Non-functional data layer patterns for Cloud applications

Strauch, Steve; Andrikopoulos, Vasilios; Breitenbuecher, Uwe; Kopp, Oliver; Leyrnann, Frank

doi:10.1109/cloudcom.2012.6427478

Cited by 12 publications

(18 citation statements)

References 10 publications

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“…The migration of anonymized or pseudonymized data to the Cloud therefore does not effect the management of the identity of users of Cloud data hosting solutions, e.g., in large-scale Public Cloud environments. The interested reader is referred to (Strauch et al, 2012a;Strauch et al, 2012b) for an in-depth discussion on these patterns.…”

Section: Anonymizer Of Critical Datamentioning

confidence: 99%

“…For this purpose, in previous work (Strauch et al, 2012a;Strauch et al, 2012b) an initial list of reusable and technology independent solutions for the identified challenges was proposed in the form of Cloud Data Patterns. A Cloud Data Pattern describes a reusable and implementation technology-independent solution for a challenge related to the Data Layer of an application in the Cloud for a specific context (Strauch et al, 2012b).…”

Section: Cloud Data Patternsmentioning

confidence: 99%

“…Section 3 presents the existing work on migrating the application and application data and Section 4 organizes it into migration scenarios. Section 5 summarizes the Cloud Data Patterns that were identified in (Strauch et al, 2012b) and (Strauch et al, 2012a) and highlights their key points. Section 6 then discusses the mapping between scenarios and patterns, and the application data migration methodology that is based on this mapping.…”

Section: Introductionmentioning

confidence: 99%

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Migrating Application Data to the Cloud using Cloud Data Patterns

Strauch

Andrikopoulos

Bachmann

et al. 2013

Proceedings of the 3rd International Conference on Cloud Computing and Services Science

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Abstract:Taking advantage of the capabilities offered by Cloud computing requires either an application to be built specifically for it, or for existing applications to be migrated to it. In this work we focus on the latter case, and in particular on migrating the application data. Migrating data to the Cloud creates a series of technical, architectural and legal challenges that the State of the Art attempts to address. We organize such efforts into a set of migration scenarios and connect them with a list of reusable solutions for the application data migration in the form of patterns. From there we define an application data migration methodology and we demonstrate how it can be used in practice.

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Section: Anonymizer Of Critical Datamentioning

confidence: 99%

Section: Cloud Data Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Migrating Application Data to the Cloud using Cloud Data Patterns

Strauch

Andrikopoulos

Bachmann

et al. 2013

Proceedings of the 3rd International Conference on Cloud Computing and Services Science

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“…Local Database Proxy : The Local Database proxy pattern provides a read scalability on a relational database by using data replication between master/slave databases and a proxy to route requests [8]. All write requests are handled by the master and replicated on its slaves while read requests are processed by slaves.…”

Section: Related Workmentioning

confidence: 99%

“…Local Sharding-Based Router : The Local Sharding-Based Router is recommended when the need for scalability concerns read and write operations [8]. Data are split among multiple databases into functional groups called shards, requests are processed by a local router to determine the suitable databases.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study of the Impact of Cloud Patterns on Quality of Service (QoS)

Hecht

Jose-Scheidt

Figueiredo

et al. 2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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Abstract-Cloud patterns are described as good solutions to recurring design problems in a cloud context. These patterns are often inherited from Service Oriented Architectures or ObjectOriented Architectures where they are considered good practices. However, there is a lack of studies that assess the benefits of these patterns for cloud applications. In this paper, we conduct an empirical study on a RESTful application deployed in the cloud, to investigate the individual and the combined impact of three cloud patterns (i.e., Local Database proxy, Local ShardingBased Router and Priority Queue Patterns) on Quality of Service (QoS). We measure the QoS using the application's response time, average, and maximum number of requests processed per seconds. Results show that cloud patterns doesn't always improve the response time of an application. In the case of the Local Database proxy pattern, the choice of algorithm used to route requests has an impact on response time, as well as the average and maximum number of requests processed per second. Combinations of patterns can significantly affect the QoS of applications. Developers and software architects can make use of these results to guide their design decisions.

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CAP-Oriented Design for Cloud-Native Applications

Andrikopoulos

Strauch

Fehling

et al. 2013

Communications in Computer and Information Science

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Brewer's conjecture, and its resulting formalization as the CAP theorem, impose serious limitations on the consistency, availability and network partitioning tolerance characteristics of distributed systems. Despite its importance however, few works explicitly consider the implications of the CAP theorem in the design of applications, especially for applications that are designed natively for the Cloud. In order to address this need, in this work we propose a CAP-oriented design methodology for Cloud-native applications. For this purpose we build and extend our previous work on Cloud architectural patterns. Finally, we show how the methodology can be used in practice to design an application solution with desired CAP properties.

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Non-functional data layer patterns for Cloud applications

Cited by 12 publications

References 10 publications

Migrating Application Data to the Cloud using Cloud Data Patterns

Migrating Application Data to the Cloud using Cloud Data Patterns

An Empirical Study of the Impact of Cloud Patterns on Quality of Service (QoS)

CAP-Oriented Design for Cloud-Native Applications

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