Project alliancing and information technology in building construction : the National Museum of Australia

The construction industry demands priority from all governments because it impacts economically and socially on all citizens. A number of recent studies have identified inefficiencies in the Australian construction industry by modelling the building process. A culture of reform supported by industry and government is now emerging in the industry – one in which alternate forms of project delivery are being trialed. The Australian Building and Construction Industry Action Agenda brought together industry and government to identify actions necessary to lift Australia’s innovative and knowledge creating capacity at the sector level. A central activity under this Action Agenda was dissemination of information relating to industry best practice initiatives in innovation, project delivery and the use of information technology. Government and industry identified project alliance contracting and more advanced information technology as means to increase efficiency in construction as part of a new innovative procurement environment.

Building and construction industries supply chain project (domestic) : report for Commonwealth Department of Industry, Science and Resources

The Commonwealth Department of Industry, Science and Resources is identifying best practice case study examples of supply chain management within the building and construction industry to illustrate the concepts, innovations and initiatives that are at work. The projects provide individual enterprises with examples of how to improve their performance, and the competitiveness of the industry as a whole.

Composite SaaS placement and resource optimization in Cloud computing using evolutionary algorithms

Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

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.