Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

Numerical modelling of load bearing LSF Walls under fire conditions

Abstract. Fire safety of light gauge cold-formed steel frame (LSF) stud walls is significant in the design of buildings. In this research, finite element thermal models of both the traditional LSF wall panels with cavity insulation and the new LSF composite wall panels were developed to simulate their thermal behaviour under standard and real design fire conditions. Suitable thermal properties were proposed for plasterboards and insulations based on laboratory tests and literature review. The developed models were then validated by comparing their results with available fire test results. This paper presents the details of the developed finite element models of load bearing LSF wall panels and the thermal analysis results. It shows that finite element models can be used to simulate the thermal behaviour of load bearing LSF walls with varying configurations of insulations and plasterboards. Failure times of load bearing LSF walls were also predicted based on the results from finite element thermal analyses.

Characterisation of flexural bond strength in thin bed concrete masonry

This paper presents an experimental investigation of the flexural bond strength of thin bed concrete masonry. Flexural bond strength of masonry depends upon the mortar type, the techniques of dispersion of mortar and the surface texture (roughness) of concrete blocks. There exists an abundance of literature on the conventional masonry bond containing 10mm thick mortar; however, the 2mm polymer flue mortar bond is not yet well researched. This paper reports a study on the examination of the effect of mortar compositions, dispersion methods and unit surface textures to the flexural bond strength of thin bed concrete masonry. Three types of polymer modified glue mortars, three surface textures and four techniques of mortar dispersion have been used in preparing 108 four point flexural test specimens. All mortar joints have been carefully prepared to ensure achievement of 2mm layer polymer mortar thickness on average. The results exhibit the flexural bond strength of thin bed concrete masonry much is higher than that of the conventional masonry; moreover the unit surface texture and the mortar dispersion methods are found to have significant influence on the flexural bond strength.

Thermal performance of non-load bearing LSF walls using numerical studies

Fire safety of light gauge cold-formed steel frame (LSF) wall systems is significant to the build-ing design. Gypsum plasterboard is widely used as a fire safety material in the building industry. It contains gypsum (CaSO4.2H2O), Calcium Carbonate (CaCO3) and most importantly free and chemically bound water in its crystal structure. The dehydration of the gypsum and the decomposition of Calcium Carbonate absorb heat, which gives the gypsum plasterboard fire resistant qualities. Recently a new composite panel system was developed, where a thin insulation layer was used externally between two plasterboards to improve the fire performance of LSF walls. In this research, finite element thermal models of both the traditional LSF wall panels with cavity insulation and the new LSF composite wall panels were developed to simulate their thermal behaviour under standard and realistic design fire conditions. Suitable thermal properties of gypsum plaster-board, insulation materials and steel were used. The developed models were then validated by comparing their results with fire test results. This paper presents the details of the developed finite element models of non-load bearing LSF wall panels and the thermal analysis results. It has shown that finite element models can be used to simulate the thermal behaviour of LSF walls with varying configurations of insulations and plasterboards. The results show that the use of cavity insulation was detrimental to the fire rating of LSF walls while the use of external insulation offered superior thermal protection. Effects of real fire conditions are also presented.

Innovative damage assessment of steel truss bridges using modal strain energy correlation

As a part of vital infrastructure and transportation network, bridge structures must function safely at all times. Bridges are designed to have a long life span. At any point in time, however, some bridges are aged. The ageing of bridge structures, given the rapidly growing demand of heavy and fast inter-city passages and continuous increase of freight transportation, would require diligence on bridge owners to ensure that the infrastructure is healthy at reasonable cost. In recent decades, a new technique, structural health monitoring (SHM), has emerged to meet this challenge. In this new engineering discipline, structural modal identification and damage detection have formed a vital component. Witnessed by an increasing number of publications is that the change in vibration characteristics is widely and deeply investigated to assess structural damage. Although a number of publications have addressed the feasibility of various methods through experimental verifications, few of them have focused on steel truss bridges. Finding a feasible vibration-based damage indicator for steel truss bridges and solving the difficulties in practical modal identification to support damage detection motivated this research project. This research was to derive an innovative method to assess structural damage in steel truss bridges. First, it proposed a new damage indicator that relies on optimising the correlation between theoretical and measured modal strain energy. The optimisation is powered by a newly proposed multilayer genetic algorithm. In addition, a selection criterion for damage-sensitive modes has been studied to achieve more efficient and accurate damage detection results. Second, in order to support the proposed damage indicator, the research studied the applications of two state-of-the-art modal identification techniques by considering some practical difficulties: the limited instrumentation, the influence of environmental noise, the difficulties in finite element model updating, and the data selection problem in the output-only modal identification methods. The numerical (by a planer truss model) and experimental (by a laboratory through truss bridge) verifications have proved the effectiveness and feasibility of the proposed damage detection scheme. The modal strain energy-based indicator was found to be sensitive to the damage in steel truss bridges with incomplete measurement. It has shown the damage indicator's potential in practical applications of steel truss bridges. Lastly, the achievement and limitation of this study, and lessons learnt from the modal analysis have been summarised.

