Using Layered Bottlenecks for Virtual Machine Provisioning in the Clouds
Bibliographic record
Abstract
Meeting the QoS objectives of fluctuating web workload requires techniques built on performance models, controller algorithms, monitors, etc. To meet the demands, we propose a controller algorithm using performance models that addresses the dynamic provisioning problem of multi-tier web applications in the cloud computing domain through addition of resources. The proposed algorithm aims to attain response time objectives by identifying "layered bottlenecks" and on this basis adding virtual machines (VM) and virtual CPUs, while keeping a check on limits such as spare VMs, processors-per-VM and replicas-per-VM. Here, Layered Queueing Network (LQN) performance models are used, alongside jLQNInterface, a tool developed in Java that allows solving, analyzing, and manipulating LQN models through the implemented API. The algorithm has been implemented using the tool and its applicability is demonstrated through a case study. By comparing two cases, it is shown that the proposed algorithm by using layered bottlenecks results in a model that satisfies the objectives with fewer resources.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".