A Decision Support Model for Cloud Bursting

The cloud computing market divides into public (commercial) and private (self-provisioned) clouds. The concept of cloud bursting combines public and private clouds: The private cloud (internal resources) provides the computational capacity, but a part of the demand is offloaded onto public clouds. This article proposes an easy-to-apply economic decision support model for determining on the one hand the optimal size of the internal capacity for cloud bursting technology, and on the other hand the cost savings. The model uses an expected value approach that considers stochastic workload and is flexible with respect to the distribution choice. Two empirical examples demonstrate the applicability of the model

On Oliver's p-group conjecture

Let S be a p-group for an odd prime p. Bob Oliver conjectures that a certain characteristic subgroup X(S) always contains the Thompson subgroup J(S). We obtain a reformulation of the conjecture as a statement about modular representations of p-groups. Using this we verify Oliver's conjecture for groups where S/X(S) has nilpotence class at most two.Comment: 9 pages; terminology altered to conform to current usag

Acceptance of a Web OS as a Commercial Consumer Service Bundle

The web is fundamentally changing. The many facets of this change are usually abstracted as Web 2.0. The core of Web 2.0 consists of the evolutionary step that interoperation and content-creating applications are provided via the web in addition to traditional static documents. Ahead of this evolution are web operating systems (WebOS) like g.ho.st that enable the consumer to migrate their complete operating system desktop to the web – a revolutionary step of personal computing. The required computational and storage resources may be procured on demand e.g. from cloud computing services by the WebOS service provider. This research-in-progress-article introduces an adoption model (TAM) for a WebOS as a consumer service. The subject of our survey will be a service bundle comparable to a mobile phone plan. The aim of the adoption model is to measure the acceptance of this service bundle and to identify the major determinants which influence the consumer’s adoption intention in order to specify which consumers may be future customers and to learn how to attract them from a marketing perspective. At the current early stage of adoption we intend to contribute insights that can be directly transformed into advice how this new technology can be successfully established

The Utility of TAM-Perceptions: Integration of Technology Perceptions into Choice-Based Conjoint Analysis

Recent papers claim the technology acceptance model [TAM] is exhaustively examined by researchers and, thus, additional studies of traditional style may only provide a marginal contribution. Instead of adding new constructs to the TAM to describe its dependent or independent variables better, we develop an approach to combine the well-established constructs of TAM, which measure perceptions of a new technology, and the choice-based conjoint analysis [CBC], which measures the monetary value of product attributes from a marketing perspective. In combining both methods we are able to compare the overall technology perceptions with particular attributes of product realisations with respect to their importance. We measure how TAM constructs influence the baseline utility of a new technology. We empirically apply and discuss our approach and show how the TAM can make a distinctive contribution to Information Systems and Marketing Research

Cataract in children and adolescents with type 1 diabetes. Insights from the German/Austrian DPV registry

Objective To study diabetic cataract in type 1 diabetes in a large pediatric cohort. Methods The 92,633 patients aged 0.5-21 years from German/Austrian multicenter diabetes registry (DPV) were analyzed. The 235 patients (0.25%) with diabetic cataract were found, 200 could be categorized: 67 with early cataract (3 months before diabetes onset - 12 months afterwards), 133 with late cataract (>12 months after diabetes onset). Regression models adjusted for age and gender were used to compare clinical parameters at diabetes onset. Regression models for patients with late cataract were implemented for the total documentation period and additionally adjusted for diabetes duration. Results Rate of cataract development shows a peak at diabetes onset and declines with longer diabetes duration. Patients with cataract showed strong female preponderance. Patients developing early cataract were older at diabetes onset (12.8 years [11.8/13.9] vs. 8.9 [8.9/9.0]; p < 0.001) and showed higher HbA1c than patients without cataract (9.0% [8.55/9.38] vs. 7.6% [7.60/7.61]; p < 0.001). They had lower height-SDS, (-0.22 [-0.48/0.04] vs. 0.25 [0.24/0.26]; p < 0.001), lower weight-SDS (-0.31 [-0.55/-0.08] vs. 0.21 [0.20/0.21]; p < 0.001) and lower BMI-SDS (-0.25 [-0.49/-0.02] vs. 0.12 [0.12/0.13); p = 0.002). Patients with late cataract showed higher HbA1c at diabetes onset (8.35% [8.08/8.62] vs. 8.04% [8.03/8.05]; p = 0.023) and higher mean HbA1c during total documentation period (8.00% [7.62/8.34] vs. 7.62% [7.61/7.63]; p = 0.048). Conclusions Our data confirm known demographic and clinical characteristics of patients developing early cataract. Hyperglycemia-induced osmotic damage to lens fibers at diabetes onset might be the main pathomechanism. Long term glycemic control is associated with cataract development

