Earticle

초록

영어

Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This paper represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The experimental results indicate that the proposed Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availibility in comparison with other similar approaches.

목차

Abstract
 1. Introduction
 2. Related Works
 3. The Proposed Approach
  3.1. Learning Automata
  3.2. The Proposed LARFH Algorithm
 4. Performance Evaluation
 5. Conclusion
 References

키워드

저자

• Seyyed Mansour Hosseini [ Department of Computer Engineering, Mahallat Branch, Islamic Azad University, Mahallat, Iran ]
• Mostafa Ghobaei Arani [ Department of Computer Engineering, Parand Branch, Islamic Azad University, Tehran, Iran ]
• Abdol Reza Rasouli Kenari [ Department of Computer Engineering, Qom Branch, University of Technology Qom, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605