SlipStream: automated provisioning and continuous deployment in the cloud
Bibliographic record
Abstract
<!--HTML--> Cloud technology is now everywhere. Beyond the hype, it provides a real opportunity to improve the engineering of software systems. Lately the DevOps movement has also gain momentum, which take an agile approach at bringing developers and system administrators closer together to better engineer software systems. In this context, this presentation focuses on new tools for exploiting cloud services (private and public) in order to create a continuous flow between software commits and fully deployed and configured software systems, automatically and on-demand. To illustrate this, we present SlipStream and StratusLab. SlipStream is a new product developed by SixSq, able to create virtual machines and orchestrate multi-machine deployments. SlipStream started from an idea developed in the context of the ETICS project, led by CERN. StratusLab is an open-source IaaS distribution, able to create public and private clouds. This presentation will also describe a case study where SlipStream deploys an entire Mission Control System for the Operations Center of the European Space Agency. We also describe how SlipStream can be integrated with management tools, such as Puppet and Chef. About the speaker Marc-Elian Bégin is co-founder of SixSq. At SixSq he is the lead developer of the SlipStream product, leads the Integration and Test Work Package of the StratusLab FP7 project, delivers regular agile training and is coach for different customers, including the Operation Centre of ESA (ESOC). He worked at CERN from 2004 to 2008 in the IT Department, as developer and technical lead on the EGEE and ETICS projects. Before joining CERN, he worked for a decade in the space industry, contributing to the delivery of several software systems, both for the space agencies and industry in Canada and Europe. Marc-Elian is a regular speaker at international events, such as Agile2011, XP2011 and DevOpsDays. In Helix Nebula, he leads the Architecture and Technical Group, responsible for short and longer term technical solutions for the cloud collaboration.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".