How the energy sector could get it wrong with cloud computing

Cloud computing has significantly impacted a broad range of industries, but these technologies and services have been absorbed throughout the marketplace unevenly. Some industries have moved aggressively towards cloud computing, while others have moved much more slowly. For the most part, the energy sector has approached cloud computing in a measured and cautious way, with progress often in the form of private cloud solutions rather than public ones, or hybridized information technology systems that combine cloud and existing non-cloud architectures. By moving towards cloud computing in a very slow and tentative way, however, the energy industry may prevent itself from reaping the full benefit that a more complete migration to the public cloud has brought about in several other industries. This short communication is accordingly intended to offer a high-level overview of cloud computing, and to put forward the argument that the energy sector should make a more complete migration to the public cloud in order to unlock the major system-wide efficiencies that cloud computing can provide. Also, assets within the energy sector should be designed with as much modularity and flexibility as possible so that they are not locked out of cloud-friendly options in the future.

GPU accelerated algorithms for computing matrix function vector products with applications to exponential integrators and fractional diffusion

The efficient computation of matrix function vector products has become an important area of research in recent times, driven in particular by two important applications: the numerical solution of fractional partial differential equations and the integration of large systems of ordinary differential equations. In this work we consider a problem that combines these two applications, in the form of a numerical solution algorithm for fractional reaction diffusion equations that after spatial discretisation, is advanced in time using the exponential Euler method. We focus on the efficient implementation of the algorithm on Graphics Processing Units (GPU), as we wish to make use of the increased computational power available with this hardware. We compute the matrix function vector products using the contour integration method in [N. Hale, N. Higham, and L. Trefethen. Computing Aα, log(A), and related matrix functions by contour integrals. SIAM J. Numer. Anal., 46(5):2505–2523, 2008]. Multiple levels of preconditioning are applied to reduce the GPU memory footprint and to further accelerate convergence. We also derive an error bound for the convergence of the contour integral method that allows us to pre-determine the appropriate number of quadrature points. Results are presented that demonstrate the effectiveness of the method for large two-dimensional problems, showing a speedup of more than an order of magnitude compared to a CPU-only implementation.

Contributing to computing services to benchmarking asset management knowledge management

Asset management has broadened from a focus on maintenance management to whole of life cycle asset management requiring a suite of new competencies from asset procurement to management and disposal. Well developed skills and competencies as well as practical experience are a prerequisite to maintain capability, to manage demand as well to plan and set priorities and ensure on-going asset sustainability. This paper has as its focus to establish critical understandings of data, information and knowledge for asset management along with the way in which benchmarking these attributes through computer-aided design may aid a strategic approach to asset management. The paper provides suggestions to improve sharing, integration and creation of asset-related knowledge through the application of codification and personalization approaches.

The fight for fair use in Australia: Copyright law in an age of cloud computing

Fair Use Week has celebrated the evolution and development of the defence of fair use under copyright law in the United States. As Krista Cox noted, ‘As a flexible doctrine, fair use can adapt to evolving technologies and new situations that may arise, and its long history demonstrates its importance in promoting access to information, future innovation, and creativity.’ While the defence of fair use has flourished in the United States, the adoption of the defence of fair use in other jurisdictions has often been stymied. Professor Peter Jaszi has reflected: ‘We can only wonder (with some bemusement) why some of our most important foreign competitors, like the European Union, haven’t figured out that fair use is, to a great extent, the “secret sauce” of U.S. cultural competitiveness.’ Jurisdictions such as Australia have been at a dismal disadvantage, because they lack the freedoms and flexibilities of the defence of fair use.

Distributed computing of all-to-all comparison problems in heterogeneous systems

The requirement of distributed computing of all-to-all comparison (ATAC) problems in heterogeneous systems is increasingly important in various domains. Though Hadoop-based solutions are widely used, they are inefficient for the ATAC pattern, which is fundamentally different from the MapReduce pattern for which Hadoop is designed. They exhibit poor data locality and unbalanced allocation of comparison tasks, particularly in heterogeneous systems. The results in massive data movement at runtime and ineffective utilization of computing resources, affecting the overall computing performance significantly. To address these problems, a scalable and efficient data and task distribution strategy is presented in this paper for processing large-scale ATAC problems in heterogeneous systems. It not only saves storage space but also achieves load balancing and good data locality for all comparison tasks. Experiments of bioinformatics examples show that about 89\% of the ideal performance capacity of the multiple machines have be achieved through using the approach presented in this paper.

The role of institutional pressures and top management support in the intention to adopt cloud computing solutions

Purpose – While many studies have predominantly looked at the benefits and risks of cloud computing, little is known whether and to what extent institutional forces play a role in cloud computing adoption. The purpose of this paper is to explore the role of institutional factors in top management team’s (TMT’s) decision to adopt cloud computing services. Design/methodology/approach – A model is developed and tested with data from an Australian survey using the partial least squares modeling technique. Findings – The results suggest that mimetic and coercive pressures influence TMT’s beliefs in the benefits of cloud computing. The results also show that TMT’s beliefs drive TMT’s participation, which in turn affects the intention to increase the adoption of cloud computing solutions. Research limitations/implications – Future studies could incorporate the influences of local actors who might also press for innovation. Practical implications – Given the influence of institutional forces and the plethora of cloud-based solutions on the market, it is recommended that TMTs exercise a high degree of caution when deciding for the types of applications to be outsourced as organizational requirements in terms of performance and security will differ. Originality/value – The paper contributes to the growing empirical literature on cloud computing adoption and offers the institutional framework as an alternative lens with which to interpret cloud-based information technology outsourcing.

