A Risk Management Approach for a Sustainable Cloud Migration
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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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".