Estimating Response Time Percentiles of Cloud-Based Tiered Web Applications in Presence of VM Failures
Bibliographic record
Abstract
Increasingly, software applications are being deployed in clouds because cloud computing offers several advantages -- for example, it relieves the application service providers from buying and maintaining data centers thereby reducing the operational costs, it allows dynamic scaling of virtual machines as required on a pay-per-use basis, and, it promotes easy deployment in multiple geographic locations at minimal cost. A key challenge in deploying a multi-tier web application in cloud is to achieve low variability in its response time. In this paper, we analyze the behaviour of a 3-tier cloud-based web application. We propose a hierarchical model to compute the response-time distribution that considers performance degradation of the application due to VM failures. Our model applies order statistics to describe the application's availability behavior and open queueing network to describe its performance behavior. We solve the open queueing network using discrete event simulation. The results show that in a 3-tier system, a configuration with large number of virtual machines (VMs) does not necessarily perform better than a configuration with smaller number of VMs. Moreover, for a given set of performance and availability parameters, the results further show that different system configurations containing the same number of VMs yield different performance depending on the replication level of the VMs in different tiers. We demonstrate that our model can be exploited to support the selection of appropriate number of replicas for different tiers that would meet the service-level agreement specified in terms of response-time percentiles.
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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 | 0.001 | 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 | 0.001 | 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 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".