Methods and tools to evaluate the availability of renewable energy sources

The recent statements of both the European Union and the US Presidency pushed in the direction of using renewable forms of energy, in order to act against climate changes induced by the growing concentration of carbon dioxide in the atmosphere. In this paper, a survey regarding methods and tools presently available to determine potential and exploitable energy in the most important renewable sectors (i.e., solar, wind, wave, biomass and geothermal energy) is presented. Moreover, challenges for each renewable resource are highlighted as well as the available tools that can help in evaluating the use of a mix of different sources

THE PRICING OF GRID SERVICES IN ENTERPRISES: DERIVING PAY-PER-USE TARIFFS FROM PREFERENCE DATA

Grid computing has been identified as an instrument to fulfil high computational demand, a promising approach for higher resource utilization, and an instrument for cost reduction. The full potential of cost savings can be tapped when incentives are set such that demand is shifted to periods or hardware with lower demand, thereby flattening the demand. To set such incentives, it is mandatory to know the preferences of the Grid resource consumers. We propose the application of the ADBUDG-function to estimate the willingness-to-pay function of prospective resource consumers and K-means clustering to determine optimal tariffs based on this information. We demonstrate the application of this approach in the financial service industry at a large European bank that is planning to move from dedicated servers for single business units to an enterprise Grid. Based on a sample of 21 project leaders and business unit heads with their own budget responsibility, we show how optimal price tariffs can be determined using our proposed approach. Based on the self-reported preferences, we determine tariffs that allow an additional cost savings of 7% or an increase in utility by at least 6% and 9%, on average

How Prices can be set to allocate Grid Computing Resources in a Financial Service Institution

GRID COMPUTING IS AN IT CONCEPT TO SHARE COMPUTING RESOURCES AMONG DEPARTMENTS AND USERS THAT REDUCES IT COSTS AND PROVIDES COMPUTING RESOURCES DYNAMICALLY WHEN THEY ARE NEEDED. RESOURCE MARKETS ARE AN EFFECTIVE MECHANISM TO REGULATE THE RESOURCE SHARING, BUT THE MOST OFTEN USED AUCTIONS ARE COMPLEX. WE HAVE DEVELOPED A STEPWISE APPROACH TO HELP FIRMS OFFERING INTERNAL GRID COMPUTING SERVICES TO SET TRANSPARENT BUT EFFECTIVE PAY-PER-USE PRICING SCHEMES AS AN ALTERNATIVE TO AUCTIONS

How prices can be set to allocate grid computing resources in a financial service institution

GRID COMPUTING IS AN IT CONCEPT TO SHARE COMPUTING RESOURCES AMONG DEPARTMENTS AND USERS THAT REDUCES IT COSTS AND PROVIDES COMPUTING RESOURCES DYNAMICALLY WHEN THEY ARE NEEDED. RESOURCE MARKETS ARE AN EFFECTIVE MECHANISM TO REGULATE THE RESOURCE SHARING, BUT THE MOST OFTEN USED AUCTIONS ARE COMPLEX. WE HAVE DEVELOPED A STEPWISE APPROACH TO HELP FIRMS OFFERING INTERNAL GRID COMPUTING SERVICES TO SET TRANSPARENT BUT EFFECTIVE PAY-PER-USE PRICING SCHEMES AS AN ALTERNATIVE TO AUCTIONS