Using Performance Models for Planning the Redeployment to Infrastructure-as-a-Service Environments: A Case Study

Lehrig, Sebastian; Becker, Steffen

doi:10.1109/qosa.2016.17

Cited by 3 publications

(7 citation statements)

References 19 publications

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“…We particularly show how we used this model to analyze capacity, scalability, elasticity, and efficiency metrics. We provide a detailed description how we created and evaluated the CloudStore model in [14]. This section provides a summary of our results (Sec.…”

Section: Analyzing Cloudstore With a Performance Modelmentioning

confidence: 99%

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CloudStore — towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing

Lehrig

Sanders

Brataas

et al. 2018

Future Generation Computer Systems

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Section: Analyzing Cloudstore With a Performance Modelmentioning

confidence: 99%

“…Otherwise, we successfully evaluated the performance model and can stop with an accurate model of CloudStore. In [14], we specify how to measure the percentage error between analysis and measurement results and argue to stop iterating when the difference is below 30%. In later steps (i.e., in Sec.…”

Section: Creating a Cloudstore Performance Modelmentioning

confidence: 99%

CloudStore — towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing

Lehrig

Sanders

Brataas

et al. 2018

Future Generation Computer Systems

Self Cite

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“…This section motivates architectural analyses on the basis of ATs along an online book shop example system. The book shop is based on an architectural model suited for architectural analyses that we previously specified . Our model particularly reflects the QoS properties of an implemented and deployed book shop version .…”

Section: Motivating Example: An Online Book Shopmentioning

confidence: 99%

“…For illustrating the AT method, we conduct a case study in which we use and extend the architectural analysis approach Palladio with the AT method.The case study focuses on the domain of cloud computing. Cloud computing is suited because of its interesting QoS properties (e.g., performance, scalability, elasticity, and cost‐efficiency ), the required reusable architectural knowledge to foster these properties (e.g., architectural patterns for elasticity ), and a large amount of architectural constraints (e.g., dictated by the targeted cloud computing environment).In the case study, we migrate CloudStore , a distributed online book shop, from a classical distributed bare‐metal setup to a virtualized cloud computing environment. We guide our migration along appropriate architectural knowledge captured via ATs.The case study shows that the AT method is applicable to the cloud computing domain and suited for the associated QoS properties performance, scalability, elasticity, and cost‐efficiency.…”

Section: Introductionmentioning

confidence: 99%

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The architectural template method: templating architectural knowledge to efficiently conduct quality‐of‐service analyses

2017

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Software architects plan the realization of software systems by assessing design decisions on the basis of architectural models. Using these models as input, architectural analyses assess the impact of architects' decisions on quality-of-service properties. While the creation of suitable architectural models requires software architects to apply complex architectural knowledge, for example, in the form of established architectural styles and patterns, current architectural analyses lack support for directly reusing such knowledge. This lack points to an unused potential to make the work of software architects more effective and efficient.To use this potential, we introduce the architectural template (AT) method, an engineering method that makes design-time analyses of quality-of-service properties of software systems more efficient. The AT method allows to quantify quality-of-service properties on the basis of reusable modeling templates that capture recurring architectural knowledge. Architects just need to customize such templates with system-specific parts. In a case study, we illustrate the applicability of the AT method in the domains of distributed and cloud computing and its suitability for the quality-of-service properties performance, scalability, elasticity, and cost-efficiency. TEMPLATING ARCHITECTURAL KNOWLEDGE TO EFFICIENTLY CONDUCT QOS ANALYSES 269 which may be used by clients to judge service quality' [9]. Architectural analyses quantify QoS properties on the basis ofS architectural models of a targeted system. These quantifications enable early evaluations and, thus, inexpensive revisions of disadvantageous design decisions. However, the creation of suitable architectural models for architectural analyses has formerly required a high modeling effort [10, p. 458]-even in recurring modeling situations [11].To approach this problem, this paper contributes the architectural template (AT) method as an extension to architectural analyses and illustrates its application in the cloud computing domain. The AT method provides software architects with templates that formally capture reusable architectural knowledge (such as architectural styles and architectural patterns) for recurring modeling and analysis situations. By applying such templates to architectural models, software architects become more effective and efficient because captured design decisions can automatically be integrated into architectural models, checked for consistency, and be analyzed.In the following, this introductory section refines this motivation and idea behind the AT method and gives an overview of this paper. Analyzing quality-of-serviceA concrete category of engineering approaches are architectural analyses [2, p. 291]. In Koziolek's process for architectural analyses [12, p. 3] (see 'QoS analysis model' and 'analysis results' of Figure 1), software architects quantify a system's QoS properties on the basis of architectural models that (semi-)formally capture taken decisions, for example, the structure of the planned software. ...

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Analyzing a Modeled System

Lehrig¹,

Brataas

Cecowski

et al. 2017

Engineering Scalable, Elastic, and Cost-Efficient Cloud Computing Applications

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Using Performance Models for Planning the Redeployment to Infrastructure-as-a-Service Environments: A Case Study

Cited by 3 publications

References 19 publications

CloudStore — towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing

CloudStore — towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing

The architectural template method: templating architectural knowledge to efficiently conduct quality‐of‐service analyses

Analyzing a Modeled System

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