Scalable Architectures for Platform-as-a-Service Clouds: Performance and Cost Analysis

Xiong, Huanhuan; Fowley, Frank; Pahl, Claus; Moran, Niall

doi:10.1007/978-3-319-09970-5_21

Cited by 9 publications

(16 citation statements)

References 16 publications

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“…Essentially, the cost function can be used to generate a ranked list of candidate patterns for a required QoS value in terms of the operational cost. In [30], we have demonstrated that different performance result, but also costs vary for a given configuration pattern.  Cloud solutions are subject to (dynamic) configurations, generally both at IaaS and PaaS level.…”

Section: Cloud Service Sla Vm Configuration and Auto-scalingmentioning

confidence: 90%

“…While our configuration here is geared towards typical IaaS attributes, our implementation work with Microsoft Azure (see Section VI) also demonstrates the possibility and benefit of PaaS-level configuration. In [30], we have discussed different PaaSlevel storage configurations and their cost and performance implications.…”

Section: Cloud Service Sla Vm Configuration and Auto-scalingmentioning

confidence: 99%

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Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

Zhang

Jamshidi

et al. 2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

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Abstract-Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.

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Section: Cloud Service Sla Vm Configuration and Auto-scalingmentioning

confidence: 90%

Section: Cloud Service Sla Vm Configuration and Auto-scalingmentioning

confidence: 99%

Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

Zhang

Jamshidi

et al. 2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

Self Cite

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“…In addition to implementing migrations of existing IT systems, we did evaluations of possible architectural configurations in the migration process . The migration patterns allow different architectural configurations to be proposed as different migration paths.…”

Section: Discussionmentioning

confidence: 99%

“…In 1 study, the architectural options in the modernisation (evolution) process are analysed. As part of a migration project, often several architectural options arise.…”

Section: A Cloud Architecture Modelmentioning

confidence: 99%

“…The migration patterns define common architecture transformation paths, driven by functional and nonfunctional concerns as determined in the variability model. The aim is to also combine architecture transformation with performance and costs analysis as annotations in Figure like time‐to‐market. These patterns are similar to design patterns in their presentation involving a structural architecture part, combined with a description of the pattern purpose, the problem description, the key idea of the solution, and expected benefits as well as challenges, Figure .…”

Section: Evolution—static Architecturementioning

confidence: 99%

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Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures

Pahl

Jamshidi

Weyns

2017

J Software Evolu Process

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SUMMARYCloud systems provide elastic execution environments of resources that link application and infrastructure/platform components, which are both exposed to uncertainties and change. Change appears in two forms: the evolution of architectural components under changing requirements and the adaptation of the infrastructure running applications. Cloud architecture continuity refers to the ability of a cloud system to change its architecture and maintain the validity of the goals that determine the architecture. Goal validity implies the satisfaction of goals in adapting or evolving systems. Architecture continuity aids technical sustainability, that is, the longevity of information, systems, and infrastructure and their adequate evolution with changing conditions. In a cloud setting that requires both steady alignment with technological evolution and availability, architecture continuity directly impacts economic sustainability. We investigate change models and change rules for managing change to support cloud architecture continuity. These models and rules define transformations of architectures to maintain system goals: evolution is about unanticipated change of structural aspects of architectures and adaptation is about anticipated change of architecture configurations. Both are driven by quality and cost, and both represent multi-dimensional decision problems under uncertainty. We have applied the models and rules for adaptation and evolution in research and industry consultancy projects.

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Pattern‐based multi‐cloud architecture migration

2016

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Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model

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Scalable Architectures for Platform-as-a-Service Clouds: Performance and Cost Analysis

Cited by 9 publications

References 16 publications

Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures

Pattern‐based multi‐cloud architecture migration

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