Power containers
Bibliographic record
Abstract
Energy efficiency and power capping are critical concerns in server and cloud computing systems. They face growing challenges due to dynamic power variations from new client-directed web applications, as well as complex behaviors due to multicore resource sharing and hardware heterogeneity. This paper presents a new operating system facility called "power containers" that accounts for and controls the power and energy usage of individual fine-grained requests in multicore servers. This facility relies on three key techniques---1) online model that attributes multicore power (including shared maintenance power) to concurrently running tasks, 2) alignment of actual power measurements and model estimates to enable online model recalibration, and 3) on-the-fly application-transparent request tracking in multi-stage servers to isolate the power and energy contributions and customize per-request control. Our mechanisms enable new multicore server management capabilities including fair power capping that only penalizes power-hungry requests, and energy-aware request distribution between heterogeneous servers. Our evaluation uses three multicore processors (Intel Woodcrest, Westmere, and SandyBridge) and a variety of server and cloud computing (Google App Engine) workloads. Our results demonstrate the high accuracy of our request power accounting (no more than 11% errors) and the effectiveness of container-enabled power virus isolation and throttling. Our request distribution case study shows up to 25% energy saving compared to an alternative approach that recognizes machine heterogeneity but not fine-grained workload affinity.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".