An experimental methodology to evaluate energy efficiency and performance in an enterprise virtualized environment
Bibliographic record
Abstract
omputing servers generally have a narrow dynamic power range. For instance, even completely idle servers consume between 50% and 70% of their peak power. Since the usage rate of the server has the main influence on its power consumption, energy-efficiency is achieved whenever the utilization of the servers that are powered on reaches its peak. For this purpose, enterprises generally adopt the following technique: consolidate as many workloads as possible via virtualization in a minimum amount of servers (i.e. maximize utilization) and power down the ones that remain idle (i.e. reduce power consumption). However, such approach can severely impact servers' performance and reliability. In this paper, we propose a methodology to determine the ideal values for power consumption and utilization for a server without performance degradation. We accomplish this through a series of experiments using two typical types of workloads commonly found in enterprises: TPC-H and SPECpower ssj2008 benchmarks. We use the first to measure the amount of queries responded successfully per hour for different numbers of users (i.e. [email protected]) in the VM. Moreover, we use the latter to measure the power consumption and number of operations successfully handled by a VM at different target loads. We conducted experiments varying the utilization level and number of users for different VMs and the results show that it is possible to reach the maximum value of power consumption for a server, without experiencing performance degradations when running indi- vidual, or mixing workloads.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".