A Quality Model for Evaluating Software-as-a-Service in Cloud Computing

Lee, Jae Yoo; Lee, Jung Woo; Cheun, Du Wan; Kim, Soo Dong

doi:10.1109/sera.2009.43

Cited by 99 publications

(88 citation statements)

References 7 publications

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“…Focusing on service characteristics, a list of essential and high-level features of cloud services were discussed in several studies (Alhamad et al, 2010;Repschlaeger et al, 2012;Tamburri and Lago, 2011). Other work proposed methods for evaluating quality of services (Garg et al, 2011;Lee et al, 2009), and cost modeling (Khajeh-Hosseini et al, 2012;Klems et al, 2009;Mach and Schikuta, 2011). Based on such requirements with the goal of reducing barrier-to-entry, concrete factors that could potentially inhibit cloud adoption need to be identified.…”

Section: Introductionmentioning

confidence: 99%

A survey study on major technical barriers affecting the decision to adopt cloud services

Phaphoom

Wang

Samuel

et al. 2015

Journal of Systems and Software

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In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption

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Section: Introductionmentioning

confidence: 99%

A survey study on major technical barriers affecting the decision to adopt cloud services

Phaphoom

Wang

Samuel

et al. 2015

Journal of Systems and Software

View full text Add to dashboard Cite

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“…As the study was angled from a product planning perspective, the obtained results confirm the importance of analytics for product management beyond product launch support [64] and product validation [14].…”

Section: Discussionmentioning

confidence: 52%

“…They provide measurements that are not affected by power and politics for guiding sales and marketing [12], for informing usability, reliability, and quality of service engineering [13,14], and to support quality assurance . Despite their importance, analytics have not been used yet for guiding product planning.…”

Section: Introductionmentioning

confidence: 99%

Analytics for Product Planning: In-Depth Interview Study with SaaS Product Managers

Fotrousi

Izadyan²,

Fricker

2013

2013 IEEE Sixth International Conference on Cloud Computing

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Abstract-SaaS cloud computing, in contrast to packaged products, enables permanent contact between users of a software product and the product-owning company. When planning the development and evolution of a software product, a product manager depends on reliable information about feature attractiveness. So far, planning decisions were based on stakeholder opinion and the customer's willingness to buy. Whether or not a feature actually is used was out of consideration.Analytics that measure the interaction between users and the SaaS gives product managers unprecedented access to information about product usage. To understand whether and how SaaS analytics can be used for product planning decision, we performed 17 in-depth interviews with experienced managers of SaaS products and analyzed the results analyzed with a mixed-method strategy. The empirical results characterize the relevance of a broad range of analytics for product planning decisions, and the strengths and limitations of an analytics-based product planning approach.

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“…Common metrics proposed for scalability evaluation assume linear scal-ing [5,11,9,19,22], yet many use arbitrary thresholds for qualifying a system to be scalable [20]. Our aim is to provide components to perform automated analysis in order to verify if system performance remains the same when resource allocation is changed because of variatons in workload [25].…”

Section: Proposalmentioning

confidence: 99%

Using Performance Forecasting to Accelerate Elasticity

Moura

Kon

Voulgaris³

et al. 2015

Adaptive Resource Management and Scheduling for Cloud Computing

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Abstract. Cloud computing facilitates dynamic resource provisioning. The automation of resource management, known as elasticity, has been subject to much research. Monitoring of a running service plays a crucial role, and adjustments are made when certain thresholds are crossed. On such occasions, it is common practice to simply add or remove resources. In this paper we ask ourselves how we can predict the performance of a service in order to dynamically adjust allocated resources based on predictions. In other words, instead of "repairing" because a threshold has been crossed, we attempt to stay ahead and allocate a best amount of resources in advance. To do so, we need to have accurate predictive models that are based on workloads. We present our approach, based on the Universal Scalability Law, and discuss initial experiments.

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A Quality Model for Evaluating Software-as-a-Service in Cloud Computing

Cited by 99 publications

References 7 publications

A survey study on major technical barriers affecting the decision to adopt cloud services

A survey study on major technical barriers affecting the decision to adopt cloud services

Analytics for Product Planning: In-Depth Interview Study with SaaS Product Managers

Using Performance Forecasting to Accelerate Elasticity

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