The Experts below are selected from a list of 13629 Experts worldwide ranked by ideXlab platform
Ian Sommerville - One of the best experts on this subject based on the ideXlab platform.
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The Cloud Adoption Toolkit: supporting Cloud Adoption decisions in the enterprise
Software: Practice and Experience, 2011Co-Authors: Ali Khajeh-hosseini, David Greenwood, James W. Smith, Ian SommervilleAbstract:Cloud computing promises a radical shift in the provisioning of computing resources within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the Adoption of Cloud computing in their organizations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the Cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the Cloud. The case study shows that running systems on the Cloud using a traditional ‘always on’ approach can be less cost effective, and the elastic nature of the Cloud has to be used to reduce costs. Therefore, decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Copyright © 2011 John Wiley & Sons, Ltd.
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The Cloud Adoption Toolkit: Supporting Cloud Adoption Decisions in the Enterprise
arXiv: Distributed Parallel and Cluster Computing, 2010Co-Authors: Ali Khajeh-hosseini, David Greenwood, James W. Smith, Ian SommervilleAbstract:Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the Adoption of Cloud computing in their organisations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the Cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the Cloud. The case study shows that running systems on the Cloud using a traditional "always on" approach can be less cost effective, and the elastic nature of the Cloud has to be used to reduce costs. Therefore, decision makers have to be able to model the variations in resource usage and their systems deployment options to obtain accurate cost estimates.
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The Cloud Adoption Toolkit: Addressing the Challenges of Cloud Adoption in Enterprise
arXiv: Distributed Parallel and Cluster Computing, 2010Co-Authors: David Greenwood, Ali Khajeh-hosseini, James W. Smith, Ian SommervilleAbstract:Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper: i) describes the challenges that decision makers face when attempting to determine the feasibility of the Adoption of Cloud computing in their organisations; ii) illustrates a lack of existing work to address the feasibility challenges of Cloud Adoption in the enterprise; iii) introduces the Cloud Adoption Toolkit that provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. The paper adopts a position paper methodology such that case study evidence is provided, where available, to support claims. We conclude that the Cloud Adoption Toolkit, whilst still under development, shows signs that it is a useful tool for decision makers as it helps address the feasibility challenges of Cloud Adoption in the enterprise.
Andreas S. Andreou - One of the best experts on this subject based on the ideXlab platform.
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Investigating Cloud Adoption Using Influence Diagrams as a Decision Support Model
International Journal on Artificial Intelligence Tools, 2015Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:The fact that Cloud Computing is steadily becoming one of the most significant fields of Information and Communication Technology (ICT) has led many organizations to consider the benefits of migrating their business operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the Adoption of Cloud Computing for their organizations. Cloud Adoption is a multi-level decision which is influenced by a number of intertwined factors and concerns thus characterizing it as a complex and difficult to model real-world problem. In this paper we propose two decision support modeling approaches based on Influence Diagrams (ID) aiming to model the answer to the question “Adopt Cloud Services or Not?” Two models are developed and tested, the first is a generic ID with nodes interacting in a probabilistic manner, while the second is a more flexible version that utilizes Fuzzy Logic. Both models combine several factors that influence the decision to be taken, which were identified through literature review and input received from field experts. The proposed approaches are validated using five experimental scenarios, two synthetic and three real-world cases, and their performance suggests that they are highly capable of supporting the right decision.
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FUZZ-IEEE - A Multilayer Fuzzy Cognitive Maps approach to the Cloud Adoption decision support problem
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:As modern computing relies more and more on distributed solutions of services and resources over the Cloud, the need of potential users to assess whether the transition from traditional software systems to the Cloud would be to their benefit becomes even greater. Cloud vendors also seek ways to study beforehand the behavior of potential users with respect to their decision to adopt the Cloud environment so as to take actions towards enhancing the positive side. Therefore, the study of the parameters forming the environment behind the Cloud Adoption decision is of paramount importance to both users and vendors. In this context the present paper proposes a multi-layer FCM approach which models a number of factors which play a decisive role to the Cloud Adoption issue and offers the means to study their influence. The factors are organized in different layers which focus on specific aspects of the Cloud environment, something which, on one hand, enables tracking the causes for the decision outcome, and on the other offers the ability to study the dependencies between the leading determinants of the decision. The construction and analysis of the model is based on factors reported in the relevant literature and the utilization of experts' opinion. The efficacy and applicability of the proposed approach are demonstrated through four real-world experimental cases.
