difficulty group. This difference declined as the discrimination difficulty was increased, such that there was no difference in the high-difficulty group. Secondary RT was slower during task-irrelevant shapes than during task-relevant shapes only in the medium-difficulty group-and only at the 150 ms probe position in the first half of the experiment. The close relationship between autonomic orienting and secondary RT at the 2000 ms probe position suggests that orienting reflects the resource allocation that results from the number of matching features between a stimulus input and a mental representation primed as significant.