Abstract
<p><em>Advancement in electronics and hardware has resulted in multiple softwares running on the same hardware. The result is multiuser, multitasking and virtualized environments. However, reliability of such high performance computing systems depends both on hardware and software. For hardware, aging can be dealt with replacement. But, software aging needs to be dealt with software only. For aging detection, a new approach using machine learning framework has been proposed in this paper. For rejuvenation, Adaptive Genetic Algorithm (A-GA) has been developed 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.</em></p>
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