Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment

Fault Tolerance (FT) is one of the cloud's very critical problems for providing security assistance. Due to the diverse service architecture, detailed architectures & multiple interrelationships that occur in the cloud, implementation is complicated. A few other previous studies attempt to integrate the different fault tolerance frameworks and solutions suggested for the cloud environment, but some accounts occur to be constrained. This research article provides a detailed survey of the state-of-the-art research on emerging methods of fault tolerance for Cloud Computing & categorizes techniques of fault tolerance into three categories: Reactive Methods, Proactive Methods & Resilient Methods. Reactive Methods allow the system to reach a defect condition but instead attempt to get the device back up. Proactive Methods help to prevent the device from reaching a defective condition by introducing actions to minimize defects before impacting the device. On either side, newly developed Resilient Methods strive to reduce the amount of time a device takes to find from a malfunction. In the Resilient Methods context, Machine Learning and Artificial Intelligence played an important role in mapping the recovery period to a task to be configured. This survey offers a comprehensive and detailed description of the various faults kinds, factors, & different methods to fault tolerance used in the cloud. In light of their Basic Methods & some other specific characteristics, & also offers a Clear study of different tolerance mechanisms for failures & provides a comparative study of the structures under the article. It is noted that approaches of fault tolerance directed to checkpoint restart and replication are mainly included to address the crash faults in the cloud. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This research article provides a detailed survey of emerging fault tolerance methods for Cloud Computing, categorizing them into Reactive Methods, Proactive Methods, and Resilient Methods. Each category focuses on different approaches to deal with system faults, with Resilient Methods aiming to reduce recovery time from malfunctions by utilizing Machine Learning and Artificial Intelligence.