Consolidation of underutilized virtual machines to reduce total power usage
Bibliographic record
Abstract
Data centers (DC) have become one of the biggest markets \nwith new challenges and opportunities in the past decade. \nMany big companies are owners of DCs providing services \nand cloud solutions. With this increasing demand and in- \nterest in cloud service, thousands of virtual machines (VM) \nare being instantiated to run a variety of services in a data \ncenter. Beside the benefit of service provisioning, a DC is a \nbig consumer of electric power and producer of greenhouse \ngasses consequently. Because the VMs are not using their \nassigned resources all the time, resources are multiplexed \nvia virtualization. However, current resource management \nmethods are oblivious to actual utilization or power con- \nsumption. Monitoring of servers in big data centers like \nGoogle and Twitter has shown that the current resource \nutilization is less than fifty percent in total. \nSpecifically, in this paper, we use OpenStack, a popu- \nlar cloud management software to orchestrate and manage \nVMs. In Open-Stack, the virtualization factor is a constant \nvalue oblivious to VM resource consumption. We design \nand implement two dynamic methods for adapting the vir- \ntualization factor based on the monitored VM resource con- \nsumption. In our first method, we identify VMs that are \nmostly idle, and we opportunistically migrate all idle VMs \nto one or more servers in such a way to keep the chance of \nperformance degradation to a minimum, while saving total \nresources. In our second, more general method, we model \nconsolidation of underutilized VMs as a knapsack problem. \nWe show that our methods save a significant amount of un- \nderutilized resources while minimizing performance degra- \ndation during and after dynamic reconfiguration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".