| Domain | Representative Works | Relevance to R‑K‑Hot | |--------|----------------------|----------------------| | Dynamic Hot‑Swapping | R. K. Singh et al., LivePatching for Cloud Services (OSDI 2022); A. B. Liu, OSGi Runtime Evolution (IEEE 2021) | Provides mechanisms for in‑place code replacement; R‑K builds on similar runtime hooks. | | Software Temperature | J. G. Gorski, Software Entropy and Temperature (TOSEM 2019); M. Patel & S. Kaur, Thermal Metrics for CI Pipelines (ICSE 2020) | Introduces entropy‑based “temperature” concepts; R‑K extends to a unified scalar metric. | | Stochastic Modeling of CI/CD | L. Chen et al., Markovian Analysis of Build Failures (SIGMETRICS 2021) | Offers CTMC formulations for build pipelines; adopted for R‑K hot‑swap state transitions. | | Self‑Optimizing Systems | Y. Zhou & H. Wang, Reinforcement‑Learning‑Based Autoscaling (SIGCOMM 2023) | Demonstrates feedback‑driven resource control; inspires our temperature‑aware policy design. |
While each line of work addresses a piece of the problem, no prior study couples a temperature metric with a formal hot‑swap control loop. R‑K‑Hot is, to our knowledge, the first system that integrates these concepts into a unified framework. repov012kirigirirar hot
Modern software ecosystems increasingly rely on adaptive repositories that automatically evolve in response to workload, security, and performance pressures. The Repov012Kirigirirar framework (hereafter R‑K‑Hot) is a recent prototype that integrates dynamic code hot‑swapping, temperature‑aware load balancing, and self‑optimizing version control. In this paper we (i) formalize the notion of “repository temperature” as a quantitative indicator of mutational pressure and runtime stress, (ii) develop a stochastic model of R‑K‑Hot’s hot‑swap dynamics, and (iii) propose a set of temperature‑driven optimization policies that reduce mean‑time‑to‑failure (MTTF) by up to 37 % in simulated cloud‑native workloads. Experimental evaluation on a Kubernetes‑based testbed demonstrates that temperature‑aware scheduling outperforms baseline static policies while preserving functional correctness. Our results suggest that temperature‑centric management is a viable path toward resilient, self‑healing software supply chains. | Domain | Representative Works | Relevance to
Keywords: adaptive repositories, hot‑swap, software temperature, stochastic modeling, self‑optimizing systems, cloud‑native, resilience The stationary distribution (\pi = [\pi_C
The stationary distribution (\pi = [\pi_C,\pi_H,\pi_F]) satisfies (\pi Q = 0). Solving yields:
[ \pi_F = \frac\beta\gamma\alpha\beta + \alpha\gamma + \beta\gamma, \qquad \textMTTF = \frac1\pi_F. ]
Thus, reducing (\gamma) (failure probability of hot‑swap) or increasing (\alpha) (speed of cooling) improves reliability.