A study of adaptive load balancing algorithms for distributed systems

With the advent of distributed computer systems with a largely transparent user interface, new questions have arisen regarding the management of such an environment by an operating system. One fertile area of research is that of load balancing, which attempts to improve system performance by redistributing the workload submitted to the system by the users. Early work in this field concentrated on static placement of computational objects to improve performance, given prior knowledge of process behaviour. More recently this has evolved into studying dynamic load balancing with process migration, thus allowing the system to adapt to varying loads. In this thesis, we describe a simulated system which facilitates experimentation with various load balancing algorithms. The system runs under UNIX and provides functions for user processes to communicate through software ports; processes reside on simulated homogeneous processors, connected by a user-specified topology, and a mechanism is included to allow migration of a process from one processor to another. We present the results of a study of adaptive load balancing algorithms, conducted using the aforementioned simulated system, under varying conditions; these results show the relative merits of different approaches to the load balancing problem, and we analyse the trade-offs between them. Following from this study, we present further novel modifications to suggested algorithms, and show their effects on system performance.

Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Examining the strategy development process through the lens of complex adaptive systems theory

The development of strategy remains a debate for academics and a concern for practitioners. Published research has focused on producing models for strategy development and on studying how strategy is developed in organisations. The Operational Research literature has highlighted the importance of considering complexity within strategic decision making; but little has been done to link strategy development with complexity theories, despite organisations and organisational environments becoming increasingly more complex. We review the dominant streams of strategy development and complexity theories. Our theoretical investigation results in the first conceptual framework which links an established Strategic Operational Research model, the Strategy Development Process model, with complexity via Complex Adaptive Systems theory. We present preliminary findings from the use of this conceptual framework applied to a longitudinal, in-depth case study, to demonstrate the advantages of using this integrated conceptual model. Our research shows that the conceptual model proposed provides rich data and allows for a more holistic examination of the strategy development process. © 2012 Operational Research Society Ltd. All rights reserved.

Strategic development process and complex adaptive systems

The two areas of theory upon which this research was based were „strategy development process?(SDP) and „complex adaptive systems? (CAS), as part of complexity theory, focused on human social organisations. The literature reviewed showed that there is a paucity of empirical work and theory in the overlap of the two areas, providing an opportunity for contributions to knowledge in each area of theory, and for practitioners. An inductive approach was adopted for this research, in an effort to discover new insights to the focus area of study. It was undertaken from within an interpretivist paradigm, and based on a novel conceptual framework. The organisationally intimate nature of the research topic, and the researcher?s circumstances required a research design that was both in-depth and long term. The result was a single, exploratory, case study, which included use of data from 44 in-depth, semi-structured interviews, from 36 people, involving all the top management team members and significant other staff members; observations, rumour and grapevine (ORG) data; and archive data, over a 5½ year period (2005 – 2010). Findings confirm the validity of the conceptual framework, and that complex adaptive systems theory has potential to extend strategy development process theory. It has shown how and why the strategy process developed in the case study organisation by providing deeper insights to the behaviour of the people, their backgrounds, and interactions. Broad predictions of the „latent strategy development? process and some elements of the strategy content are also possible. Based on this research, it is possible to extend the utility of the SDP model by including peoples? behavioural characteristics within the organisation, via complex adaptive systems theory. Further research is recommended to test limits of the application of the conceptual framework and improve its efficacy with more organisations across a variety of sectors.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.

Investigating tiredness and ad complexity effects on visual processing of advertisements:an eye-tracking study

Although advertising is pervasive in our daily, it proves to be not necessarily efficient all the times due to bad conditions or bad contexts of reception. Indeed, the communication process might be jeopardized at its very last stage because of advertising exposure quality. However critical it may be, ad exposure quality is not very much examined by researchers or practitioners. In this paper, we investigate how tiredness combined with ad complexity might influence the way consumers extract and process ad elements. Investigating tiredness is useful because it is a common daily state experienced by everyone at various moments of the day. And although it might drastically alter ad reception, it has not been studied in advertising for the moment. In this regards, we observe eye movement patterns of consumers viewing simple or complex advertisements being tired or not. We surprisingly find that tired subjects viewing complex ads don’t adopt a lessening effort visual strategy. They rather use a resource demanding one. We assume that the Sustained Attention strategy occurring is a kind of adaptive strategy allowing to deal with an anticipated lack of resource.

