Title
A Low-Cost Multi-Failure Resilient Replication Scheme for High-Data Availability in Cloud Storage
Abstract
Data availability is one of the most important performance factors in cloud storage systems. To enhance data availability, replication is a common approach to handle the machine failures. However, previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures, especially while increasing the data availability with limited resources. The schemes for correlated machine failures must create a constant number of replicas for each data object, which often neglects diverse data popularities and does not utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing costs caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures, and therefore minimize the cost caused by replication. Based on the model’s solution for the replication degree of each data object, we propose a low-cost multi-failure (correlated and non-correlated machine failures) resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 demonstrate that MRR achieves high data availability, low data loss probability and low consistency maintenance and storage costs when compared to previous replication schemes.
Year
DOI
Venue
2021
10.1109/TNET.2020.3027814
IEEE/ACM Transactions on Networking
Keywords
DocType
Volume
Cloud storage,replication,data availability,cost-effectiveness
Journal
29
Issue
ISSN
Citations 
4
1063-6692
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Jinwei Liu1147.78
Haiying Shen211.70
Hongmei Chi37926.07
Husnu S. Narman4347.33
Yongyi Yang510.35
Long Cheng69116.99
Wingyan Chung777055.10