Understanding the impact of denial of service attacks on virtual machines
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
Virtualization, which allows multiple Virtual Machines (VMs) to reside on a single physical machine, has become an indispensable technology for today's IT infrastructure. It is known that the overhead for virtualization affects system performance; yet it remains largely unknown whether VMs are more vulnerable to networked Denial of Service (DoS) attacks than conventional physical machines. A clear understanding here is obviously critical to such networked virtualization system as cloud computing platforms. In this paper, we present an initial study on the performance of modern virtualization solutions under DoS attacks. We experiment with the full spectrum of modern virtualization techniques, from paravirtualization, hardware virtualization, to container virtualization, with a comprehensive set of benchmarks. Our results reveal severe vulnerability of modern virtualization: even with relatively light attacks, the file system and memory access performance of VMs degrades at a much higher rate than their non-virtualized counterparts, and this is particularly true for hypervisor-based solutions. We further examine the root causes, with the goal of enhancing the robustness and security of these virtualization systems. Inspired by the findings, we implement a practical modification to the VirtIO drivers in the Linux KVM package, which effectively mitigates the overhead of a DoS attack by up to 40%.
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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 | 0.001 | 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 it