Model based control for multi-cloud applications

Miglierina, Marco; Gibilisco, Giovanni Paolo; Ardagna, Danilo; Nitto, Elisabetta Di

doi:10.1109/mise.2013.6595294

Cited by 22 publications

(14 citation statements)

References 14 publications

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“…Further optimization of distribution of applications like the Palladio-based approach discussed in (Miglierina et al, 2013) aims at optimizing for availability and operational expenses. Moreover, optimization mechanisms are based on simulation techniques requiring the definition of their corresponding models.…”

Section: Related Workmentioning

confidence: 99%

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

Sáez

Andrikopoulos

Leymann

2016

Proceedings of the 6th International Conference on Cloud Computing and Services Science

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The increase of available Cloud services and providers has contributed to accelerate the development and has broaden the possibilities for building and provisioning Cloud applications in heterogeneous Cloud environments. The necessity for satisfying business and operational requirements in an agile and rapid manner has created the need for adapting traditional methods and tooling support for building and provisioning Cloud applications. Focusing on the application's performance and its evolution, we observe a lack of support for specifying, capturing, analyzing, and reasoning on the impact of using different Cloud services and configurations. This paper bridges such a gap by proposing the conceptual and tooling support to enhance Cloud application topology models to capture and analyze the evolution of the application's performance. The tooling support is built upon an existing modeling environment, which is subsequently evaluated using the MediaWiki (Wikipedia) application and its realistic workload.

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Section: Related Workmentioning

confidence: 99%

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

Sáez

Andrikopoulos

Leymann

2016

Proceedings of the 6th International Conference on Cloud Computing and Services Science

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“…To overcome this a method of automating the process of deployment is required. The Modeldriven based approach is proposed in [22] and [23]. In the latter, the MODACLOUD solution is described.…”

Section: Related Workmentioning

confidence: 99%

CloudWave: Where adaptive cloud management meets DevOps

Bruneo

Fritz

Keidar-Barner

et al. 2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. The design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another, which limits both the efficiency of the cloud environment and the Quality of Service which can be delivered to the hosted applications. This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles. Abstract-The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

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“…The models manipulated by the MODACloudML are grouped as shown in the dashed box of Figure 4. More details can be found in [7], [8].…”

Section: B Conceptsmentioning

confidence: 99%

Towards Multi-Clouds engineering

Petcu

Nitto

Ardagna

et al. 2014

2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Multi-Clouds are nowadays motivated by the needs of Cloud service consumers to ensure a certain level of service quality. Main requirements and challenges of the developing Multi-Clouds are analyzed in this paper. A particular attention is payed on the application and service portability. As complementary solution to the current approaches of using uniform application programming interfaces or standard protocols, a model-driven engineering approach dealing with the quality of service in Multi-Clouds is discussed.

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Model based control for multi-cloud applications

Cited by 22 publications

References 14 publications

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

Consolidation of Performance and Workload Models in Evolving Cloud Application Topologies

CloudWave: Where adaptive cloud management meets DevOps

Towards Multi-Clouds engineering

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