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AIAI - A Cloud Adoption Decision Support Model Using Influence Diagrams
IFIP Advances in Information and Communication Technology, 2013Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:Cloud Computing has become nowadays a significant field of Information and Communication Technology (ICT), and this has led many organizations moving their computing operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the Adoption of Cloud Computing for their organizations. The decision to adopt Cloud services falls within the category of complex and difficult to model real-world problems. In this paper we propose an approach based on Influence Diagrams modeling, aiming to support the Cloud Adoption decision process. The developed ID model combines a number of factors which were identified through litterature review and input received from field experts. The proposed approach is validated against four experimental cases, two realistic and two real-world, and its performance proved to be highly capable of estimating and predicting correctly the right decision.
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PROFES - A Cloud Adoption Decision Support Model Based on Fuzzy Cognitive Maps
Product-Focused Software Process Improvement, 2013Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:Cloud Computing has become nowadays a significant field of Information and Communication Technology (ICT). Both Cloud providers and customers invest time and resources in an endeavor of the former to serve effectively the needs of the latter so as to adopt efficiently such Cloud services, based their needs. The decision to adopt Cloud services falls within the category of complex and difficult to model real-world problems. Aiming to support the Cloud Adoption decision process, we propose in this paper an approach based on Fuzzy Cognitive Maps (FCM) which models the parameters that potentially influence such a decision. The construction and analysis of the map is based on factors reported in the relevant literature and the utilization of experts’ opinion. The proposed approach is evaluated through four real-world experimental cases and the suggestions of the model are compared with the customers’ final decisions. The evaluation indicated that the proposed approach is capable of capturing the dynamics behind the interdependencies of the participating factors.
Laurence Hirsch - One of the best experts on this subject based on the ideXlab platform.
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Cloud Adoption decision support for SMEs using Analytical Hierarchy Process (AHP)
2016 IEEE 4th Workshop on Advances in Information Electronic and Electrical Engineering (AIEEE), 2016Co-Authors: Berlin Mano Robert Wilson, Babak Khazaei, Laurence HirschAbstract:For a successful Cloud Adoption, decision makers need to consider numerous aspects before deciding to adopt Cloud infrastructure. In this paper, we propose a framework to support Cloud Adoption decisions for SMEs in Tamil Nadu (India) using the established principles of Analytical Hierarchy Process (AHP). This study is focused on SMEs in Tamil Nadu, one of the constituent states of the Indian Union. This paper reports on the findings of applying AHP to real data collected from decision makers and demonstrates its usefulness as a decision support tool for SMEs in Tamil Nadu.
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Enablers and Barriers of Cloud Adoption among Small and Medium Enterprises in Tamil Nadu
2015 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2015Co-Authors: Berlin Mano Robert Wilson, Babak Khazaei, Laurence HirschAbstract:Cloud computing has the potential to speed up IT Adoption among SMEs in developing economies. Though the benefits of Cloud computing is very appealing, the level of Cloud Adoption is still low among SMEs. This research aims to identify the key enablers, barriers and other factors that influence Cloud Adoption among SMEs in Tamil Nadu by conducting empirical investigations. We have used TOE framework to identify and capture the factors that affect technology Adoption. We highlight cost benefits of using Cloud infrastructure, scalability and agility of Cloud services as the key enablers of Cloud Adoption. Broadband availability, high bandwidth cost and vendor lock-in are the main barrier for Cloud Adoption. Compatibility to existing system, complexity of the migration process, top management support, government policies and competitor pressure are the major organizational factors affecting Cloud Adoption among SMEs in Tamil Nadu. This study is part of a larger study which aims to develop a Cloud migration decision support system (CMDSS) for SMEs in Tamil Nadu.
Andreas Christoforou - One of the best experts on this subject based on the ideXlab platform.
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Investigating Cloud Adoption Using Influence Diagrams as a Decision Support Model
International Journal on Artificial Intelligence Tools, 2015Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:The fact that Cloud Computing is steadily becoming one of the most significant fields of Information and Communication Technology (ICT) has led many organizations to consider the benefits of migrating their business operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the Adoption of Cloud Computing for their organizations. Cloud Adoption is a multi-level decision which is influenced by a number of intertwined factors and concerns thus characterizing it as a complex and difficult to model real-world problem. In this paper we propose two decision support modeling approaches based on Influence Diagrams (ID) aiming to model the answer to the question “Adopt Cloud Services or Not?” Two models are developed and tested, the first is a generic ID with nodes interacting in a probabilistic manner, while the second is a more flexible version that utilizes Fuzzy Logic. Both models combine several factors that influence the decision to be taken, which were identified through literature review and input received from field experts. The proposed approaches are validated using five experimental scenarios, two synthetic and three real-world cases, and their performance suggests that they are highly capable of supporting the right decision.