External auditors’ perceptions of cloud computing adoption in Australia

Adopting a multi-theoretical approach, I examine external auditors’ perceptions of the reasons why organizations do or do not adopt cloud computing. I interview forensic accountants and IT experts about the adoption, acceptance, institutional motives, and risks of cloud computing. Although the medium to large accounting firms where the external auditors worked almost exclusively used private clouds, both private and public cloud services were gaining a foothold among many of their clients. Despite the advantages of cloud computing, data confidentiality and the involvement of foreign jurisdictions remain a concern, particularly if the data are moved outside Australia. Additionally, some organizations seem to understand neither the technology itself nor their own requirements, which may lead to poorly negotiated contracts and service agreements. To minimize the risks associated with cloud computing, many organizations turn to hybrid solutions or private clouds that include national or dedicated data centers. To the best of my knowledge, this is the first empirical study that reports on cloud computing adoption from the perspectives of external auditors.

Implementation of parallel tridiagonal solvers for a heterogeneous computing environment

Tridiagonal diagonally dominant linear systems arise in many scientific and engineering applications. The standard Thomas algorithm for solving such systems is inherently serial forming a bottleneck in computation. Algorithms such as cyclic reduction and SPIKE reduce a single large tridiagonal system into multiple small independent systems which can be solved in parallel. We have developed portable cyclic reduction and SPIKE algorithm OpenCL implementations with the intent to target a range of co-processors in a heterogeneous computing environment including Field Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and other multi-core processors. In this paper, we evaluate these designs in the context of solver performance, resource efficiency and numerical accuracy.

Data distribution and task scheduling for distributed computing of all-to-all comparison problems

This research studied distributed computing of all-to-all comparison problems with big data sets. The thesis formalised the problem, and developed a high-performance and scalable computing framework with a programming model, data distribution strategies and task scheduling policies to solve the problem. The study considered storage usage, data locality and load balancing for performance improvement in solving the problem. The research outcomes can be applied in bioinformatics, biometrics and data mining and other domains in which all-to-all comparisons are a typical computing pattern.

On reconsideration of IT governance structures to govern cloud computing services

There is an increase in the uptake of cloud computing services (CCS). CCS is adopted in the form of a utility, and it incorporates business risks of the service providers and intermediaries. Thus, the adoption of CCS will change the risk profile of an organization. In this situation, organisations need to develop competencies by reconsidering their IT governance structures to achieve a desired level of IT-business alignment and maintain their risk appetite to source business value from CCS. We use the resource-based theories to suggest that collaborative board oversight of CCS, competencies relating to CCS information and financial management, and a CCS-related continuous audit program can contribute to business process performance improvements and overall firm performance. Using survey data, we find evidence of a positive association between these IT governance considerations and business process performance. We also find evidence of positive association between business process performance improvements and overall firm performance. The results suggest that the suggested considerations on IT governance structures can contribute to CCS-related IT-business alignment and lead to anticipated business value from CCS. This study provides guidance to organizations on competencies required to secure business value from CCS.

Cloud computing services for the small and medium enterprises: Key considerations for sourcing sustainable business value

The concept of cloud computing services (CCS) is appealing to small and medium enterprises (SMEs). However, while there is a significant push by various authorities on SMEs to adopt the CCS, knowledge of the key considerations to adopt the CCS is very limited. We use the technology-organization-environment (TOE) framework to suggest that a strategic and incremental intent, understanding the organizational structure and culture, understanding the external factors, and consideration of the human resource capacity can contribute to sustainable business value from CCS. Using survey data, we find evidence of a positive association between these considerations and the CCS-related business objectives. We also find evidence of positive association between the CCS-related business objectives and CCS-related financial objectives. The results suggest that the proposed considerations can ensure sustainable business value from the CCS. This study provides guidance to SMEs on a path to adopting the CCS with the intention of a long-term commitment and achieving sustainable business value from these services.

Governing cloud computing services: Reconsideration of IT governance structures

There is an increase in the uptake of cloud computing services (CCS). CCS is adopted in the form of a utility, and it incorporates business risks of the service providers and intermediaries. Thus, the adoption of CCS will change the risk profile of an organization. In this situation, organizations need to develop competencies by reconsidering their IT governance structures to achieve a desired level of IT-business alignment and maintain their risk appetite to source business value from CCS. We use the resource-based theories to suggest that collaborative board oversight of CCS, competencies relating to CCS information and financial management, and a CCS-related continuous audit program can contribute to business process performance improvements and overall firm performance. Using survey data, we find evidence of a positive association between these IT governance considerations and business process performance. We also find evidence of positive association between business process performance improvements and overall firm performance. The results suggest that the suggested considerations on IT governance structures can contribute to CCS-related IT-business alignment and lead to anticipated business value from CCS. This study provides guidance to organizations on competencies required to secure business value from CCS.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.