Capturing Cloud Computing Knowledge and Experience in Patterns

Fehling, Christoph; Ewald, Thilo; Leymann, Frank; Pauly, Michael; Rütschlin, Jochen; Schumm, David

doi:10.1109/cloud.2012.124

Cited by 22 publications

(8 citation statements)

References 14 publications

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“…Examples: in the domain of IT architecture, the concepts of a "server", "node", "component", and others have been used in solution descriptions. In order to homogenize them during the information collection and later pattern description, their graphical representations has been homogenized in [29] and provided as an authoring toolkit [4], which is also available online 1 . This authoring toolkit also includes Word and Excel template to homogenize the collection of information sources and later pattern writing.…”

Section: Information Format Designmentioning

confidence: 99%

“…Cloud Computing Patterns [4] [5] [6]: in collaboration with several industry partners, we reviewed existing applications that had been developed for cloud environments and those that should be migrated to it. The resulting cloud computing pattern language contains patterns to characterize the cloud environment by describing different deployment options, service models, and offerings provided by clouds.…”

Section: Introductionmentioning

confidence: 99%

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A process for pattern identification, authoring, and application

Fehling

Barzen

Breitenbücher

et al. 2014

Proceedings of the 19th European Conference on Pattern Languages of Programs

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The process to identify, author, and apply patterns is mostly performed manually by pattern experts. When performing pattern research in large domains involving many persons, the current state of the art of pattern research techniques, such as shepherding and writers' workshops, should be extended to a larger organizational process coordinating the work of all involved participants. This paper presents the process we followed to identify, author, and apply patterns in various domains involving multiple industry partners. Due to the diversity of these domains, we claim that the process is general enough to be applicable in other domains as well. This paper documents this process for use, discussion, further refinement, and evaluation in a larger pattern research community.

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Section: Information Format Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A process for pattern identification, authoring, and application

Fehling

Barzen

Breitenbücher

et al. 2014

Proceedings of the 19th European Conference on Pattern Languages of Programs

Self Cite

View full text Add to dashboard Cite

show abstract

“…That is, cloud service provider i hold a full resource pooling pattern conjecture hierarchy about the cloud consumers' choices and the cloud consumers' conjecture. If we assume, moreover, that each of the conjecture in this hierarchy can be represented by a TODIS, this leads to the following elastic scaling planning model [7], [13], [15], [16], [25].…”

Section: Elastic Scaling Planning Modelmentioning

confidence: 99%

Given a Static Workload Cloud Computing Patterns does it have an Elastic Scaling?

Ravi¹,

Kiran²

2016

CAE

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Measurability is a concept in elastic scaling based on the following two conditions: (a) a cloud service provider should be cautious, that is, should not exclude any cloud consumer's resource pooling pattern strategy from consideration; and (b) a cloud service provider should consider the cloud consumers' resource pooling pattern preferences, that is, should deem a cloud consumer's resource pooling pattern strategy k i infinitely more likely than k' i if it premises the cloud consumer to prefer k i to k' i . A resource pooling pattern strategy is measurable if it can optimally be chosen under common resource pooling pattern conjecture in the events (a) and (b). In this paper we present an algorithm that for every finite elastic scaling operation computes the set of all measurable resource pooling pattern strategies. The algorithm is based on the new idea of a Static Workload preference limitation, which is a pair (k i , V i ) consisting of a resource pooling pattern strategy k i , and a subset of resource pooling pattern strategies V i , for cloud service provider i. The interpretation is that cloud service provider i prefers some resource pooling pattern strategy in V i to k i . The algorithm proceeds by successively adding Static Workload preference limitations to the elastic scaling.

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“…That is, cloud service provider i hold a full resource pooling pattern conjecture hierarchy about the cloud consumers' choices and the cloud consumers' conjecture. If we assume, moreover, that each of the conjecture in this hierarchy can be represented by a TODIS, this leads to the following periodic static scaling planning model [7], [13], [15], [16], [25].…”

Section: Periodic Static Scaling Planning Modelmentioning

confidence: 99%

On Characterized Once-in-a-Lifetime Workload patterns for Periodic Static Scaling

Ravi¹,

Kiran²

2016

IJAIS

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Measurability is a concept in periodic static scaling based on the following two conditions: (a) a cloud service provider should be cautious, that is, should not exclude any cloud consumer's resource pooling pattern strategy from consideration; and (b) a cloud service provider should consider the cloud consumers' resource pooling pattern preferences, that is, should deem a cloud consumer's resource pooling pattern strategy k i infinitely more likely than k' i if it premises the cloud consumer to prefer k i to k' i . A resource pooling pattern strategy is measurable if it can optimally be chosen under common resource pooling pattern conjecture in the events (a) and (b). In this paper we present an algorithm that for every finite periodic static scaling operation computes the set of all measurable resource pooling pattern strategies. The algorithm is based on the new idea of an Once-in-aLifetime Workload preference limitation, which is a pair (k i , V i ) consisting of a resource pooling pattern strategy k i , and a subset of resource pooling pattern strategies V i , for cloud service provider i. The interpretation is that cloud service provider i prefers some resource pooling pattern strategy in V i to k i . The algorithm proceeds by successively adding Once-ina-Lifetime Workload preference limitations to the periodic static scaling.

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Capturing Cloud Computing Knowledge and Experience in Patterns

Cited by 22 publications

References 14 publications

A process for pattern identification, authoring, and application

A process for pattern identification, authoring, and application

Given a Static Workload Cloud Computing Patterns does it have an Elastic Scaling?

On Characterized Once-in-a-Lifetime Workload patterns for Periodic Static Scaling

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