A Critical Review of Faults in Cloud Computing: Types, Detection, and Mitigation Schemes

The continuous rise in for demand services in large-scale distributed systems led to the development of cloud Computing (CC). Because it provides a combination of various software resources, CC is considered dynamically scalable. However, due to the cloud’s dynamic environment, a variety of unanticipated problems and faults occur that hinder CC performance. Fault tolerance refers to a platform’s capacity to respond smoothly to unanticipated hardware or programming failure. Failure must be analyzed and dealt with efficiently in cloud computing in order to accomplish high accuracy and reliability. Over the years, a significant number of techniques and approaches have been proposed for detecting the faults in CC as well as increasing their tolerance ability. In this review paper, we first provided a brief overview of Cloud computing systems, their architecture, and their working mechanism. Moreover, the services provided by Cloud computing and the issues faced by it are also highlighted in this paper. Also, the taxonomy of various faults that occur in the CC environment along with their mitigation techniques is discussed. Furthermore, it has been analyzed that traditional fault detection methods were not generating effective results which resulted in poor performance in cloud environments. Therefore, an ample number of authors stated to use Machine Learning (ML) based models for fault detection in CC. Nonetheless, ML algorithms were not able to handle a large volume of data therefore the concept of Deep Learning was introduced in fault detection approaches. Moreover, it has been also observed that the performance of DL methods can be enhanced significantly by using optimization algorithms along with them. Some of the recently proposed fault detection and tolerant systems based on ML, DL and optimization have been reviewed in this paper.