Performance of Virtual Machines Under Networked Denial of Service Attacks: Experiments and Analysis
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
The use of virtual machines (VMs) to provide computational infrastructure and services to organizations is increasingly prevalent in the modern IT industry. The growing use of this technology has been driven by a desire to increase utilization of resources through server consolidation. Virtualization has also made the dream of such utility computing platforms as cloud computing a reality. Today, virtualization technologies can be found in almost every data center. However, it remains unknown whether the VMs are more vulnerable on external malicious attacks. If so, to what extent their performance degrades, and which virtualization technique has the closest to native performance? To this end, we devised a representative set of experiments to examine the performance of most typical virtualization techniques under typical denial-of-service (DoS) attacks. We show that, on a DoS attack, the performance of a web server hosted in a VM can degrade by up to 23%, while that of a nonvirtualized server hosted on the same hardware degrades by only 8%. Even with relatively light attacks, the file system and memory access performance of hypervisor-based virtualization degrades at a much higher rate than their nonvirtualized counterparts. We further examine the root causes of such degradation and our results shed new lights in enhancing the robustness and security of modern virtualization systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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