DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management
Bibliographic record
Abstract
Computing today is shifting from hosting services in servers owned by individual organizations to data centres providing resources to a number of organizations on a shared infrastructure. Managing such a data centre presents a unique set of goals and challenges. Through the use of virtualization, multiple users can run isolated virtual machines (VMs) on a single physical host, allowing for a higher server utilization. By consolidating VMs onto fewer physical hosts, infrastructure costs can be reduced in terms of the number of servers required, power consumption, and maintenance. To meet constantly changing workload levels, running VMs may need to be migrated (moved) to another physical host. Algorithms to perform dynamic VM reallocation, as well as dynamic resource provisioning on a single host, are open research problems. Experimenting with such algorithms on the data centre scale is impractical. Thus, there is a need for simulation tools to allow rapid development and evaluation of data centre management techniques. We present DCSim, an extensible simulation framework for simulating a data centre hosting an Infrastructure as a Service cloud. We evaluate the scalability of DCSim, and demonstrate its usefulness in evaluating VM management techniques.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".