A Risk Management Approach for a Sustainable Cloud Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing is not just about resource sharing, cost savings and optimisation of business performance; it also involves fundamental concerns on how businesses need to respond on the risks and challenges upon migration. Managing risks is critical for a sustainable cloud adoption. It includes several dimensions such as cost, practising the concept of green IT, data quality, continuity of services to users and clients, guarantee tangible benefits. This paper presents a risk management approach for a sustainable cloud migration. We consider four dimensions of sustainability, i.e., economic, environmental, social and technology to determine the viability of cloud for the business context. The risks are systematically identified and analysed based on the existing in house controls and the cloud service provider offerings. We use Dempster Shafer (D-S) theory to measure the adequacy of controls and apply semi-quantitative approach to perform risk analysis based on the theory of belief. The risk exposure for each sustainability dimension allows us to determine the viability of cloud migration. A practical migration use case is considered to determine the applicability of our work. The results identify the risk exposure and recommended control for the risk mitigation. We conclude that risks depend on specific migration case and both Cloud Service Provider (CSP) and users are responsible for the risk mitigation. Inherent risks can evolve due to the cloud migration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it