Cloud Server Backup Resource Allocation Models Based on Probabilistic Protection

As cloud systems scale, ensuring service availability while minimizing backup resources has become a critical challenge. A reliable method for evaluating service availability is essential for building a robust cloud infrastructure, as accurately estimating backup failure probabilities is critical for effective resource allocation and maintaining overall reliability. The previous model enumerates all the failure patterns of the primary physical machine and the backup physical machine and accumulates the backup failure probability. When the number of physical machines increases, the computational complexity grows exponentially. This paper proposes models calculating backup failure probabilities for two common cloud infrastructure architectures: bare metal and virtual machine. For the bare metal case, a binomial distribution with quadratic time complexity calculates the backup failure probability. For the virtual machine case, integrating the first-fit decreasing algorithm with probabilistic analysis partially reduces the complexity associated with backup physical machine failure patterns. Numerical results confirm that our proposed models yield failure probabilities consistent with previous model results while decreasing computational complexity, particularly as the number of physical machines increases.