A Holistic Decision Framework to Avoid Vendor Lock-in for Cloud SaaS Migration
Bibliographic record
Abstract
Cloud computing offers an innovative business model to enterprise for IT services consumption and delivery. Software as a Service (SaaS) is one of the cloud offerings that attract organisations as a potential solution in reducing their IT cost. However, the vast diversity among the available cloud SaaS services makes it difficult for customers to decide whose vendor services to use or even to determine a valid basis for their selections. Moreover, this variety of cloud SaaS services has led to proprietary architectures and technologies being used by cloud vendors, increasing the risk of vendor lock-in for customers. Therefore, when enterprises interact with SaaS providers within the purview of the current cloud marketplace, they often encounter significant lock-in challenges to migrating and interconnecting cloud. Hence, the complexity and variety of cloud SaaS service offerings makes it imperative for businesses to use a clear and well understood decision process to procure, migrate and/or discontinue cloud services. To date, the expertise and technological solutions to simplify such transition and facilitate good decision making to avoid lock-in risks in the cloud are limited. Besides, little investigation has been carried out to provide a comprehensive decision framework to support enterprises on how to avoid lock-in risks when selecting and implementing cloud-based SaaS solutions within existing environments. Such decision framework is important to reduce complexity and variations in implementation patterns on the cloud provider side, while at the same time minimising potential switching cost for enterprises by resolving integration issues with existing IT infrastructures. This paper proposes a holistic 6-step decision framework that enables an enterprise to assess its current IT landscape for potential SaaS replacement, and provides effective strategies to mitigate vendor lock-in risks in cloud (SaaS) migration. The framework follows research findings and addresses the core requirements for choosing vendor-neutral interoperable and portable cloud services without the fear of vendor lock-in, and architectural decisions for secure SaaS migration. Therefore, the results of this research can help IT managers have a safe and effective migration to cloud computing SaaS environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".