Mengchun Wu | Searcharxiv

arXiv2026

Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments.

cs.CR↗

arXiv2021

On the Determination of the Transition to Pure Reptation by Dielectric Spectroscopy

Polymer melts show a characteristic molecular weight dependent relaxation time that can be related to the unentangled and entangled regime. At high molecular weights, influence of contour length fluctuations and con-straint release cease and pure reptation prevails. With the broad frequency range dielectric spectroscopy can fol-low polymer dynamics over a very broad temperature range, with the additional advantage of recording the spec-tral shape which contains information on reptation. Here we investigate the apparent discrepancy in the molecular weight for the onset of pure reptation from the molecular weight dependence and the spectral shape. We examined the popular derivative method and compared it with a version that includes higher order terms. Higher order terms lead to a more accurate peak shape and position than those determined with the simpler version. This becomes important if experimental spectra contain conductivity and polarization contributions. Higher order terms require the introduction of an interpolating function to analyze experimental spectra, which lets the Havriliak-Negami function appear to be a more robust, yet reliable tool to determine the peak shapes. We reach the conclusion that molecular weight dependence and spectral shape can be both strongly affected by conductivity and polarization contributions. While this leaves uncertainties on the accurate value of the transition molecular weight, the peak shape points to the existence of reptation and contour length fluctuations in polyisoprene with a molecular weight greater than 1000 kg/mol, which would imply a ten times greater threshold molecular weight than expected from previous estimates using the molecular weight dependence.

cond-mat.soft↗