Automating serverless deployments for DevOps organizations

Sokolowski, Daniel; Weisenburger, Pascal; Salvaneschi, Guido

doi:10.1145/3468264.3468575

Cited by 15 publications

(10 citation statements)

References 42 publications

(40 reference statements)

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“…(e) Continuous Deployment (C5): Automated Deployment Pipeline (P8): An automated deployment pipeline automates the process of deploying software changes to production environments 88 . It is a critical component of continuous deployment, allowing for rapid and reliable deployment of new features or bug fixes. Defining the Deployed Features (P11): Defining the deployed features involves specifying which features or changes will be included in a particular deployment 92 . It is part of continuous deployment as it helps determine the scope of each deployment and ensures a smooth release process. Planning for Early Release (P17): Planning for early release involves considering factors like user feedback, market demands, and business goals to determine when to release new features or updates 93 .…”

Section: Results and Analysismentioning

confidence: 99%

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Multicriteria decision‐making–based framework for implementing DevOps practices: A fuzzy best–worst approach

Kumar,

Nadeem,

Shameem

2023

J Software Evolu Process

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Increasingly, software organizations are implementing DevOps culture to benefit from it in terms of continuous testing, delivery, improvement, and so forth. Implementing DevOps is difficult due to a lack of understanding about the practices and their effective application for its effective implementation. This paper aims to explore different DevOps practices that can be implemented in software organizations. The study involves conducting a systematic literature review (SLR) to identify DevOps implementation practices, followed by the utilization of the fuzzy best–worst method (FBWM) to establish a taxonomy or classification of software practices. We have used an SLR to investigate the practices, and subsequently, the survey method was followed to validate the identified practices. Moreover, the best–worst method (BWM) was considered to evaluate the significance and develop the taxonomy of the practices. The results of this study extracted 19 practices that have been identified in the SLR process. The identified factors are further classified into six core DevOps lifecycle phases. The results of the BWM approach are shown. The outcomes of the study conclude that the proposed taxonomy of the practices could help DevOps practitioners and researchers effectively implement them in software development organizations.

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Section: Results and Analysismentioning

confidence: 99%

“…• Defining the Deployed Features (P11): Defining the deployed features involves specifying which features or changes will be included in a particular deployment. 92 It is part of continuous deployment as it helps determine the scope of each deployment and ensures a smooth release process.…”

Section: Validation Responsesmentioning

confidence: 99%

Multicriteria decision‐making–based framework for implementing DevOps practices: A fuzzy best–worst approach

Kumar,

Nadeem,

Shameem

2023

J Software Evolu Process

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“…A first version of µs is implemented as a TypeScript library using Pulumi as driver and Hareactive [40] for the reactive engine [37] and presented in [36]. With it, a set of initial evaluations was performed.…”

Section: Achieved Resultsmentioning

confidence: 99%

Deployment coordination for cross-functional DevOps teams

Sokolowski

2021

Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Software stability and reliability are the core concerns of DevOps. They are improved by tightening the collaboration between developers and operators in cross-functional teams on the one hand and by automating operations through continuous integration (CI) and infrastructure as code (IaC) on the other hand. Ideally, teams in DevOps are fully independent. Still, their applications often depend on each other in practice, requiring them to coordinate their deployment through centralization or manual coordination.With this work, we propose and implement the novel IaC solution µs ([mju:z] łmusež ), which automates deployment coordination in a decentralized fashion. µs is the first approach that is compatible with the DevOps goals as it enables truly independent operations of the DevOps teams. We define our research problem through a questionnaire survey with IT professionals and evaluate the solution by comparing it to other modern IaC approaches, assessing its performance, and applying it to existing IaC programs. CCS CONCEPTS• Computer systems organization → Cloud computing; • Software and its engineering → Orchestration languages; Cloud computing; Architecture description languages.

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“…Both terms indicate a kind of operational abstraction. Furthermore, both terms are considered important within the IaC and DevOps community [93,252]. When comparing the relative frequency of these terms used in queries in subfigure (e), Kubernetes as a search term was used four times more often than Serverless.…”

Section: Search Trends At Google's Search Enginementioning

confidence: 99%

A Simulation Framework for Function as a Service

Manner

2024

Schriften Aus Der Fakultät Wirtschaftsinformatik Und Angewandte Informatik Der Otto-Friedrich-Universität Bamberg

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Serverless Computing is seen as a game changer in operating large-scale applications. While practitioners and researches often use this term, the concept they actually want to refer to is Function as a Service (FaaS). In this new service model, a user deploys only single functions to cloud platforms where the cloud provider deals with all operational concerns – this creates the notion of server-less computing for the user. Nonetheless, a few configurations for the cloud function are necessary for most commercial FaaS platforms as they influence the resource assignments like CPU time and memory. Due to these options, there is still an abstracted perception of servers for the FaaS user. The resource assignment and the different strategies to scale resources for public cloud offerings and on-premise hosted open-source platforms determine the runtime characteristics of cloud functions and are in the focus of this work. Compared to cloud offerings like Platform as a Service, two out of the five cloud computing characteristics improved. These two are rapid elasticity and measured service. FaaS is the first computational cloud model to scale functions only on demand. Due to an independent scaling and a strong isolation via virtualized environments, functions can be considered independent of other cloud functions. Therefore, noisy neighbor problems do not occur. The second characteristic, measured service, targets billing. FaaS platforms measure execution time on a millisecond basis and bill users accordingly based on the function configuration. This leads to new performance and cost trade-offs. Therefore, this thesis proposes a simulation approach to investigate this tradeoff in an early development phase. The alternative would be to deploy functions with varying configurations, analyze the execution data from several FaaS platforms and adjust the configuration. However, this alternative is time-consuming, tedious and costly. To provide a proper simulation, the development and production environment should be as similar as possible. This similarity is also known as dev-prod parity. Based on a new methodology to compare different virtualized environments, users of our simulation framework are able to execute functions on their machines and investigate the runtime characteristics for different function configurations at several cloud platforms without running their functions on the cloud platform at all. A visualization of the local simulations guide the user to choose an appropriate function configuration to resolve the mentioned trade-off dependent on their requirements.

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Automating serverless deployments for DevOps organizations

Cited by 15 publications

References 42 publications

Multicriteria decision‐making–based framework for implementing DevOps practices: A fuzzy best–worst approach

Multicriteria decision‐making–based framework for implementing DevOps practices: A fuzzy best–worst approach

Deployment coordination for cross-functional DevOps teams

A Simulation Framework for Function as a Service

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