Model-driven Elasticity and DoS Attack Mitigation in Cloud Environments
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
Workloads for web applications can change rapidly. When the change is an increase in customers, a common adaptive approach to maintain SLAs is elasticity, the on-demand allocation of computing resources. However, application-level denial-of-service (DoS) attacks can also cause changes in workload, and require an entirely different response. These two issues are often addressed separately (in both research and application). This paper presents a model-driven adaptive management mechanism which can correctly scale a web application, mitigate a DoS attack, or both, based on an assessment of the business value of workload. This approach is enabled by modifying a layered queuing network model previously used to model data centers to also accurately predict short-term cloud behavior, despite cloud variability over time. We evaluate our approach on Amazon EC2 and demonstrate the ability to horizontally scale a sample web application in response to an increase in legitimate traffic while mitigating multiple DoS attacks, achieving the established performance goal.
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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 | 0.000 | 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 | 0.000 | 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