Subscriber-Driven Interference Detection for Cloud-Based Web Services
Bibliographic record
Abstract
Web services are now increasingly being hosted on public cloud infrastructure as a service platforms such as the Amazon Web service elastic compute cloud (EC2). However, previous studies have shown that the virtualized infrastructure used in public clouds can introduce contention among virtual machines (VMs) for shared physical host resources eventually leading to performance problems. Subscribers in a public cloud platform typically do not have access to metrics that can directly quantify the adverse impact of such inter-VM interference on Web service response times. We present a software probe based system to address this limitation. The probe is a lightweight application that runs on each Web service VM that needs to be monitored. We periodically measure the probe's response time on a monitored VM. We then compare this response time with the probe's previously recorded baseline no-interference response time when it executes in isolation on a VM of the same type. Statistically significant increase in the probe's response time from the baseline is used to detect interference. The probe also indicates the type of contention at the physical host that causes the interference. This information can be exploited by a subscriber to mitigate the problem. Results show that our approach is quite effective over two different cloud platforms and a wide variety of workload scenarios. In particular, results indicate that Web service instances hosted on EC2 suffer from interference. Our probe was able to detect 93% of performance degradations triggered by such interference. In all these cases, the probe imposed an average overhead of only 3%-4% on the mean response time of the Web service being monitored.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".