Bibliographic record
Abstract
There is currently a lot of interest in resource virtualization as an important technique for addressing the problems of manageability, reliability, and security in computer systems. Resource virtualization decouples the user's perception of hardware and software resources from the actual implementation of these resources. It adds a flexible and programmable layer of software between user applications (such as database systems) and the resources that they use. This layer of software maps the virtual resources perceived by the applications to real physical resources. An example of this layer of software is a virtual machine monitor, which partitions the resources of a machine (CPU, disk, memory, network, etc.) into multiple virtual machines, and independent operating systems and applications can be installed on each virtual machine. The power of resource virtualization comes from the ability to manage the mapping from virtual resources to physical resources in the virtualization layer, and to change it as needed.The trend towards virtualization is of interest to us in the database research community because database systems are increasingly being run in virtualized environments. This presents a major opportunity since virtualization can help in solving many important problems in the areas of database system usability, manageability, deployment, scalability, and availability. Leveraging the capabilities of virtualization to solve these problems will require some effort on the part of our community. At the same time, virtualization poses some unique research challenges that must be addressed to enable database systems to run efficiently in these virtualized environments that are becoming increasingly common. In this tutorial, we will introduce resource virtualization and how it affects database systems. We will present the opportunities that resource virtualization provides for database systems and the unique research challenges that it poses, and we will review ongoing research in this area.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".