On the problem of mapping virtual machines to physical machines for delay sensitive services
Bibliographic record
Abstract
Server virtualization is usually used to consolidate multiple virtual machines in the same physical server for power consumption and cost reduction purposes. On the other hand, several virtual machines' killer applications are delay sensitive since they interact with the user in real time such as IP telephony signalling, thin clients and video. Therefore, server virtualization can be exploited to run VMs on servers that provide the lowest delay to their users. In this paper, we consider the case of a virtualized IP Multimedia Subsystems. As a first optimization objective, assigning VMs to lowest delay servers will consolidate them in very few servers serving high density regions. However, it will create overloaded hot spot servers. Thus, in order to provide a globally higher number of VMs to servers mappings, we allow delay-sensitive applications to tolerate a delay up to a given threshold. Consequently, we will be able to provide an option to optimize a second objective that consists of load balancing over a higher number of servers. We show that this optimization problem is NP complete. We formulate the problem using a weighted bipartite matching graph and then we solve it using a modified Hungarian method. Results show that our proposed algorithms provide near-optimal solutions in short time.
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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".