Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments

• Published in: Journal of computer science and technology Vol. 27; no. 2; pp. 256 - 272
• Main Author: 孙大为 常桂然 高尚 靳立忠 王兴伟
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.03.2012; Springer Nature B.V; School of Information Science and Engineering,Northeastern University,Shenyang 110819,China%Computing Center,Northeastern University,Shenyang 110819,China%School of Engineering and Information Technology,Deakin University,Geelong,Victoria 3217,Australia
• Subjects: Algorithms; Analysis; Artificial Intelligence; Availability; Cloud computing; Computer centers; Computer networks; Computer Science; Consumption; Data replication; Data Structures and Information Theory; Distributed processing; Dynamical systems; Dynamics; Fault tolerance; Information Systems Applications (incl.Internet); Internet; Modelling; Popularity; Regular Paper; Replication; Science; Software; Software Engineering; Strategy; Studies; System theory; Theory of Computation; 分布式计算环境; 动态数据复制; 可用性; 复制策略; 建模系统; 环境建模; 站点访问; 算法设计; system availability; temporal locality; replication perspective; high fault tolerance; cloud computing
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-012-1221-4

Failures are normal rather than exceptional in the cloud computing environments. To improve system avai- lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes： 1） analyzing and modeling the relationship between system availability and the number of replicas; 2） evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3） calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4） designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.