Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Buyya, Rajkumar; Yeo, Chee Shin; Venugopal, Srikumar; Broberg, James; Brandić, Ivona

doi:10.1016/j.future.2008.12.001

Cited by 4,753 publications

(2,202 citation statements)

References 24 publications

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“…With the increase in relevance of modern Web engineering concepts, such as continuous deployment [16] and cloud computing [17], empirical research on how to quickly build and deploy code on virtualized infrastructure has received more attention. Seminal early work on build systems has been conducted by McIntosh et al, who studied both, Java-based build systems [18] and building software more generally [19].…”

Section: Related Workmentioning

confidence: 99%

An empirical analysis of the Docker container ecosystem on GitHub

Cito

Schermann

Wittern

et al. 2017

Preprint

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Abstract-Docker allows packaging an application with its dependencies into a standardized, self-contained unit (a so-called container), which can be used for software development and to run the application on any system. Dockerfiles are declarative definitions of an environment that aim to enable reproducible builds of the container. They can often be found in source code repositories and enable the hosted software to come to life in its execution environment. We conduct an exploratory empirical study with the goal of characterizing the Docker ecosystem, prevalent quality issues, and the evolution of Dockerfiles. We base our study on a data set of over 70000 Dockerfiles, and contrast this general population with samplings that contain the Top-100 and Top-1000 most popular Docker-using projects. We find that most quality issues (28.6%) arise from missing version pinning (i.e., specifying a concrete version for dependencies). Further, we were not able to build 34% of Dockerfiles from a representative sample of 560 projects. Integrating quality checks, e.g., to issue version pinning warnings, into the container build process could result into more reproducible builds. The most popular projects change more often than the rest of the Docker population, with 5.81 revisions per year and 5 lines of code changed on average. Most changes deal with dependencies, that are currently stored in a rather unstructured manner. We propose to introduce an abstraction that, for instance, could deal with the intricacies of different package managers and could improve migration to more light-weight images.

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Section: Related Workmentioning

confidence: 99%

An empirical analysis of the Docker container ecosystem on GitHub

Cito

Schermann

Wittern

et al. 2017

Preprint

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“…Several papers have explained IaaS, PaaS and SaaS as the cloud business model (Buyya et al 2009;Chen, Wills, Gilbert, Bacigalupo, 2010;Armbrust et al, 2009;Schubert, Jeffery and Neidecker-Lutz, 2010). Despite all having a slightly different focus, all of them are classified under "Service Provider and Service Orientation", regardless of whether they are IaaS, PaaS, or SaaS service providers, or their focus is on billing, or SLA or CRM, since this is a mainstream model that still has areas of unexploited opportunities.…”

Section: Cloud Computing For Business Usementioning

confidence: 99%

A Proposed Framework for Cloud Computing adoption

Chang¹

2016

International Journal of Organizational and Collective Intelligence

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This paper presents a review related to Cloud Computing focusing on Cloud business requirements. From the review we recommend a number of methods managing Cloud services and evaluating its service performance, including the use of a pair of the Hexagon Models. Three organizational challenges of Cloud adoption are identified: (i) Organizational Sustainability; (ii) Portability and (iii) Linkage. The Cloud Computing Adoption Framework (CCAF) is designed to deal with these challenges by helping organizations to achieve good Cloud designs, deployment and services. How these three challenges are addressed by the CCAF is demonstrated using case studies. Services implemented by CCAF are reviewed using the Hexagon Models for comparison. This paper provides recommendations to help organizations, researchers and practitioners to understand Cloud business context, to measure their risk and return analysis, to migrate their services to Cloud from all types and to connect and integrate different services as a single service. Future direction and security concerns have been addressed in our framework.

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“…Firstly, it provides an on-demand and online access for users, which is cost-effective and users will not be charged when they are not using the resource (Buyya et al 2009). Secondly, the cloud can be accessible from anywhere and enables collaboration between developers and users from different locations.…”

Section: Cloud-based Software Testingmentioning

confidence: 99%

How to test bioinformatics software?

et al. 2015

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Bioinformatics is the application of computational, mathematical and statistical techniques to solve problems in biology and medicine. Bioinformatics programs developed for computational simulation and large-scale data analysis are widely used in almost all areas of biophysics. The appropriate choice of algorithms and correct implementation of these algorithms are critical for obtaining reliable computational results. Nonetheless, it is often very difficult to systematically test these programs as it is often hard to verify the correctness of the output, and to effectively generate failure-revealing test cases. Software testing is an important process of verification and validation of scientific software, but very few studies have directly dealt with the issues of bioinformatics software testing. In this work, we review important concepts and state-ofthe-art methods in the field of software testing. We also discuss recent reports on adapting and implementing software testing methodologies in the bioinformatics field, with specific examples drawn from systems biology and genomic medicine.

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Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Cited by 4,753 publications

References 24 publications

An empirical analysis of the Docker container ecosystem on GitHub

An empirical analysis of the Docker container ecosystem on GitHub

A Proposed Framework for Cloud Computing adoption

How to test bioinformatics software?

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