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2019
DOI: 10.1002/spe.2741
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CloudsStorm: A framework for seamlessly programming and controlling virtual infrastructure functions during the DevOps lifecycle of cloud applications

Abstract: The infrastructure-as-a-service (IaaS) model of cloud computing provides virtual infrastructure functions (VIFs), which allow application developers to flexibly provision suitable virtual machines' (VM) types and locations, and even configure the network connection for each VM. Because of the pay-as-you-go business model, IaaS provides an elastic way to operate applications on demand. However, in current cloud applications DevOps (software development and operations) lifecycle, the VM provisioning steps mainly… Show more

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Cited by 19 publications
(20 citation statements)
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“…There is a need to develop data processing technologies that address the problem by abstracting from (and virtualising) the platform(s) that take care of executing the processing pipeline. Such technologies should go in tandem with optimisation technologies and should provide the data processing designer with fine-grained processing directives and facilitate detailed specification of processing algorithms [35].…”
Section: Discussionmentioning
confidence: 99%
“…There is a need to develop data processing technologies that address the problem by abstracting from (and virtualising) the platform(s) that take care of executing the processing pipeline. Such technologies should go in tandem with optimisation technologies and should provide the data processing designer with fine-grained processing directives and facilitate detailed specification of processing algorithms [35].…”
Section: Discussionmentioning
confidence: 99%
“…All these works lack an efficient solution for the application to program and control the computing infrastructure in the DevOps lifecycle. Although our previous work CloudsStorm [7], [8] framework tackling the resource management issue from the application DevOps perspective, it still mainly focuses on working with Cloud resources, which is insufficient for a more complex Edge computing environment.…”
Section: Challenges and Related Workmentioning
confidence: 99%
“…It bridges the gap between real-time scheduling theory and Xen, and provides a platform for integrating a broad range of realtime and embedded systems. The performance guarantee at the software system level relies on the optimisation between application and infrastructure, e.g., CloudStorm [17] for service applications and PrEstoCloud [18] for real-time Big Data using an extension of the Fog computing paradigm to the extreme Edge of the network, real-time container scheduling [19]. However, the engineering of quality critical applications across heteronomous infrastructure for DApps is still very challenging, due to diverse programming interfaces, and the lack of effective programming methods.…”
Section: Introductionmentioning
confidence: 99%