Fire tests of load bearing steel stud walls exposed to real building fires

Abstract. Fire resistance has become an important part in structural design due to the ever increasing loss of properties and lives every year. Conventionally the fire rating of load bearing Light gauge Steel Frame (LSF) walls is determined using standard fire tests based on the time-temperature curve given in ISO 834 [1]. Full scale fire testing based on this standard time-temperature curve originated from the application of wood burning furnaces in the early 1900s and it is questionable whether it truly represents the fuel loads in modern buildings. Hence a detailed fire research study into the performance of LSF walls was undertaken using real design fires based on Eurocode parametric curves [2] and Barnett’s ‘BFD’ curves [3]. This paper presents the development of these real fire curves and the results of full scale experimental study into the structural and fire behaviour of load bearing LSF stud wall systems.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Nerves of steel? Stress, work performance and elite athletes

There is a notable shortage of empirical research directed at measuring the magnitude and direction of stress effects on performance in a controlled environment. One reason for this is the inherent difficulties in identifying and isolating direct performance measures for individuals. Additionally, most traditional work environments contain a multitude of exogenous factors impacting individual performance, but controlling for all such factors is generally unfeasible (omitted variable bias). Moreover, instead of asking individuals about their self-reported stress levels, we observe workers’ behaviour in situations that can be classified as stressful. For this reason, we have stepped outside the traditional workplace in an attempt to gain greater controllability of these factors using the sports environment as our experimental space. We empirically investigate the relationship between stress and performance, in an extreme pressure situation (football penalty kicks) in a winner take all sporting environment (FIFA World Cup and UEFA European Cup competitions). Specifically, we examine all the penalty shootouts between 1976 and 2008 covering in total 16 events. The results indicate that extreme stressors can have a positive or negative impact on individuals’ performance. On the other hand, more commonly experienced stressors do not affect professionals’ performances.

Shear buckling characteristics of cold-formed steel channel beams

Cold-formed steel members are increasingly used as primary structural elements in the building industries around the world due to the availability of thin and high strength steels and advanced cold-forming technologies. Cold-formed lipped channel beams (LCB) are commonly used as flexural members such as floor joists and bearers. However, their shear capacities are determined based on conservative design rules. For the shear design of LCB web panels, their elastic shear buckling strength must be determined accurately including the potential post-buckling strength. Currently the elastic shear buckling coefficients of LCB web panels are determined by assuming conservatively that the web panels are simply supported at the junction between their flange and web elements. Hence finite element analyses were conducted to investigate the elastic shear buckling behavior of LCBs. An improved equation for the higher elastic shear buckling coefficient of LCBs was proposed based on finite element analysis results and included in the ultimate shear capacity equations of the North American cold-formed steel codes. Finite element analyses show that relatively short span LCBs without flange restraints are subjected to a new combined shear and flange distortion action due to the unbalanced shear flow. They also show that significant post-buckling strength is available for LCBs subjected to shear. New equations were also proposed in which post-buckling strength of LCBs was included.

Ultimate limit states design of concrete railway sleepers

The railway industry has been slow to adopt limit states principles in the structural design of concrete sleepers for its tracks, despite the global take up of this form of design for almost every other type of structural element. Concrete sleeper design is still based on limiting stresses but is widely perceived by track engineers to lead to untapped reserves of strength in the sleepers. Limit design is a more rational philosophy, especially where it is based on the ultimate dynamic capacity of the concrete sleepers. The paper describes the development of equations and factors for a limit design methodology for concrete sleepers in flexure using a probabilistic evaluation of sleeper loading. The new method will also permit a cogent, defensible means of establishing the true capacity of the billions of concrete sleepers that are currently in-track around the world, leading to better utilisation of track infrastructure. The paper demonstrates how significant cost savings may be achieved by track owners.