A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.

Comparing alternate teaching styles to teach computing skills to girls in their English classes

Low female participation rates in computing are a current concern of the education sector. To address this problem an intervention was developed — computing skills were introduced to girls in their English classes using three different teaching styles: peer tutoring, cross-age tutoring and teacher instruction (control). The sample comprised 136 girls from Years 8 and 10 from a single-sex government school. A pre-test post-test quantitative design was used. To describe the students perspectives, qualitative data were collected from six focus groups conducted with 8–10 students — one from each of the six classes. It was predicted that cross-age tutoring would yield more positive effects than peer tutoring which, in turn, would yield more positive effects than traditional teacher instruction as assessed by achievement on class tasks and attitudes towards computing. The hypothesis was not supported by the quantitative analysis, however in the qualitative data cross-age tutoring was appraised more favourably than peer tutoring or teacher instruction. The latter was the least preferred condition due to: (1) inefficiency; (2) difficulty understanding teachers' explanations; and (3) lack of teacher knowledge. Problems with the implementation of the intervention identified in the focus groups were teacher differences, system failures, missed classes, lack of communication, and selection of computing activities. Practical suggestions were provided relevant to the introduction of cross-age tutoring and the use of computers within secondary level English classes.

The core of the global warming problem: energy

From the thermodynamic point of view, the global warming problem is an ''energy balance'' problem. The heat (energy) accumulation in the earth and its atmosphere is the cause of the global warming. This accumulation is mainly due to the imbalance of (solar) energy reaching and the energy leaving the earth, caused by ''greenhouse effect'' in which the CO₂ and other greenhouse gases play a critical role; so that balance of the energy entering and leaving the earth should be the key to solve the problem. Currently in the battle of tackling the global warming, we mainly focus on the development of CO₂-related measures, i.e., emission reduction, CO₂ sequestration, and CO₂ recycle technologies. It is right in technical aspect, because they are attempting thinner the CO₂ ''blanket'' around the earth. However, ''Energy'' that is the core of the problem has been overlooked, at least in management/policy aspect. This paper is proposing an ''Energy Credit'' i.e., the energy measure concept as an alternative to the ''CO₂ credit'' that is currently in place in the proposed emission trading scheme. The proposed energy credit concept has the advantages such as covering broad activities related to the global warming and not just direct emissions. Three examples are given in the paper to demonstrate the concept of the energy measure and its advantages over the CO₂ credit concept.

Using an IDMI methodology to make multi-criteria decisions

Multi-criteria decisions usually require measurement or evaluation of performance in different units and their mix by application of weighting factors. This approach leads to potential manipulation of the results as a direct consequence of the applied weightings. In this paper a mechanism has been proposed to overcome this problem. It is known as the : Interlink Decision Making Index (IDMI) and has all the desired features: simple, interlink (all criteria) and automatically and quantified influence of critical criteria (i.e. no human weighting needed). The IDMI is capable of reflecting the total merits of a particular option once the normal decision making criteria and (up to two) critical criteria (CC) have been chosen. Then, without arbitrarily weighting criteria, comparison and selection of the best possible option can be made. Simple software has been developed to do this numerical transfer and graphic presentation. Two hypothetical examples are presented in the paper to demonstrate the application of the IDMI concept and its advantages over the traditional "tabular and weighting method" in the decision making process.

Applying a reconfigurable multi-agent scheduler to product distribution

More intelligent scheduling methods are required for manufacturing scheduling due to the move to more agile systems. Multi-agent methods are one such approach. This paper describes the application of a reconfigurable multi-agent scheduler to the problem of allocating orders to warehouses in a distribution supply chain. This multi-agent system was originally developed for allocation of orders to machines in a highly reconfigurable manufacturing system and this work was aimed at investigating the ease of applying this same scheduler to other problems. It was found that this new application was readily achieved because of the modular structure of the scheduler. This paper shows how the application to the new problem was achieved.

The application of soft systems methodology to supply chain management

This chapter reflects upon techniques that might facilitate improved strategic decision making in a supply chain management (SCM) environment. In particular, it presents the integration of a selection of techniques adapted from an approach to systems-based problem solving that has emerged primarily in the UK over the last 20-30 years—the soft systems methodology (SSM). The results reported indicate that SSM techniques can complement existing SCM decision-making tools. In particular, this chapter outlines a framework for integrating some SSM techniques with approaches based upon the supply-chain operations reference-model (SCOR) .

Structural equation modelling of complex sample survey : an application to brand signalling data

The purpose of this paper is to present an empirical analysis of complex sample data with regard to the biasing effect of nonindependence of observations on standard error parameter estimates. In a two-factor confirmatory factor analysis model, using real data, we show how the bias in standard errors can be derived when the nonindependence is ignored. We demonstrate that the standard error bias produced by the nonindependence of observations can be considerable and we briefly discuss solutions to overcome the problem.

Towards insightful algorithm selection for optimisation using meta-learning concepts

In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several meta-heuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods.

