Managing Performance Interference in Cloud-Based Web Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Web services have increasingly begun to rely on public cloud platforms. The virtualization technologies employed by public clouds can, however, trigger contention between virtual machines (VMs) for shared physical machine resources, thereby leading to performance problems for Web services. Past studies have exploited physical-machine-level performance metrics such as clock cycles per instruction to detect such platform-induced performance interference. Unfortunately, public cloud customers do not have access to such metrics. They can only typically access VM-level metrics and application-level metrics such as transaction response times, and such metrics alone are often not useful for detecting inter-VM contention. This poses a difficult challenge to Web service operators for detecting and mitigating platform-induced performance interference issues inside the cloud. We propose a machine-learning-based interference detection technique to address this problem. The technique applies collaborative filtering to predict whether a given transaction being processed by a Web service is adversely suffering from interference. The results can be then used by a management controller to trigger remedial actions, e.g., reporting problems to the system manager or switching cloud providers. Results using a realistic Web benchmark show that the approach is effective. The most effective variant of our approach is able to detect about 96% of performance interference events with almost no false alarms. Furthermore, we show that a load redistribution technique that exploits the information from our detection technique is able to more effectively mitigate the interference than techniques that are interference agnostic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it