The psychosocial impact of an activity holiday for young children with severe food allergy:a longitudinal study

Background Food allergy has been shown to severely affect quality of life (QoL) in children and their families. The Anaphylaxis Campaign UK supports families with allergic children and as part of that support ran an activity holiday for those with food allergy. This study investigated the effectiveness of this activity holiday for reducing anxiety and improving QoL and food allergy management for these children. Methods Measures were taken at baseline, at the start of the activity holiday, at the end of the holiday, at 3 and 6 months follow-up. Children (n = 24) completed a paediatric food allergy–specific QoL questionnaire (PFA-QL), a generic QoL questionnaire (PedsQL™), the Spence Children's Anxiety Scale (SCAS) and the Children's Health Locus of Control (CHLC) scale at all stages of the study. Results There were significant improvements in social QoL, food allergy–specific QoL, total CHLC and internal locus of control scores over time (p > 0.05). There were significant decreases in powerful others locus of control, total anxiety and obsessive compulsive disorder scores (p < 0.05). Greater anxiety significantly correlated with poorer QoL at all time points; no correlations with locus of control were significant at the 3- and 6-month follow-up. Conclusions The activity holiday was of significant benefit to the children who took part, providing support for the need for activity holidays such as this for children with severe food allergy. Ways in which adaptive locus of control and improved quality of life can be facilitated need to be further explored.

Are transglutaminase 2 inhibitors able to reduce gliadin-induced toxicity related to celiac disease? A proof-of-concept study

Purpose Celiac disease is an autoimmune-mediated enteropathy characterized by adaptive and innate immune responses to dietary gluten in wheat, rye and barley in genetically susceptible individuals. Gluten-derived gliadin peptides are deamidated by transglutaminase 2 (TG2), leading to an immune response in the small-intestinal mucosa. TG2 inhibitors have therefore been suggested as putative drugs for celiac disease. In this proof-of-concept study we investigated whether two TG2 inhibitors, cell-impermeable R281 and cell-permeable R283, can prevent the toxic effects of gliadin in vitro and ex vivo. Methods Intestinal epithelial Caco-2 cells were treated with peptic-tryptic-digested gliadin (PT-gliadin) with or without TG2 inhibitors and thereafter direct toxic effects (transepithelial resistance, cytoskeletal rearrangement, junction protein expression and phoshorylation of extracellular-signal-regulated kinase 1/2) were determined. In an organ culture of celiacpatient- derived small-intestinal biopsies we measured secretion of TG2-autoantibodies into the culture medium and the densities of CD25- and interleukin (IL) 15-positive cells, forkhead box P3 (FOXP3)-positive regulatory Tcells (Tregs) and Ki-67- positive proliferating crypt cells. Results Both TG2 inhibitors evinced protective effects against gliadin-induced detrimental effects in Caco-2 cells but the cellimpermeableR281seemedslightlymorepotent. Inaddition,TG2 inhibitor R281 modified the gluten-induced increase in CD25- and IL15-positive cells,Tregs and crypt cell proliferation, but had no effect on antibody secretion in celiac-patient-derived biopsies. Conclusions Our results suggest that TG2 inhibitors are able to reduce certain gliadin-induced effects related to responses in vitro and ex vivo. © Springer Science+Business Media, LLC 2012.

