Bibliographic record
Abstract
Burstable instances have recently been introduced by cloud providers as a cost-efficient alternative to customers that do not require powerful machines for running their workloads. Unlike conventional instances, the CPU capacity of burstable instances is rate limited, but they can be boosted to their full capacity for small periods when needed. Currently, the majority of cloud providers offer this option as a cheaper solution for their clients. However, little research has been done on the practical usage of these CPU-limited instances. In this paper, we present a novel autoscaling solution that uses burstable instances along with regular instances to handle the queueing arising in traffic and flash crowds. We design BIAS Autoscaler, a state-of-the-art framework that leverages burstable and regular instances for cost-efficient autoscaling and evaluate it on the Google Cloud Platform. We apply our framework to a real-world microservice workload, and conduct extensive experimental evaluations using Google Compute Engines. Experimental results show that BIAS Autoscaler can reduce the overall cost up to 25% and increase resource efficiency by 42% while maintaining the same service quality observed when using conventional instances only.
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 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".