Optimum Software Aging Prediction and Rejuvenation Model for Virtualized Environment

Advancement in electronics and hardware has resulted in multiple softwares running on the same hardware. The result is multiuser, multitasking, multithreaded and virtualized environments. However, reliability of such high performance computing system depends both on hardware and software. For hardware, aging can be dealt with replacement. But, software aging needs to be dealt with different techniques. For software aging detection, a new approach using machine learning framework is proposed in this paper. For rejuvenation, the proposed solution uses Adaptive Genetic Algorithm (A-GA) to perform live migration to avoid downtime and SLA violation. The proposed A-GA based rejuvenation controller (A-GARC) has outperformed other heuristic techniques such as Ant Colony Optimization (ACO) and best fit decreasing (BFD) for migration. Results reveal that the proposed aging forecasting method and A-GA based rejuvenation outperforms other approaches to ensure optimal system availability, minimum task migration, performance degradation and SLA violation.