DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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 it