The evidential problem of evil

The evidential problem of evil is the problem of determining whether and (if so) to what extent the existence of evil (or certain instances, kinds, quantities, or distributions of evil) constitutes evidence against the existence of God, that is to say, a being perfect in power, knowledge and goodness. Evidential arguments from evil attempt to show that, once we put aside any evidence there might be in support of the existence of God, it becomes unlikely, if not highly unlikely, that the world was created and is governed by an omnipotent, omniscient, and wholly good being. Such arguments are not to be confused with logical arguments from evil, which have the more ambitious aim of showing that, in a world in which there is evil, it is logically impossible – and not just unlikely – that God exists.

This entry begins by clarifying some important concepts and distinctions associated with the problem of evil, before providing an outline of one of the more forceful and influential evidential arguments developed in contemporary times, viz., the evidential argument advanced by William Rowe. Rowe’s argument has occasioned a range of responses from theists, including the so-called "skeptical theist" critique (according to which God’s ways are too mysterious for us to comprehend) and the construction of various theodicies, that is, explanations as to why God permits evil. These and other responses to the evidential problem of evil are here surveyed and assessed.

Application of real and functional analysis to solve boundary value problems

This thesis is about using appropriate tools in functional analysis arid classical analysis to tackle the problem of existence and uniqueness of nonlinear partial differential equations. There being no unified strategy to deal with these equations, one approaches each equation with an appropriate method, depending on the characteristics of the equation. The correct setting of the problem in appropriate function spaces is the first important part on the road to the solution. Here, we choose the setting of Sobolev spaces. The second essential part is to choose the correct tool for each equation. In the first part of this thesis (Chapters 3 and 4) we consider a variety of nonlinear hyperbolic partial differential equations with mixed boundary and initial conditions. The methods of compactness and monotonicity are used to prove existence and uniqueness of the solution (Chapter 3). Finding a priori estimates is the main task in this analysis. For some types of nonlinearity, these estimates cannot be easily obtained, arid so these two methods cannot be applied directly. In this case, we first linearise the equation, using linear recurrence (Chapter 4). In the second part of the thesis (Chapter 5), by using an appropriate tool in functional analysis (the Sobolev Imbedding Theorem), we are able to improve previous results on a posteriori error estimates for the finite element method of lines applied to nonlinear parabolic equations. These estimates are crucial in the design of adaptive algorithms for the method, and previous analysis relies on, what we show to be, unnecessary assumptions which limit the application of the algorithms. Our analysis does not require these assumptions. In the last part of the thesis (Chapter 6), staying with the theme of choosing the most suitable tools, we show that using classical analysis in a proper way is in some cases sufficient to obtain considerable results. We study in this chapter nonexistence of positive solutions to Laplace's equation with nonlinear Neumann boundary condition. This problem arises when one wants to study the blow-up at finite time of the solution of the corresponding parabolic problem, which models the heating of a substance by radiation. We generalise known results which were obtained by using more abstract methods.

From quality assurance to process improvement : an action research project

This action research project was undertaken at the Casino & District Memorial Hospital in northern N.S.W. during 1995 & 1996. The purpose was to utilise the Action Research frameork to enable the participants to improve their problem solving skills in relation to quality improvement strategies within the Nursing Division.

Evidence-informed process to identify policies that will promote a healthy food environment in the Pacific Islands

Objective: To implement a systematic evidence-informed process to enable Fiji and Tonga to identify the most feasible and targeted policy interventions which would have most impact on diet-related non-communicable diseases.

Design: A multisectoral stakeholder group of policy advisers was formed in each country. They used participatory approaches to identify the problem policies and gaps contributing to an unhealthy food environment. Potential solutions to these problems were then identified, and were assessed by them for feasibility, effectiveness, cost-effectiveness and side-effects. Data were gathered on the food and policy environment to support the assessments. A shortlist of preferred policy interventions for action was then developed.

Results: Sixty to eighty policy problems were identified in each country, affecting areas such as trade, agriculture, fisheries and pricing. Up to 100 specific potential policy solutions were then developed in each country. Assessment of the policies highlighted relevant problem areas including poor feasibility, limited effectiveness or cost-effectiveness and serious side-effects. A shortlist of twenty to twenty-three preferred new policy options for action in each country was identified.

Conclusions: Policy environments in these two countries were not conducive to supporting healthy eating. Substantial areas of potential action are possible, but some represent better choices. It is important for countries to consider the impact of non-health policies on diets.

An approach to filtering duplicate RFID data streams

In a system where distributed network of Radio Frequency Identification (RFID) readers are used to collaboratively collect data from tagged objects, a scheme that detects and eliminates redundant data streams is required. To address this problem, we propose an approach that is based on Bloom filter to detect duplicate readings and filter redundant RFID data streams. We have evaluated the performance of the proposed approach and compared it with existing approaches. The experimental results demonstrate that the proposed approach provides superior performance as compared to the baseline approaches.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Aesthetic anxieties : the problem of defining the migrant house in Australia

The interest of this paper concerns the problem about the migrant house – why has it been so difficult to define? In order to examine the problem of the lack of literature on the migrant house it is important to look at the literature on the Australian house, and to examine how the migrant house is positioned or not included in this literature. It will approach this through what is accepted as discourse analysis, but with a particular position informed by the work of Stuart Hall on representation.