Jörn Altmann

Identifying the Effects of Co-Authorship Networks on the Performance of Scholars: A Correlation and Regression Analysis of Performance Measures and Social Network Analysis Measures

In this study, we develop a theoretical model based on social network theories and analytical methods for exploring collaboration (co-authorship) networks of scholars. We use measures from social network analysis (SNA) (i.e., normalized degree centrality, normalized closeness centrality, normalized betweenness centrality, normalized eigenvector centrality, average ties strength, and efficiency) for examining the effect of social networks on the (citation-based) performance of scholars in a given discipline (i.e., information systems). Results from our statistical analysis using a Poisson regression model suggest that research performance of scholars (g-index) is positively correlated with four SNA measures except for the normalized betweenness centrality and the normalized closeness centrality measures. Furthermore, it reveals that only normalized degree centrality, efficiency, and average ties strength have a positive significant influence on the g-index (as a performance measure). The normalized eigenvector centrality has a negative significant influence on the g-index. Based on these results, we can imply that scholars, who are connected to many distinct scholars, have a better citation-based performance (g-index) than scholars with fewer connections. Additionally, scholars with large average ties strengths (i.e., repeated co-authorships) show a better research performance than those with low tie strengths (e.g., single co-authorships with many different scholars). The results related to efficiency show that scholars, who maintain a strong co-authorship relationship to only one co-author of a group of linked co-authors, perform better than those researchers with many relationships to the same group of linked co-authors. The negative effect of the normalized eigenvector suggests that scholars should work with many students instead of other well-performing scholars. Consequently, we can state that the professional social network of researchers can be used to predict the future performance of researchers.

On the Correlation between Research Performance and Social Network Analysis Measures Applied to Research Collaboration Networks

In this study, we develop a theoretical model based on social network theory to understand how the collaboration (co-authorship) network of scholars correlates to the research performance of scholars. For this analysis, we use social network analysis (SNA) measures (i.e., normalized closeness centrality, normalized betweenness centrality, efficiency, and two types of degree centrality). The analysis of data shows that the research performance of scholars is positively correlated with two SNA measures (i.e., weighted degree centrality and efficiency). In particular, scholars with strong ties (i.e., repeated co-authorships, i.e., high weighted degree centrality) show a better research performance than those with low ties (e.g., single co-authorships with many different scholars). The results related to efficiency show that scholars, who maintain a strong co-authorship relationship to only one co-author of a group of linked co-authors (i.e., co-authors that have joined publications), perform better than those researchers with many relationships to the same group of linked co-authors.

Economics of Grids, Clouds, Systems, and Services

The paper focuses the attention to different business models and intended strategic aims of the firms providing Software-as-a-Service (SaaS). SaaS vendors have been said to challenge the business practices of the existing vendors providing proprietary or customer-specific solutions. The current studies on the topic have shown that SaaS is different from preceding software business models, but consider and emphasize SaaS business model as an invariable configuration. This case study compares two SaaS firms with different backgrounds and reveals characteristics of two very different SaaS business models. The findings indicate that along with SaaS vendors providing only standard software applications and focusing on cost efficiency, there are vendors who provide more specialized software applications and complement the SaaS offering with services required by larger customers.

GridEcon: A Market Place for Computing Resources

This paper discusses the rationales for a Grid market and, in particular, the introduction of a market place for trading commoditized computing resources. The market place proposed makes computing resources from different providers substitutable through virtualization. This includes the definition of a spot and future market as well as the parameters that a market mechanism for computing resources should consider. The above market place is complemented by a set of value-added services (e.g. insurance against resource failures, capacity planning, resource quality assurance, stable price offering) that ensure quality for Grid users over time. The market place technology for all of the above services has been designed by the GridEcon project, contributing to a broader adoption of Grid technology and enabling a service-oriented knowledge utility environment.

Cloud Computing Value Chains: Understanding Businesses and Value Creation in the Cloud

Based on the promising developments in Cloud Computing technologies in recent years, commercial computing resource services (e.g. Amazon EC2) or software-as-a-service offerings (e.g. Salesforce.com) came into existence. However, the relatively weak business exploitation, participation, and adoption of other Cloud Computing services remain the main challenges. The vague value structures seem to be hindering business adoption and the creation of sustainable business models around its technology. Using an extensive analyze of existing Cloud business models, Cloud services, stakeholder relations, market configurations and value structures, this Chapter develops a reference model for value chains in the Cloud. Although this model is theoretically based on porters value chain theory, the proposed Cloud value chain model is upgraded to fit the diversity of business service scenarios in the Cloud computing markets. Using this model, different service scenarios are explained. Our findings suggest new services, business opportunities, and policy practices for realizing more adoption and value creation paths in the Cloud.

