Designing Resource-Aware Cloud Applications

Hähnle, Reiner; Johnsen, Einar Broch

doi:10.1109/mc.2015.172

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…It also explicitly supports the modeling of resource consumption on virtual machine instances [24]. Thus, the language allows analysis of deployment decisions, including a configurable model of cloud provisioning [17], and has been used for industrial case studies [3]. Both the resource requirements and timing properties of models can be expressed and analyzed, which makes it easy to compare deployment decisions at the level of models [33] by means of a large portfolio of analysis and deployment tools (see Figure 3).…”

Section: Formal Verification Tool Supports Deductive Analysis Of Behamentioning

confidence: 99%

Analysis of SLA Compliance in the Cloud - An Automated, Model-based Approach

Boer

Giachino

Gouw

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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Service Level Agreements (SLA) are commonly used to specify the quality attributes between cloud service providers and the customers. A violation of SLAs can result in high penalties.To allow the analysis of SLA compliance before the services are deployed, we describe in this paper an approach for SLA-aware deployment of services on the cloud, and illustrate its workflow by means of a case study. The approach is based on formal models combined with static analysis tools and generated runtime monitors. As such, it fits well within a methodology combining software development with information technology operations (DevOps).

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Section: Formal Verification Tool Supports Deductive Analysis Of Behamentioning

confidence: 99%

Analysis of SLA Compliance in the Cloud - An Automated, Model-based Approach

Boer

Giachino

Gouw

et al. 2019

Electron. Proc. Theor. Comput. Sci.

Self Cite

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“…Using model-based analyses, appropriate configurations and deployment decisions can be explored and compared "in the laboratory" (Hähnle and Johnsen, 2015), thereby helping users to predict the performance of an application before the application is deployed. Our goal is to give users an easy-touse support for such analyses based on a highly configurable and executable model.…”

Section: Introductionmentioning

confidence: 99%

A configurable and executable model of Spark Streaming on Apache YARN

Lin

Lee

et al. 2020

IJGUC

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Streams of data are produced today at an unprecedented scale. Efficient and stable processing of these streams requires a careful interplay between the parameters of the streaming application and of the underlying stream processing framework. Today, finding these parameters happens by trial and error on the complex, deployed framework. This paper shows that high-level models can help determine these parameters by predicting and comparing the performance of streaming applications running on stream processing frameworks with different configurations. To demonstrate this approach, this paper considers Spark Streaming, a widely used framework to leverage data streams on the fly and provide real-time stream processing. Technically, we develop a configurable and executable model to simulate both the streaming applications and the underlying Spark stream processing framework. Furthermore, we model the deployment of Spark Streaming on Apache YARN, which is a popular open-source distributed software framework for big data processing. We show that the developed model provides a satisfactory accuracy for predicting performance by means of empirical validation.

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“…Models are executable; a simulation tool for ABS supports rapid prototyping and visualization. The use of languages such as ABS enables developers to shift deployment decisions from late in the software engineering process to become an integral part of software design [8]. ABS permits to design and validate these services by connecting executable models to quality of service requirements, using a Cloud API to interface with an abstraction of the cloud provisioning.…”

Section: Introductionmentioning

confidence: 99%

Modeling Deployment Decisions for Elastic Services with ABS

Johnsen¹,

Pun²,

Tarifa³

2016

Electron. Proc. Theor. Comput. Sci.

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The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software designs that scale poorly, it is important to make deployment decisions for the service at design time, early in the development of the service itself. ABS offers a formal, model-based approach which integrates the design of services with the modeling of deployment decisions. In this paper, we illustrate the main concepts of this approach by modeling a scalable pool of workers with an auto-scaling strategy and by using the model to compare deployment decisions with respect to client traffic with peak loads.Comment: In Proceedings iFMCloud 2016, arXiv:1610.0770

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Designing Resource-Aware Cloud Applications

Abstract: Realizing the full potential of virtualized computation-the cloud-requires rethinking software development. Deployment decisions, and their validation, can and should be moved up the development chain into the design phase.

Cited by 10 publications

References 5 publications

Analysis of SLA Compliance in the Cloud - An Automated, Model-based Approach

Analysis of SLA Compliance in the Cloud - An Automated, Model-based Approach

A configurable and executable model of Spark Streaming on Apache YARN

Modeling Deployment Decisions for Elastic Services with ABS

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