Providing a new approach to increase fault tolerance in cloud computing using fuzzy logic

ABSTRACT

It is generally accepted that with the ever-increasing need of users to various resources, cloud computing is rapidly evolving as one of the new practical technologies. Cloud computing can be categorized as a computing solution in which required technology or services allow users to access computing resources on demand. Moreover, fault tolerance is one of the major concerns to ensure availability and reliability of services as well as to perform the tasks. In order to minimize the impact of failures on the system and ensure correct task execution, they must be anticipated and managed. Few newly developed methods for fault tolerance have focused on fault detection dimension. Therefore, in this paper, a detailed analysis of the nature of the error and its detection will provide as well as a fuzzy-based method to prepare an appropriate response to the error tolerance. In order to increase the error tolerance and load balancing when the error occurs, the requesting a task re-execution and migration techniques through the checkpoint are used. The migration technique overlaps with time, so check-point use can avoid re-execution as well as tasks as much as possible. The results of the experiments also indicate a mean superiority of 6.49% for accuracy criterion in comparison with ABFT method and 2.27% in comparison with FFD algorithm.

KEYWORDS:

• Fault tolerance
• cloud computing
• fuzzy logic
• reliability
• resource allocation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Amin Rezaeipanah

Amin Rezaeipanah received his B.E. in Computer Science & Engineering from Faculty of Engineering at Ferdows University, Mashhad, Iran in 2010 and M.E. in Artificial intelligence from Shiraz University, Shiraz, Iran in 2013. He is currently researcher and lecturere at Rahjuyan Danesh University, Borazjan, Iran. His main research interests consist of recommender systems, social network analysis, wireless sensor networks and large-scale data mining.

Musa Mojarad

Mousa Mojarad received her Ph.D. in Software Engineering. He is currently a member of the faculty of Islamic Azad University of Firoozabad Branch. Her favorite research areas are data mining, evolutionary algorithms, graph mining, social networking and optimization. He has 7 years of teaching experience and 5 years of Research Experience.

Ahad Fakhari

Ahad Fakhari is graduated in B.E of Computer Networking Engineering from Department of Computer Engineering and Information Technology at Lian Bushehr Institute in 2019. His research interests includes wireless sensor networks, cloud computing, computer network analysis.