Quantitative Analysis of Web Services Using SRMC

Clark, Allan; Gilmore, Stephen; Tribastone, Mirco

doi:10.1007/978-3-642-01918-0_8

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Several works instead adopt a description that includes standard deviations [76,82,83] or finite ranges of variability for the execution times [84,85]. Parametric service models instead assume exponential or Markovian distributions [86,87], Pareto distributions to capture heavytailed execution times [88], or general distributions with Laplace transforms [89].…”

Section: Black-box Service Modelsmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

218

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Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

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Section: Black-box Service Modelsmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

218

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“…The success of Sensoria is demonstrated by the realisation of an integrated set of theoretical and concrete tools. In particular, UML-like visual languages have been developed for the high-level modelling of Service-Oriented applications (see, e.g., [18,7]), several process calculi have been designed to formally represent the operational aspects of such applications (see, e.g., [4,30,29,5]), analysis techniques have been developed to perform both qualitative and quantitative analysis on these formal models of the applications (see, e.g., [19,37,13]), and also runtime support for application deployment has been realised by providing some of the proposed calculi with an execution environment (see, e.g., [34,31]).…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Jolie

Lanese

Montesi

Zavattaro

2015

Software, Services, and Systems

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Jolie is an orchestration language conceived during Sensoria, an FP7 European project led by Martin Wirsing in the time frame 2005-2010. Jolie was designed having in mind both the novel-at project timeconcepts related to Service-Oriented Computing and the traditional approach to the modelling of concurrency typical of process calculi. The foundational work done around Jolie during Sensoria has subsequently produced many concrete results. In this paper we focus on two distinct advancements, one aiming at the development of dynamically adaptable orchestrated systems and one focusing on global choreographic specifications. These works, more recently, contributed to the realisation of a framework for programming dynamically evolvable distributed Service-Oriented applications that are correct-by-construction.

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“…This process is similar to methods employed for Service Oriented Architecture analysis and design in e.g [47], [48] , [49] or the UML MARTE Profile [50]. Enterprise architecture analysis can be used to support management of for instance Quality of Service of enterprise wide applications, in the development and evaluation of Service Level Agreements when designing or operating Service Oriented Architectures.…”

mentioning

confidence: 99%

Hybrid Probabilistic Relational Models for System Quality Analysis

Närman

Buschle

König

et al. 2010

2010 14th IEEE International Enterprise Distributed Object Computing Conference

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The formalism Probabilistic Relational Models (PRM) couples discrete Bayesian Networks with a modeling formalism similar to UML class diagrams and has been used for architecture analysis. PRMs are well-suited to perform architecture analysis with respect to system qualities since they support both modeling and analysis within the same formalism. A particular strength of PRMs is the ability to perform meaningful analysis of domains where there is a high level of uncertainty, as is often the case when performing system quality analysis. However, the use of discrete Bayesian networks in PRMs complicates the analysis of continuous phenomena. The main contribution of this paper is the Hybrid Probabilistic Relational Models (HPRM) formalism which extends PRMs to enable continuous analysis thus extending the applicability for architecture analysis and especially for tradeoff analysis of system qualities. HPRMs use hybrid Bayesian networks which allow combinations of discrete and continuous variables. In addition to presenting the HPRM formalism, the paper contains an example which details the use of HPRMs for architecture trade-off analysis.

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Quantitative Analysis of Web Services Using SRMC

Cited by 7 publications

References 25 publications

Quality-of-service in cloud computing: modeling techniques and their applications

Quality-of-service in cloud computing: modeling techniques and their applications

The Evolution of Jolie

Hybrid Probabilistic Relational Models for System Quality Analysis

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