Cloud Benchmarking For Maximising Performance of Scientific Applications

How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings - one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.

The role of Lean at the interface with between operations management and applied services within a large aerospace organisation: a boundary spanning perspective

Increased complexity in large design and manufacturing organisations requires improvements at the operations management (OM)–applied service (AS) interface areas to improve project effectiveness. The aim of this paper is explore the role of Lean in improving the longitudinal efficiency of the OM–AS interface within a large aerospace organisation using Lean principles and boundary spanning theory. The methodology was an exploratory longitudinal case approach including exploratory interviews (n = 21), focus groups (n = 2), facilitated action-research workshops (n = 2) and two trials or experiments using longitudinal data involving both OM and AS personnel working at the interface. The findings draw upon Lean principles and boundary spanning theory to guide and interpret the findings. It was found that misinterpretation, and forced implementation, of OM-based Lean terminology and practice in the OM–AS interface space led to delays and misplaced resources. Rather both OM and AS staff were challenged to develop a cross boundary understanding of Lean-based boundary (knowledge) objects in interpreting OM requests. The longitudinal findings from the experiments showed that the development of Lean Performance measurements and lean Value Stream constructs was more successful when these Lean constructs were treated as boundary (knowledge) objects requiring transformation over time to orchestrate improved effectiveness and in leading to consistent terminology and understanding between the OM–AS boundary spanning team.