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FUZZ-IEEE - A Multilayer Fuzzy Cognitive Maps approach to the Cloud Adoption decision support problem
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:As modern computing relies more and more on distributed solutions of services and resources over the Cloud, the need of potential users to assess whether the transition from traditional software systems to the Cloud would be to their benefit becomes even greater. Cloud vendors also seek ways to study beforehand the behavior of potential users with respect to their decision to adopt the Cloud environment so as to take actions towards enhancing the positive side. Therefore, the study of the parameters forming the environment behind the Cloud Adoption decision is of paramount importance to both users and vendors. In this context the present paper proposes a multi-layer FCM approach which models a number of factors which play a decisive role to the Cloud Adoption issue and offers the means to study their influence. The factors are organized in different layers which focus on specific aspects of the Cloud environment, something which, on one hand, enables tracking the causes for the decision outcome, and on the other offers the ability to study the dependencies between the leading determinants of the decision. The construction and analysis of the model is based on factors reported in the relevant literature and the utilization of experts' opinion. The efficacy and applicability of the proposed approach are demonstrated through four real-world experimental cases.
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A Cloud Adoption Decision Support Model Using Influence Diagrams
2013Co-Authors: Andreas Christoforou, Andreas AndreouAbstract:Cloud Computing has become nowadays a significant field of Information and Communication Technology (ICT), and this has led many organizations moving their computing operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the Adoption of Cloud Computing for their organizations. The decision to adopt Cloud services falls within the category of complex and difficult to model real-world problems. In this paper we propose an approach based on Influence Diagrams modeling, aiming to support the Cloud Adoption decision process. The developed ID model combines a number of factors which were identified through litterature review and input received from field experts. The proposed approach is validated against four experimental cases, two realistic and two real-world, and its performance proved to be highly capable of estimating and predicting correctly the right decision.
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AIAI - A Cloud Adoption Decision Support Model Using Influence Diagrams
IFIP Advances in Information and Communication Technology, 2013Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:Cloud Computing has become nowadays a significant field of Information and Communication Technology (ICT), and this has led many organizations moving their computing operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the Adoption of Cloud Computing for their organizations. The decision to adopt Cloud services falls within the category of complex and difficult to model real-world problems. In this paper we propose an approach based on Influence Diagrams modeling, aiming to support the Cloud Adoption decision process. The developed ID model combines a number of factors which were identified through litterature review and input received from field experts. The proposed approach is validated against four experimental cases, two realistic and two real-world, and its performance proved to be highly capable of estimating and predicting correctly the right decision.
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PROFES - A Cloud Adoption Decision Support Model Based on Fuzzy Cognitive Maps
Product-Focused Software Process Improvement, 2013Co-Authors: Andreas Christoforou, Andreas S. AndreouAbstract:Cloud Computing has become nowadays a significant field of Information and Communication Technology (ICT). Both Cloud providers and customers invest time and resources in an endeavor of the former to serve effectively the needs of the latter so as to adopt efficiently such Cloud services, based their needs. The decision to adopt Cloud services falls within the category of complex and difficult to model real-world problems. Aiming to support the Cloud Adoption decision process, we propose in this paper an approach based on Fuzzy Cognitive Maps (FCM) which models the parameters that potentially influence such a decision. The construction and analysis of the map is based on factors reported in the relevant literature and the utilization of experts’ opinion. The proposed approach is evaluated through four real-world experimental cases and the suggestions of the model are compared with the customers’ final decisions. The evaluation indicated that the proposed approach is capable of capturing the dynamics behind the interdependencies of the participating factors.
Gerhard Kristandl - One of the best experts on this subject based on the ideXlab platform.
