Bibliographic record
Abstract
The acquisition of IT services is key to any public or private organisation and the advent of cloud computing requires innovation in the procurement of cloud services. Although cloud computing has become increasingly popular, it appears that potential customers, in the public sector in general and in the research community in particular, are facing barriers that inhibit the wider adoption of cloud services. This report presents a list of barriers to cloud services procurement identified through literature, in-depth interviews of IT managers surveyed over a period of 3 months, and input provided by the PICSE Task Force members as well as intergovernmental research organisations such as CERN and EMBL. The survey demonstrated that barriers to cloud service procurement mainly relate to the adoption of new technology (i.e. cloud computing) and the procurement process itself. The first category encompasses legal jurisdiction impediments, which Eurostat [1] highlighted as one of the main barriers to the procurement of cloud services. Indeed, services are often hosted in one country and consumed in another, hence cloud consumers’ uncertainty about data location and the applicable laws in case of dispute or in relation to compliance. Prerequisites such as expertise and knowledge of both contractual and operational aspects also impede the purchase of cloud computing services. The barriers posed by the procurement process vary depending on the type of process used. Restricted procurement processes suffer from a lack of competition and higher costs while open procurement processes are time consuming, require detailed specifications to be ready at the start of the procurement process and therefore lead to higher tendering and evaluation costs. Cloud marketplaces and brokerage services are seen as aiding the procurement process but suffering from under-investment and thus not sufficiently mature. In addition, the nature of cloud services and the pay-per-use method can complicate budget planning for research organisations. Therefore, framework procurement agreements are perceived as a good alternative for the procurement of cloud services; along with PCP (Pre-Commercial Procurement), PPI (Public Procurement of Innovative solutions), and JPA (Joint Procurement Actions), which could potentially fulfil the needs of the research community. To combat this situation and increase the uptake of cloud computing in the public research sector, cloud service providers (CSPs) are advised to increase transparency in their offers; particularly regarding security, privacy and data management, ensure the trustworthiness of their privacy policies and improve the Service Level Agreements (SLA) so that their offers stand out. Similarly users are advised to acknowledge that the commoditisation of IT services that cloud services represents has economic advantages and that customisation of those services will increase procurement costs and potentially increase service provider dependence.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.128 | 0.041 |
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".