An Intelligent System for Energy-Aware Flexible Fault Tolerance in Cloud

Fault tolerance in cloud computing environments has matured significantly, and most cloud service providers now offer highly available and reliable services. However, such high levels of fault tolerance are often achieved through resource replication, which consequently increases overall energy consumption. Existing research primarily relies on conventional energy management techniques when designing fault-tolerant cloud systems. With recent advancements in artificial intelligence, there is considerable potential to improve energy management beyond traditional approaches. Motivated by this opportunity, this study presents an intelligent system for energy-aware and flexible fault tolerance in cloud environments. The proposed system employs an intelligent resource group formation algorithm that selects virtual machines based on both their energy consumption and reliability characteristics. Furthermore, by dynamically selecting appropriate fault tolerance mechanisms, the system adapts to changing workload conditions and system states. The proposed approach is evaluated through extensive simulation experiments. The results indicate that the system completes more than 96% of tasks within their specified deadlines while maintaining energy-efficient operation. Consequently, the proposed solution effectively balances the trade-off between energy efficiency and service reliability, making it a practical and sustainable approach for modern cloud computing environments.

@article{Hasan2026,
  title     = {An Intelligent System for Energy-Aware Flexible Fault Tolerance in Cloud},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Moin Hasan and Shailee Lohmor Choudhary and Rinku Sharma Dixit and Sujith Jayaprakash},
  doi       = {10.14569/IJACSA.2026.0170730},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170730}
}