Managing Performance Interference in Cloud-Based Web Services
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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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".