Performance of Virtual Machines Under Networked Denial of Service Attacks: Experiments and Analysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".