Modeling Performance of Elasticity Rules for Cloud-Based Applications

Suleiman, Basem; Venugopal, Srikumar

doi:10.1109/edoc.2013.31

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…Suleiman et al have proposed in [Suleiman 2013] an analytical model, using queuing theory, to study the effects of elasticity strategies on the performance of multi-tier applications (more particularly three-tier applications) deployed on cloud infrastructures. Particularly, they studied the impact of changing the threshold values, used by the elasticity strategy, on application performance.…”

Section: Elasticity Strategies Evaluationmentioning

confidence: 99%

“…In [Amziani 2015],the author proposed an approach for ensuring and evaluating SBPs elasticity in the cloud by defining: (i) a formal model representing the deployment model of SBPs associated with two elasticity mechanisms (Duplication/Consolidation) Figure 3.10: The analytical model for elasticity evaluation [Suleiman 2013] for horizontal scaling that allow to add/remove service's copies in order to preserve some QoS requirements (process/service level) and (ii) an elasticity controller that is used to ensure SBPs elasticity through applying the defined elasticity mechanisms according to a given elasticity strategy and allows to evaluate different elasticity strategies. The formal model for SBP elasticity has been modelled based on Petri nets to describe their composed services and the relationship between them without considering the distinguish characteristics of services requests.…”

Section: Elasticity Strategies Evaluationmentioning

confidence: 99%

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Description and Evaluation of Elasticity Strategies for Business Processes in the Cloud

Bhiri

Tata

2016

2016 IEEE International Conference on Services Computing (SCC)

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Titre : Description et évaluation de stratégies d'élasticité des processus métiers dans le Cloud Mots clés : Applications à base de services, Stratégies d'élasticité, Evaluation, Modèle d'élasticité, Cloud Computing. Résumé : De nos jours, de plus en plus d'entreprises migrent leurs applications à base de services (AbSs) vers le Cloud. L'élasticité du Cloud Computing est la caractéristique principale derrière ce phénomène de migration. Le principe d'élasticité a attiré beaucoup d'attention ces dernières années comme une tâche pivot qui permet d'assurer un bon compromis entre les Qualités de service désirées et les coûts opérationnels des AbSs. Toutefois, le contrôle d'élasticité des AbSs et la définition des stratégies d'élasticité non-triviales sont encore des tâches difficiles à réaliser. La difficulté de ces tâches est de plus accentuée avec l'absence d'un langage unifié pour exprimer ces stratégies et un moyen pour les évaluer et les valider avant de les utiliser dans un environnement Cloud réel. Dans notre travail, nous nous intéressons à la définition et l'évaluation des stratégies d'élasticité des applications à base de services dans le Cloud.

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Section: Elasticity Strategies Evaluationmentioning

confidence: 99%

Section: Elasticity Strategies Evaluationmentioning

confidence: 99%

Description and Evaluation of Elasticity Strategies for Business Processes in the Cloud

Bhiri

Tata

2016

2016 IEEE International Conference on Services Computing (SCC)

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“…Rules for controlling cloud elasticity have been proposed in [21] as well as in the MODAClouds 5 and Optimis 6 projects. Those elasticity rules are defined during design time.…”

Section: Privacy Checks During Runtimementioning

confidence: 99%

“…In order to implement the geo-location policy concept, existing technologies and languages may be used. The elasticity rules presented in [21] specify the exchange of resources but do not reflect data classifications and content types. Approaches for specifying data access, e.g., work based on XACML 9 and [7], do not provide entities for specifying data types and geo-location constraints.…”

Section: Data Geo-location Policymentioning

confidence: 99%

A Runtime Model Approach for Data Geo-location Checks of Cloud Services

Schmieders

Metzger

Pohl

2014

Service-Oriented Computing

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Organizations have to comply with geo-location policies that prescribe geographical locations at which personal data may be stored or processed. When using cloud services, checking data geo-location policies during design-time is no longer possible -data geo-location policies need to be checked during run-time. Cloud elasticity mechanisms dynamically replicate and migrate virtual machines and services among data centers, thereby affecting the geo-location of data. Due to the dynamic nature of such replications and migrations, the actual, concrete changes to the deployment of cloud services and thus to the data geolocations are not known. We propose a policy checking approach utilizing runtime models that reflect the deployment and interaction structure of cloud services and components. By expressing privacy policy checks as an st-connectivity problem, potential data transfers that violate the geolocation policies can be rapidly determined. We experimentally evaluate our approach with respect to applicability and performance using an SOA-version of the CoCoME case study.

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Monitoring and Checking Privacy Policies of Cloud Services Based on Models

Schmieders

2015

Service-Oriented Computing - ICSOC 2014 Workshops

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Modeling Performance of Elasticity Rules for Cloud-Based Applications

Cited by 14 publications

References 5 publications

Description and Evaluation of Elasticity Strategies for Business Processes in the Cloud

Description and Evaluation of Elasticity Strategies for Business Processes in the Cloud

A Runtime Model Approach for Data Geo-location Checks of Cloud Services

Monitoring and Checking Privacy Policies of Cloud Services Based on Models

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