Maternal mind-mindedness during infancy, general parenting sensitivity and observed child feeding behavior:a longitudinal study

Maternal mind-mindedness, or the tendency to view the child as a mental agent, has been shown to predict sensitive and responsive parenting behavior. As yet the role of mind-mindedness has not been explored in the context of feeding interactions. This study evaluates the relations between maternal mind-mindedness at 6 months of infant age and subsequently observed maternal sensitivity and feeding behaviors with children at age 1 year. Maternal mind-mindedness was greater in mothers who had breast-fed compared to formula-fed. Controlling for breast-feeding, mind-mindedness at 6 months was correlated with observations of more sensitive and positive feeding behaviors at 1 year of age. Mind-mindedness was also associated with greater general maternal sensitivity in play and this general parenting sensitivity mediated the effect of mind-mindedness on more sensitive and positive feeding behaviors. Interventions to promote maternal tendency to consider their child's mental states may encourage more adaptive parental feeding behaviors. © 2014 Taylor & Francis.

Adaptive modulation scaling scheme for wireless sensor networks

Limited energy is a big challenge for large scale wireless sensor networks (WSN). Previous research works show that modulation scaling is an efficient technique to reduce energy consumption. However, the impacts of using modulation scaling on packet delivery latency and loss are not considered, which may have adverse effects on the application qualities. In this paper, we study this problem and propose control schemes to minimize energy consumption while ensuring application qualities. We first analyze the relationships of modulation scaling and energy consumption, end-to-end delivery latency and packet loss ratio. With the analytical model, we develop a centralized control scheme to adaptively adjust the modulation levels, in order to minimize energy consumption and ensure the application qualities. To improve the scalability of the centralized control scheme, we also propose a distributed control scheme. In this scheme, the sink will send the differences between the required and measured application qualities to the sensors. The sensors will update their modulation levels with the local information and feedback from the sink. Experimental results show the effectiveness of energy saving and QoS guarantee of the control schemes. The control schemes can adapt efficiently to the time-varying requirements on application qualities. Copyright © 2005 The Institute of Electronics, Information and Communication Engineers.

Adaptive responses by mouse early embryos to maternal diet protect fetal growth but predispose to adult onset disease

Poor maternal nutrition during pregnancy can alter postnatal phenotype and increase susceptibility to adult cardiovascular and metabolic diseases. However, underlying mechanisms are largely unknown. Here, we show that maternal low protein diet (LPD), fed exclusively during mouse preimplantation development, leads to offspring with increased weight from birth, sustained hypertension, and abnormal anxiety-related behavior, especially in females. These adverse outcomes were interrelated with increased perinatal weight being predictive of later adult overweight and hypertension. Embryo transfer experiments revealed that the increase in perinatal weight was induced within blastocysts responding to preimplantation LPD, independent of subsequent maternal environment during later pregnancy. We further identified the embryo-derived visceral yolk sac endoderm (VYSE) as one mediator of this response. VYSE contributes to fetal growth through endocytosis of maternal proteins, mainly via the multiligand megalin (LRP2) receptor and supply of liberated amino acids. Thus, LPD maintained throughout gestation stimulated VYSE nutrient transport capacity and megalin expression in late pregnancy, with enhanced megalin expression evident even when LPD was limited to the preimplantation period. Our results demonstrate that in a nutrient-restricted environment, the preimplantation embryo activates physiological mechanisms of developmental plasticity to stablize conceptus growth and enhance postnatal fitness. However, activation of such responses may also lead to adult excess growth and cardiovascular and behavioral diseases. © 2008 by the Society for the Study of Reproduction, Inc.

Energy aware data gathering based on adaptive modulation scaling in wireless sensor networks

Energy consumption has been a key concern of data gathering in wireless sensor networks. Previous research works show that modulation scaling is an efficient technique to reduce energy consumption. However, such technique will also impact on both packet delivery latency and packet loss, therefore, may result in adverse effects on the qualities of applications. In this paper, we study the problem of modulation scaling and energy-optimization. A mathematical model is proposed to analyze the impact of modulation scaling on the overall energy consumption, end-to-end mean delivery latency and mean packet loss rate. A centralized optimal management mechanism is developed based on the model, which adaptively adjusts the modulation levels to minimize energy consumption while ensuring the QoS for data gathering. Experimental results show that the management mechanism saves significant energy in all the investigated scenarios. Some valuable results are also observed in the experiments. © 2004 IEEE.