An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method.

Transition: improved literacy outcomes research study

This paper reports on the Transition: Improved Literacy Outcomes Research Study established by the Department of Education and Training (DET). The purpose of the study was to identify the most useful and usable data for educators to access in order to ensure that student literacy learning is maximised during the transition from Primary to Secondary school.1. The research followed a mixed-methods approach, combining a statistically defensible sample of schools selected to represent school types (primary, secondary, combined), locations (rural, regional, urban) and all regions with a survey and follow- up interviews.2. The results are presented as a mixture of statistics, case studies and discussion. The case studies were developed from both survey data and interview data.3. Leaders and teachers overwhelmingly valued teacher judgement (through AusVELS) as the most useful data in understanding students’ strengths and weaknesses, followed by On-Demand testing.4. NAPLAN results were frequently transferred between schools, but were found to be less useful than other data by teachers.5. School leaders and teachers are currently using data for ability streaming, or for tailoring curriculum for individual student needs. A number of schools use the Literacy data for achieving a balance of student abilities within classes.6. Support for access to, and use of, data is mainly manual, with some use of spreadsheets.7. As the number of feeder Primary schools to a single Secondary school increases, data management becomes a much larger burden on the Secondary staff concerned.8. In Secondary schools, there was a clear demand for Literacy data, but these data were not always those that were being provided by their feeder Priamary schools. This mis-match appears to lead to some frustration among Literacy transition staff at both levels of schooling.9. A gap that needs filling is around the nature of the pedagogies at the two school levels. Not knowing, or misunderstanding, the approaches and needs of each level of schooling leads to the passing on of irrelevant Literacy data in some instances.10. The need for a common template for transition data was expressed by many Transition staff, both Primary and Secondary. In some instances the secondary school had set up a local template for this11. It would be advantageous for data to be directly up-loaded or transferred into a system via electronic tools already in use, such as an Excel spreadsheet.12. We would recommend that the capacity to provide some visualisation of data, and the compilation of internal Literacy data such as teacher judgements and AusVELS levels, or additional testing carried out by the school.13. Student background data was seen by many Secondary transition staff as being equally as valuable as more formal Literacy data for determining student needs.

Impact of past and future residential housing development patterns on energy demand and related emissions

The continued outward growth from a central business district has been the dominant characteristic of most cities in Australia. However, this feature is seen as unsustainable and alternative scenarios to contain the outward growth are being proposed. Melbourne is currently grappling with this issue while simultaneously trying to reduce per capita greenhouse gas emissions. Housing size, style and its location are the three principal factors which determine the emissions from the residential sector. This paper describes a methodology to assess the combined impact of these factors on past and possible future forms of residential development in Melbourne. The analysis found that the location of the housing and its size are the dominant factors determining energy use and greenhouse gas emissions.

The influence of surface forces on thin film drainage between a fluid drop and a flat solid

An experiment is described in which a mica surface is driven towards a mercury drop immersed in aqueous electrolyte. Under appropriate conditions, hydrodynamic pressure in the aqueous film creates a classical dimple in the mercury drop. The use of optical interferometry and video recording to monitor the shape of the drop and the thickness of the aqueous film with sub-nanometre resolution yields a high density of precise data showing the formation and evolution of the dimple as the film drains. Variation of electrical potential applied to the mercury phase allows control of the surface forces acting between the drop and the mica surface, so that the effect of surface forces on the film drainage process is highlighted. It is found that the film thickness at the centre of the dimple and the lateral extent of the dimple are not significantly affected by surface forces. On the other hand, the minimum film thickness at the edge of the dimple is sensitive even to weak surface forces. Since this minimum film thickness is a major determinant of the film drainage rate, it is shown that surface forces have an important effect on the overall drainage process.

The effect of surface and hydrodynamic forces on the shape of a fluid drop approaching a solid surface

This paper describes an experiment designed to measure surface and hydrodynamic forces between a mercury drop and a flat mica surface immersed in an aqueous medium. An optical interference technique allows measurement of the shape of the mercury drop as well as its distance from the mica, for various conditions of applied potential, applied pressure, and solution conditions. This enables a detailed exploration of the surface forces, particularly double-layer forces, between mercury and mica. A theoretical analysis of drop shape under the influence of surface forces shows that deformation of the drop is a sensitive indicator of the forces, as well as being a very important factor in establishing the overall interaction between the solid and the fluid.

