Abstract
Data replication can improve the performance and availability for applications, and when it is employed by big data applications, it has to solve the challenges posed by big data applications, i.e., offering scalability and varying consistency levels. In this paper, we design and implement a data replication framework Pyxis+, whereby replication-aware applications can be developed in a rapid and convenient way. Pyxis+ allows the applications to register different consistency levels and automatically switches the consistency levels according to the change of requirements and performance. Meanwhile, on the basis of the consistency guarantees, Pyxis+ takes advantage of the consistent hashing technology to improve the scalability of data access. Simulation experimental results show that Pyxis+ can obtain relatively stable throughputs and response time by adding or removing replica managers while facing the increase of user requests.
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Yang, Y., Jin, B., Li, S. (2013). Pyxis+: A Scalable and Adaptive Data Replication Framework. In: Yoshida, T., Kou, G., Skowron, A., Cao, J., Hacid, H., Zhong, N. (eds) Active Media Technology. AMT 2013. Lecture Notes in Computer Science, vol 8210. Springer, Cham. https://doi.org/10.1007/978-3-319-02750-0_43
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DOI: https://doi.org/10.1007/978-3-319-02750-0_43
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