Cost model based service placement in federated hybrid clouds

As cloud federation allows companies in need of computational resources to use computational resources hosted by different cloud providers, it reduces the cost of IT infrastructure by lowering capital and operational expenses. This is the result of economies of scale and the possibility for organizations to purchase just as much computing and storage resources as needed whenever needed. However, a clear specification of cost savings requires a detailed specification of the costs incurred. Although there are some efforts to define cost models for clouds, the need for a comprehensive cost model, which covers all cost factors and types of clouds, is undeniable. In this paper, we cover this gap by suggesting a cost model for the most general form of a cloud, namely federated hybrid clouds. This type of cloud is composed of a private cloud and a number of interoperable public clouds. The proposed cost model is applied within a cost minimization algorithm for making service placement decisions in clouds. We demonstrate the workings of our cost model and service placement algorithm within a specific cloud scenario. Our results show that the service placement algorithm with the cost model minimizes the spending for computational services. Comparison of cost models for cloud computing.Design of a comprehensive cost model for federated hybrid clouds.Design of an algorithm for service placements in federated hybrid clouds.Performed a sensitivity analysis with the help of the service placement algorithm.

Impact of pricing schemes on a market for Software-as-a-Service and perpetual software

In this paper, we present an agent-based simulation system that allows modeling the interactions between software buyers and vendors in a software market. The market offers Software-as-a-Service (SaaS) and perpetual software (PS) licenses under different pricing schemes. Four dynamic pricing schemes are analyzed: derivative-follower pricing, demand-driven pricing, skimming pricing, and penetration pricing. Customer (buyer) agents respond to these prices by selecting the most appropriate software license scheme based on four criteria using the Analytic Hierarchy Process (AHP) decision support mechanism. The four decision criteria relate to finance, software capability, organization, and vendor. The simulation results show that the demand-driven pricing scheme is the most effective method but hard to implement since it requires perfect knowledge about market conditions. As an alternative, penetration pricing and skimming pricing could be used. In addition to this, it can be stated that SaaS is most attractive for small enterprises while PS is attractive for large enterprises.

Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods

Due to the large variety in computing resources and, consequently, the large number of different types of service level agreements (SLAs), computing resource markets face the problem of a low market liquidity. Restricting the number of different resource types to a small set of standardized computing resources seems to be the appropriate solution to counteract this problem. Standardized computing resources are defined through an SLA template. An SLA template defines the structure of an SLA, the service attributes, the names of the service attributes, and the service attribute values. However, since existing research results have only introduced static SLA templates so far, the SLA templates cannot reflect changes in user needs and market structures. To address this shortcoming, we present a novel approach of adaptive SLA matching. This approach adapts SLA templates based on SLA mappings of users. It allows Cloud users to define mappings between a public SLA template, which is available in the Cloud market, and their private SLA templates, which are used for various in-house business processes of the Cloud user. Besides showing how public SLA templates are adapted to the demand of Cloud users, we also analyze the costs and benefits of this approach. Costs are incurred every time a user has to define a new SLA mapping to a public SLA template due to its adaptation. In particular, we investigate how the costs differ with respect to the public SLA template adaptation method. The simulation results show that the use of heuristics within adaptation methods allows balancing the costs and benefits of the SLA mapping approach.

A cost model for hybrid clouds

Cloud computing aims at allowing customers to utilize computational resources and software hosted by service providers. Thus, it shifts the complex and tedious resource and software management tasks typically done by customers to the service providers. Besides promising to eliminate these obstacles of resource management for consumers, Cloud computing also promises to reduce the cost of IT infrastructure. In particular, it promises to reduce the cost of IT through lower capital and operational expenses, stemming from a Clouds economies of scale and from allowing organizations to purchase just as much computer and storage resources as needed whenever needed. A clear specification of savings however requires a detailed specification of the costs incurred. Although there are some efforts to define cost models for Clouds, the need for a comprehensive cost model, which covers all cost factors, is undeniable. In this paper, we cover this gap by suggesting a cost model for hybrid Clouds (i.e., the combinations of a private data center (private Cloud) and the public Cloud). This model is based on a comprehensive literature research on cost factors and the idea of combining cost of data centers and cost for using Clouds. Finally, we demonstrate the workings of the suggested cost model by applying it to a specific Cloud scenario.

The GridEcon Platform: A Business Scenario Testbed for Commercial Cloud Services

Within this paper, we present the GridEcon Platform, a testbed for designing and evaluating economics-aware services in a commercial Cloud computing setting. The Platform is based on the idea that the exact working of such services is difficult to predict in the context of a market and, therefore, an environment for evaluating its behavior in an emulated market is needed. To identify the components of the GridEcon Platform, a number of economics-aware services and their interactions have been envisioned. The two most important components of the platform are the Marketplace and the Workflow Engine. The Workflow Engine allows the simple composition of a market environment by describing the service interactions between economics-aware services. The Marketplace allows trading goods using different market mechanisms. The capabilities of these components of the GridEcon Platform in conjunction with the economics-aware services are described in this paper in detail. The validation of an implemented market mechanism and a capacity planning service using the GridEcon Platform also demonstrated the usefulness of the GridEcon Platform.