Expansion of congested railway capacity using triple-track line operation based on hourly passenger demand

This paper proposes a method of improving level of service in congested urban railways by means of a triple-track line operation for a highly dense urban area with special travel demand characteristics. Where the future travel demand forecasts show sluggish growth or no growth at all, there is little to no incentives for heavy railway investments like quadruple-track extension and construction of new railway routes to alleviate current railway congestion problems. In such a situation, triple-track line operation can be the best alternative due to its moderate investment cost and ease in land acquisition for just an additional single track along the existing tracks. Our simulation investigation in one of the congested railway lines in Tokyo showed that triple track line operation increases railway capacity by 26% and shortens travel time by 38% in peak direction during morning peak hours. These results are encouraging and are useful for removing current railways problems in Tokyo and in similar urban situations elsewhere.

The impact of trade liberalization on import demand

In this paper, we empirically analyze the effects of trade reforms on import demand and derive their implications on economic development in Turkey, a country that underwent sudden and substantial trade liberalization in the mid-1980s. The tool for this analysis is the estimation of disaggregated import demand elasticities. The adoption of a more liberal trade regime as well as radical attempts to foster economic development makes the Turkish experience particularly interesting for analysis. Almost all of our elasticities are estimated to be significant, unlike those of most previous studies in the literature on other countries. We test for different elasticities over “closed” and “open” economy periods, and find that the effects of the trade reforms of the 1980s were significant for a number of industries that form the backbone of the Turkish economy. We also compare our results with elasticity estimates from past studies for developed countries.

The effects of the late 2000s global financial crisis on Australia's construction demand

An accurate measurement of the impacts of external shocks on construction demand will enable construction industry policymakers and developers to make allowances for future occurrences and advance the construction industry in a sustainable manner. This paper aims to measurethe dynamic effects of the late 2000s global financial crisis on the level of demand in the Australian construction industry. The vector error correction (VEC) model with intervention indicators is employed to estimate the external impact from the crisis on a macro-level construction economic indicator, namely construction demand. The methodology comprises six main stages to produce appropriate VEC models that describe the characteristics of the underlying process. Research findings suggestthat overall residential and non-residential construction demand were affected significantly by the recent crisis and seasonality. Non-residentialconstruction demand was disrupted more than residential construction demand at the crisis onset. The residential constructionindustry is more reactive and is able to recover faster following the crisis in comparison with the non-residential industry. The VEC model with intervention indicators developed in this study can be used as an experiment for an advanced econometric method. This can be used to analyse the effects of special eventsand factors not only on construction but also on other industries.

Does supply always come on the heels of demand? Matches and mismatches in e-learning

Developing sustainable e-learning requires a better understanding of the perceptions and preferences of e-learning providers and e-learners on the four crucial dimensions for elearning success including pedagogies, technologies, learning resources and management of learning resources. There is, however, little research on evaluating whether these critical dimensions are perceived as critical by e-learning providers and e-learners. To address this issue, this study investigates the gap between e-learners’ and e-learning providers’ perceptions and preferences on these critical dimensions for e-learning effectiveness. Such an investigation paves the way for developing appropriate measures to reduce the gap between the supply and the demand for sustainable e-learning.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Data mining analysis of an urban tunnel pressure drop based on CFD data

An accurate estimation of pressure drop due to vehicles inside an urban tunnel plays a pivotal role in tunnel ventilation issue. The main aim of the present study is to utilize computational intelligence technique for predicting pressure drop due to cars in traffic congestion in urban tunnels. A supervised feed forward back propagation neural network is utilized to estimate this pressure drop. The performance of the proposed network structure is examined on the dataset achieved from Computational Fluid Dynamic (CFD) simulation. The input data includes 2 variables, tunnel velocity and tunnel length, which are to be imported to the corresponding algorithm in order to predict presure drop. 10-fold Cross validation technique is utilized for three data mining methods, namely: multi-layer perceptron algorithm, support vector machine regression, and linear regression. A comparison is to be made to show the most accurate results. Simulation results illustrate that the Multi-layer perceptron algorithm is able to accurately estimate the pressure drop.