Toward a Smart Cloud: A Review of Fault-Tolerance Methods in Cloud Systems

This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance methods proposed for cloud computing. The survey classifies fault-tolerance methods into three categories: 1) ReActive Methods (RAMs); 2) PRoactive Methods (PRMs); and 3) ReSilient Methods (RSMs). RAMs allow the system to enter into a fault status and then try to recover the system. PRMs tend to prevent the system from entering a fault status by implementing mechanisms that enable them to avoid errors before they affect the system. On the other hand, recently emerging RSMs aim to minimize the amount of time it takes for a system to recover from a fault. Machine Learning and Artificial Intelligence have played an active role in RSM domain in such a way that the recovery time is mapped to a function to be optimized (i.e., by converging the recovery time to a fraction of milliseconds). As the system learns to deal with new faults, the recovery time will become shorter. In addition, current issues and challenges in cloud fault tolerance are also discussed to identify promising areas for future research.

Automated summary

This paper provides a comprehensive survey of fault tolerance methods for cloud computing, including ReActive Methods (RAMs), PRoactive Methods (PRMs), and ReSilient Methods (RSMs). Machine Learning and Artificial Intelligence have played a significant role in optimizing recovery time in RSM domain. Current issues and challenges in cloud fault tolerance are also discussed to identify potential areas for future research.