Pyxis+: A Scalable and Adaptive Data Replication Framework

  • Yuwei Yang
  • Beihong Jin
  • Sen Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


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.


Replication Framework Consistency Level Scalability 


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  1. 1.
    Eric, A.B.: Towards Robust Distributed Systems (abstract). In: 19th Annual ACM Symposium on Principles of Distributed Computing, New York, p. 7 (2000)Google Scholar
  2. 2.
    Seth, G., Nancy, L.: Brewer’s Conjecture and the Feasibility of Consistent Available Partition-tolerant Web Services. ACM SIGACT News 33, 51–59 (2002)CrossRefGoogle Scholar
  3. 3.
    Ion, S., Robert, M., David, K.: M. Frans, K., Hari, B.: Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications. In: ACM Special Interest Group on Data Communication, San Diego, pp. 149–160 (2001)Google Scholar
  4. 4.
    Giuseppe, D., Deniz, H., Madan, J., Gunavardhan, K., Avinash, L., Alex, P., Swaminathan, S., Peter, V.: Dynamo: Amazon’s Highly Available Key-value Store. In: 21st ACM SIGOPS Symposium on Operating Systems Principles, New York, pp. 205–220 (2007)Google Scholar
  5. 5.
    Brian, F.C., Raghu, R., Utkarsh, S., Adam, S., Philip, B., Hans-Arno, J., Nick, P., Daniel, W., Ramana, Y.: PNUTS: Yahoo!’s Hosted Data Serving Platform. VLDB Endowment 1, 1277–1288 (2008)Google Scholar
  6. 6.
    Wyatt, L., Michael, J.F., Michael, K., David, G.A.: Don’t Settle For Eventual: Scalable Causal Consistency for Wide-Area Storage with COPS. In: 23rd ACM Symposium on Operating Systems Principles, Cascais, pp. 401–416 (2011)Google Scholar
  7. 7.
    Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. ACM SIGOPS Operating Systems Review 44, 35–40 (2010)CrossRefGoogle Scholar
  8. 8.
    Houssem-Eddine, C., Shadi, I., Gabriel, A., Maria, S.P.: Harmony: Towards Automated Self-Adaptive Consistency in Cloud Storage. In: 2012 IEEE International Conference on Cluster Computing, Beijing, pp. 293–301 (2012)Google Scholar
  9. 9.
    Tim, K., Martin, H., Gustavo, A., Donald, K.: Consistency Rationing in the Cloud: Pay only when it matters. VLDB Endowment 2, 253–264 (2009)Google Scholar
  10. 10.
    Haifeng, Y., Amin, V.: Design and Evaluation of a Conit-Based Continuous Consistency Model for Replicated Services. ACM Transactions on Computer Systems 20, 239–282 (2002)CrossRefGoogle Scholar
  11. 11.
    Golding, R.A., Long, D.D.E.: Modeling Replica Divergence in a Weak-consistence Protocol for Global-scale Distributed Data Bases. In: Concurrent Systems Laboratory, Computer and Information Sciences (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Yuwei Yang
    • 1
    • 2
  • Beihong Jin
    • 1
    • 2
  • Sen Li
    • 1
    • 2
  1. 1.Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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