Migrating legacy software to the cloud: approach and verification by means of two medical software use cases

Maenhaut, Pieter‐Jan; Moens, Hendrik; Ongenae, Veerle; Turck, Filip De

doi:10.1002/spe.2320

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Maenhaut et al demonstrated the benefits of migrating the legacy software to the cloud. The authors investigated and confirmed the long term benefits of migrating applications (such as nurse call systems, schedule planner, etc) for managing medical appointments that are widely used within hospitals.…”

Section: Related Workmentioning

confidence: 99%

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Framework for cloud‐based software test data generation service

2019

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This paper presents the framework of cloud-based software test data generation service (CSTS) that caters to cost-effective test data generation service in a cloud environment. In contrast to existing conventional or cloud-based testing frameworks, CSTS has a number of unique benefits. First, CSTS is designed to facilitate test data generation in minimum time and cost. Second, unlike existing frameworks which mandates clients to opt for resources to test their jobs, CSTS guides customer for selecting best cluster configuration in order to minimize the cost. While the existing models do not provide any solution for trust establishment in cloud computing services, CSTS delivers it by implementing security mechanism with the provision of role based access control. The security mechanism proposed in this paper ensures the protection of data and code of different users.Third, CSTS provides a mathematical pricing model to fulfill the expectations of customers and also to maximize the net profit of service providers. Cloud service request model has also been designed that postulates service level agreements between customers and service providers. We have evaluated, compared, and analyzed our framework and have found that it outperforms other existing cloud-based frameworks.

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Section: Related Workmentioning

confidence: 99%

“…The conclusion made by the authors is similar to our research work in which we proved effectiveness of test data generation framework over cloud. Although our proposed strategy works with different set of applications, our vision for the significance of working on cloud environment is same as that of the work of Maenhaut et al…”

Section: Related Workmentioning

confidence: 99%

Framework for cloud‐based software test data generation service

2019

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“…Java is a widely used object‐oriented programming language with the characteristics open‐source and cross‐platform . A typical software framework contains presentation layer, business logic layer, and data access layer . This study focuses on the data access and business logic layers.…”

Section: Related Frameworkmentioning

confidence: 99%

JDAS: a software development framework for multidatabases

et al. 2017

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Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object-oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases.

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“…Similar, Miranda et al (2013) conduct a cloud-to-cloud migration between two IaaS offerings that uses software metrics to calculate the estimated migration costs in man hours rather than making migration efforts explicit. When unveiling occurred effort, the focus is often on operational cost comparisons (Khajeh-Hosseini, Greenwood, & Sommerville, 2010;Khajeh-Hosseini, Sommerville, Bogaerts, & Teregowda, 2011;Tak, Urgaonkar, & Sivasubramaniam, 2011;Andrikopoulos, Song, & Leymann, 2013), e.g., infrastructure costs, support, and maintenance or migration effort in man hours Maenhaut, Moens, Ongenae, & De Turck, 2015). Solely, Ward et al (2010) mention migration metrics related to the effort to create build automation or the server provisioning time comparable to our deployment metrics.…”

Section: Related Workmentioning

confidence: 99%

Application Migration Effort in the Cloud

Kolb¹,

Lenhard²,

Wirtz³

2015

STCC

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Over the last years, the utilization of cloud resources has been steadily rising and an increasing number of enterprises are moving applications to the cloud. A leading trend is the adoption of Platform as a Service to support rapid application deployment. By providing a managed environment, cloud platforms take away a lot of complex configuration effort required to build scalable applications. However, application migrations to and between clouds cost development effort and open up new risks of vendor lock-in. This is problematic because frequent migrations may be necessary in the dynamic and fast changing cloud market. So far, the effort of application migration in PaaS environments and typical issues experienced in this task are hardly understood. To improve this situation, we present a cloud-to-cloud migration of a real-world application to seven representative cloud platforms. In this case study, we analyze the feasibility of the migrations in terms of portability and the effort of the migrations. We present a Dockerbased deployment system that provides the ability of isolated and reproducible measurements of deployments to platform vendors, thus enabling the comparison of platforms for a particular application. Using this system, the study identifies key problems during migrations and quantifies these differences by distinctive metrics.

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Migrating legacy software to the cloud: approach and verification by means of two medical software use cases

Cited by 16 publications

References 20 publications

Framework for cloud‐based software test data generation service

Framework for cloud‐based software test data generation service

JDAS: a software development framework for multidatabases

Application Migration Effort in the Cloud

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