Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.

An efficient adaptive scheduling policy for high-performance computing

The advent of commodity-based high-performance clusters has raised parallel and distributed computing to a new level. However, in order to achieve the best possible performance improvements for large-scale computing problems as well as good resource utilization, efficient resource management and scheduling is required. This paper proposes a new two-level adaptive space-sharing scheduling policy for non-dedicated heterogeneous commodity-based high-performance clusters. Using trace-driven simulation, the performance of the proposed scheduling policy is compared with existing adaptive space-sharing policies. Results of the simulation show that the proposed policy performs substantially better than the existing policies.

Determining service trustworthiness in inter loud computing environments

Deployment of applications and scientific workflows that require resources from multiple distributed platforms are fuelling the federation of autonomous clouds to create cyber infrastructure environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. In this paper, we present a fully distributed framework that enable interested parties determine the trustworthiness of federated cloud computing entities.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems–which is the focus of this paper–is very important. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrate that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. On the basis of the validated model, we propose an adaptive assignment function based on the existing heterogeneity of the system to minimize multi-user environment overhead.

Human-computer interaction in ubiquitous computing environments

Purpose – The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings.

Design/methodology/approach – The paper quantifies the performance of human movement based on Fitt's Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium.

Findings – The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm.

Practical implications – In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user's body-based interaction styles.

Originality/value – The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings.

Mining frequent itemsets in distorted databases with granula computing

Data perturbation is a popular method to achieve privacy-preserving data mining. However, distorted databases bring enormous overheads to mining algorithms as compared to original databases. In this paper, we present the GrC-FIM algorithm to address the efficiency problem in mining frequent itemsets from distorted databases. Two measures are introduced to overcome the weakness in existing work: firstly, the concept of independent granule is introduced, and granule inference is used to distinguish between non-independent itemsets and independent itemsets. We further prove that the support counts of non-independent itemsets can be directly derived from subitemsets, so that the error-prone reconstruction process can be avoided. This could improve the efficiency of the algorithm, and bring more accurate results; secondly, through the granular-bitmap representation, the support counts can be calculated in an efficient way. The empirical results on representative synthetic and real-world databases indicate that the proposed GrC-FIM algorithm outperforms the popular EMASK algorithm in both the efficiency and the support count reconstruction accuracy.

Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model

When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Mart´ınez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale.We find that the key of computational techniques
based on linguistic 2-tuples is to set suitable numerical scale with
the purpose of making transformations between linguistic 2-tuples
and numerical values. By defining the concept of the transitive
calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.

Parallel computing on clusters and enterprise grids : practice and experience

We assert that companies can make more money and research institutions can improve their performance if inexpensive clusters and enterprise grids are exploited. In this paper, we have demonstrated that our claim is valid by showing the study of how programming environments, tools and middleware could be used for the execution of parallel and sequential applications, multiple parallel applications executing simultaneously on a non-dedicated cluster, and parallel applications on an enterprise grid and that the execution performance was improved. For this purpose an execution environment, and parallel and sequential benchmark applications selected for, and used in, the experiments were characterised.

An empirical investigation of women's affective responses to filmed violence

This thesis examined the subjective emotional responses of women to depictions of violent and sexually violent film. Findings highlighted the significance of contextual factors, such as the gender of the perpetrator and victim upon viewers, and found that exposure to sexual violence in the media has important implications for women. The portfolio's four case studies demonstrate the complexities involved in making risk judgements and treatment planning given the diversity of offences and needs, as well as the implications these decisions can have when determining the amenability of offenders to specific sex offender treatment.

Individual differences in affective responses to violence and sexual violence

The thesis aimed to identify and compare individual differences in anger related affective responses upon exposure to neutral, violent and sexually violent film. The findings revealed that both graphic and non-graphic sexually violent film content may have a stronger impact on viewers' anger levels than exposure to standard violent film. The portfolio examined in four case studies the utility of the Violence Risk Scale when seeking to identify factors associated with risk of violent recidivism and subsequent treatment targets for intellectually disabled offenders . Treatment indications must consider the impaired cognitive and adaptive abilities and the difficulties inherent in modifying the behavioural characteristics of intellectually disabled offenders.

Comparing multiple discrepancies theory to affective models of subjective wellbeing

The Subjective Wellbeing (SWB) literature is replete with competing theories detailing the mechanisms underlying the construction and maintenance of SWB. The current study aimed to compare and contrast two of these approaches: multiple discrepancies theory (MDT) and an affective-cognitive theory of SWB. MDT posits SWB to be the result of perceived discrepancies between multiple standards of comparison. By contrast, affective-cognitive theory asserts that SWB is primarily influenced by trait affect, and indirectly influenced by personality and cognition through trait affect. Participants comprised 387 individuals who responded to the 5th longitudinal survey of the Australian Unity Wellbeing Index. Results of Structural Equation Modelling (SEM) indicated the poorest fit to the data for the MDT model. The affective-cognitive model also did not provide a good fit to the data. A purely affective model provided the best fit to the data, was the most parsimonious, and explained 66% of variance in SWB.

Energy-efficient management of data center resources for cloud computing : a vision, architectural elements, and open challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.

Regular exercise participation mediates the affective response to acute bouts of vigorous exercise

Physical inactivity is a leading factor associated with cardiovascular disease and a major contributor to the global burden of disease in developed countries. Subjective mood states associated with acute exercise are likely to influence future exercise adherence and warrant further investigation. The present study examined the effects of a single bout of vigorous exercise on mood and anxiety between individuals with substantially different exercise participation histories. Mood and anxiety were assessed one day before an exercise test (baseline), 5 minutes before (pre-test) and again 10 and 25 minutes post-exercise. Participants were 31 university students (16 males, 15 females; Age M = 20), with 16 participants reporting a history of regular exercise with the remaining 15 reporting to not exercise regularly. Each participant completed an incremental exercise test on a Monark cycle ergometer to volitional exhaustion. Regular exercisers reported significant post-exercise improvements in mood and reductions in state anxiety. By contrast, non-regular exercisers reported an initial decline in post-exercise mood and increased anxiety, followed by an improvement in mood and reduction in anxiety back to pre-exercise levels. Our findings suggest that previous exercise participation mediates affective responses to acute bouts of vigorous exercise. We suggest that to maximise positive mood changes following exercise, practitioners should carefully consider the individual's exercise participation history before prescribing new regimes.