Search results

In modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.

In this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.

Resource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.

The main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.

As a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

This paper compares six reliability-based design optimization (RBDO) approaches dealing with uncertainties for a simple mathematical model and a multidisciplinary optimization problem of a safety transformer to highlight the most effective.

The RBDO and various approaches to calculate the probability of failure are is presented. They are compared in terms of precision and number of evaluations on mathematical and electromagnetic design problems.

The mathematical example shows that the six RBDO approaches have almost the same results except the approximate moment approach that is less accurate. The optimization of the safety transformer highlights that not all the methods can converge to the global solution. Performance measure approach, single-loop approach and sequential optimization and reliability assessment (SORA) method appear to be more stable. Considering both numerical examples, SORA is the most effective method among all RBDO approaches.

The comparison of six RBDO methods on the optimization problem of a safety transformer is achieved for the first time. The comparison in terms of precision and number of evaluations highlights the most effective ones.

There is a rising demand for high-performance 3D printed objects that have established potential applications in the sector of dental, automotive, electronics, aerospace, etc. Thus, to meet the requirements of high-performance 3D printed objects, this study has synthesized, formulated and applied a resorcinol epoxy acrylate (REA) oligomer to a stereolithography (SLA) 3D printer.

Different formulations were developed by blending reactive diluents in the concentration of 10%, 15% and 20%, along with the fixed quantity of photo-initiators in the REA oligomer. The structure of synthesized REA oligomer was confirmed using 13 C nuclear magnetic resonance (NMR) and 1H NMR spectroscopy, and the rheological properties for prepared REA formulations were also evaluated. The ultraviolet (UV)-cured specimens of all REA formulations were thoroughly examined based on physical, chemical, optical, mechanical and thermal properties. The best suitable formulation was selected for SLA 3D printing.

As perceived, UV cured REA specimens exhibit superior mechanical, chemical and thermal properties, portraying the ability to use as a high-performance material. The increase in the concentration of reactive diluents indicated a significant improvement in the properties of REA resin. The 20% diluted formulation achieved excellent compatibility with a SLA 3D printer; thus, 3D objects are cast with good dimensional stability and printability.

Resorcinol-based resins have always been a key additive used to enhance properties in the coating and tire industry. In a new attempt UV, curable REA has been applied to a SLA 3D printer to cast high-performance 3D printed objects.

The term, ‘mixed methods’ is, of course, very broad. In general, though, it is used to describe an approach which combines more than one type of data or more than one type of analysis and, more often than not, refers to research which draws on both qualitative and quantitative data and analyses. This chapter analyses the methods adopted in a two-year study of Sri Lanka's post-tsunami housing policy, showing how quantitative and qualitative data can be triangulated in a variety of ways, and demonstrating the value of qualitative methods in contributing to understanding of different aspects of policy process and impact.

This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work.

In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided.

It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research.

The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports.

Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making.

Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/techniques and opportunities.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.

propose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.

The findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.

Limited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.

The SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

The purpose of this paper is to explore lecturers’ perceptions on factors that affect the implementation of bilingual instruction (BI) policy in Indonesian higher education.

This qualitative study used 15 lecturers who taught in BI programs in three Indonesian universities. The data were gained through semi-structured interviews. The semi-structured interview data were analyzed via thematic approach.

The findings of the study suggest a number of factors influencing the implementation of BI, including the support from lecturers, leadership, and government. The availability of adapted curriculum and systematic assessment also influences the successful implementation of BI in Indonesian tertiary education.

The findings of this study have implications for the success of similar programs and the ways to gain understanding of BI within higher education contexts.

BI research is not new; however, little information is related to BI in Indonesia. This work contributes to a growing body of literature that explore BI and education factors within higher education setting. The significance of this study is to raise greater understanding of several important factors that influence the implementation of BI policy within university settings.

Optimization techniques can be used to design geometrically complex components with a wide variety of optimization criteria. However, such components have been very difficult and costly to produce. Layered fabrication technologies such as electron beam melting (EBM) open up new possibilities though. This paper seeks to investigate the integration of structural optimization and direct metal fabrication process.

Mesh structures were designed, and optimization problems were defined to improve structural performance. Finite element analysis code in conjunction with nonlinear optimization routines were used in MATLAB. Element data were extracted from an STL‐file, and output structures from the optimization routine were manufactured using an EBM machine. Original and optimized structures were tested and compared.

There were discrepancies between the performance of the theoretical structures and the physical EBM structures due to the layered fabrication approach. A scaling factor was developed to account for the effect of layering on the material properties.

Structural optimization can be used to improve the performance of a design, and direct fabrication technologies can be used to realise these structures. However, designers must realize that fabricated structures are not identical to idealized CAD structures, hence material properties much be adjusted accordingly.

Integration of structural optimization and direct metal fabrication was reported in the paper. It shows the process from design through manufacturing with integrated analysis.

Determining whether a set of tolerances on a part can be met by a rapid prototyping (RP) machine is often difficult. To achieve a set of tolerances as closely as possible, relationships between part geometry, tolerances, and process variables must be understood quantitatively. This paper presents an empirical model for stereolithography apparatus (SLA) accuracy, as specified by geometric tolerances, and a process planning method based on response surface methodology and multiobjective optimization. Response surfaces are used to capture the relationships among part surfaces, tolerances, and process variables. These response surfaces were generated by extensive design‐of‐experiment studies for a variety of geometries. An annotated STL data format is also presented that enables the inclusion of tolerance and surface information in faceted representations. Application of the accuracy models and process planning method is illustrated on one part.