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Cloud security, emotions and diffusion of innovation – a neoinstitutional-behavioural approach on decision-making on Cloud Adoption in SMBs
2017Co-Authors: Gerhard KristandlAbstract:Cloud computing is often claimed to become a major enabling and disruptive innovation for businesses. As business functions and data are being moved outside the traditional boundaries of companies (Blandford 2011), Cloud providers assert that this new way of computing can enable especially small and medium-sized businesses (SMBs) to access information technology that has hitherto been inaccessible to them. In the nascent Cloud industry, change agents such as Cloud providers and business partners aim to convince decision-makers in SMBs that storing and processing their data off their premises will level the playing field in terms of IT, data processing, and as such, decision-making. Considering its purported benefits, it comes as a surprise that studies about Cloud Adoption have found the diffusion of this new technology among SMBs to be relatively slow and cautious (Strauss et al 2015; Bean 2011). A main factor might be the perception that sensitive data and functions are not secure when moved to the Internet, and anybody with adequate technical skills could easily access it. Such a perception may be exacerbated by data leak scandals like the iCloud celebrity photo hack (BBC 2014) or Sony’s loss of sensitive customer data (The Telegraph 2014). Similar security breaches like these often lead to widespread negative media coverage, seemingly influencing decision-makers not to adopt the Cloud – security concerns about the Cloud typically feature on top of the main reasons for businesses not to adopt the Cloud. Grounded in New Institutional Sociology (NIS) and Rogers’ (1983) Diffusion of Innovation theory, this paper aims to explain this phenomenon by depicting Cloud technology as an innovation that - despite a strong supply-driven effort by Cloud providers – is adopted at a rate that is slower than expected by peers (e.g. Bitkom 2014, 2015). In this context, the Cloud industry is portrayed as nascent institutional field that has not yet reached a stable institutional equilibrium in terms of rules, norms and taken-for-granted practices like in more mature, established industries (Kshetri 2013; Scott 2008). It appears that due to this seeming absence of established rules and norms, decision-makers in SMBs become more impressionable by and vulnerable to widespread media coverage emphasising the negative aspects of moving data to the “Cloud”. By drawing on and combining Heikkila and Isett’s (2003) institutional decision-making model and Loewenstein et al.’s (2001) risk-as-feelings hypothesis, it becomes possible to illustrate this vulnerability of the decision process about Cloud Adoption to emotions of risk and dread, invoked by negative news about this technology. Through mimetic isomorphism (Scott 2008), SMBs then seek to legitimize their decision not to adopt the Cloud, thus linking the micro-perspective from SMBs to the macro-implications of the entire Cloud industry. Therefore, this paper follows up on Lounsbury’s (2008) criticism of NIS not taking micro-processes (such as decision-making) and resulting divergences in practice into account.
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Cloud security, emotions and management accounting – a neoinstitutional-behavioural approach on decision-making on Cloud Adoption in SMBs
2016Co-Authors: Gerhard KristandlAbstract:Cloud technology found its way into many businesses in the last five years or so. More and more business functions are being moved to the Cloud, and with it data is moved outside the traditional boundaries of companies (Blandford 2011). As such, Cloud technology has the ability to become a disruptive technology that can enable especially small and medium-sized businesses (SMBs) to access IT that has hitherto been too costly for them. Considering the purported benefits of Cloud technology and the involvement of the management accounting function, it comes as a surprise that studies about Cloud Adoption have found that in spite of the benefits, the deployment of this new technology is – if at all - relatively slow (Strauss et al 2015; Bean 2011). I hypothesise that this is mainly due to a perception that it is still deemed risky to move data to the internet, as it is assumed that anybody with adequate technical skills could fairly easily access it. Such a perception may be exacerbated by data leak scandals like the recent iCloud celebrity photo hack (BBC 2014). Major security breaches like this often lead to the negative media coverage, seemingly swaying decision-makers not to adopt the Cloud. This strong external influence that stalls technological change seems at odds with the responsibility of the management accounting function to provide a full and coherent decision-relevant information basis. Motivated by the results of a CIMA-study co-published by the author of this paper (Strauss et al 2015), the aim of this discussion is to develop explanations for this seemingly strong influence of an external negative perception on internal decision-making on Cloud technology. I aim to develop theoretical explanations for this alleged phenomenon drawing on neoinstitutional sociology and behavioural studies that aim to explain the impact of emotions on cognitive evaluation and decision-making processes. Based on these conceptual deliberations, this paper also attempts to highlight ways forward for the management accountant in order to overcome external influences such as negative public perception that might influence management decisions, as well as a way forward for policy makers in the medium-to-long run.