CVL: A Cloud Vendor Lock-In Prediction Framework

Amal Alhosban (), Saichand Pesingu and Krishnaveni Kalyanam
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Amal Alhosban: Computer Science and Director Academic Program, College of Innovation and Technology, University of Michigan-Flint, Flint, MI 48502, USA
Saichand Pesingu: Computer Science and Director Academic Program, College of Innovation and Technology, University of Michigan-Flint, Flint, MI 48502, USA
Krishnaveni Kalyanam: Computer Science and Director Academic Program, College of Innovation and Technology, University of Michigan-Flint, Flint, MI 48502, USA

Mathematics, 2024, vol. 12, issue 3, 1-18

Abstract: This paper presents the cloud vendor lock-in prediction framework (CVL), which aims to address the challenges that arise from vendor lock-in in cloud computing. The framework provides a systematic approach to evaluate the extent of dependency between service providers and consumers and offers predictive risk analysis and detailed cost assessments. At the heart of the CVL framework is the Dependency Module, which enables service consumers to input weighted factors that are critical to their reliance on cloud service providers. These factors include service costs, data transfer expenses, security features, compliance adherence, scalability, and technical integrations. The research delves into the critical factors that are necessary for dependency calculation and cost analysis, providing insights into determining dependency levels and associated financial implications. Experimental results showcase dependency levels among service providers and consumers, highlighting the framework’s utility in guiding strategic decision-making processes. The CVL is a powerful tool that empowers service consumers to proactively navigate the complexities of cloud vendor lock-in. By offering valuable insights into dependency levels and financial implications, the CVL aids in risk mitigation and facilitates informed decision-making.

Keywords: cloud vendor lock-in; dependency analysis; cost evaluation; cloud service providers; risk mitigation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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