Modeling a Load-adaptive Data Replication in Cloud Environments

Julia Myint, Axel Hunger

2013

Abstract

Replication is an essential cornerstone of cloud storage where 24x7 availability is needed. Failures are normal rather than exceptional in the cloud computing environments. Aiming to provide high reliability and cost effective storage, replicating based on data popularity is an advisable choice. Before committing a service level agreement (SLA) to the customers of a cloud, the service provider needs to carry out analysis of the system on which cloud storage is hosted. Hadoop Distributed File System (HDFS) is an open source storage platform and designed to be deployed in low-cost hardware. PC Cluster based Cloud Storage System is implemented with HDFS by enhancing replication management scheme. Data objects are distributed and replicated in a cluster of commodity nodes located in the cloud. In this paper, we propose a Markov chain model for replication system which is able to adapt the load changes of cloud storage. According to the performance evaluation, the system can be able to adapt the different workloads (i.e data access rates) while maintaining the high reliability and long mean time to absorption.

References

  1. Borthakur, D., 2007, “The Hadoop Distributed File System: Architecture and Design”. The Apache Software Foundation.
  2. Chuob, S. and et.al., 2011, “Modeling and Analysis of Cloud Computing Availability based on Eucalyptus Platform for E-Government Data Center”, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.
  3. Cooper, B. F., Ramakrishnan, R., Srivastava, U., Silberstein , A., Bohannon, P., and et.al., 2008, “Pnuts: Yahoo!'s Hosted Data Serving Platform”,In VLDB.
  4. Cristina, L. and et. al., 2012 “A strong-Centric Analysis of MapReduce Workloads: File Popularity, Temporal Locality and Arrival Patterns”, In Proc. IEEE IISWC.
  5. Decandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P. and Vogels, W., 2007,“Dynamo: Amazon's Highly Available Key-value Store”, In SOSP.
  6. Epstein, R., 1977, The Theory of Gambling and Statistical Logic. Academic Press.
  7. Feller, W., 1968, An Introduction to Probability Theory and Its Applications. John Wiley and Sons.
  8. Ghemawat, S., Gobioff, H., Leung, S. T., October, 2003, “The Google File System”, Proceedings of 19th ACM Symposium on Operating Systems Principles(SOSP 2003), New York USA.
  9. Jagadish, H. V., Ooi, B. C., and Vu, Q. H., 2005, “BATON: A Balanced Tree Structure for Peer-to-Peer Networks”. In VLDB.
  10. Lakshman, Malik, P., April 2010, “Cassandra - A Decentralized Structured Storage System”, ACM SIGOPS Operating Systems Review, Volume 44 Issue 2.
  11. Longo, F., Ghosh, R., Naik, V. K. and Trivedi, K. S., 2011 “A Scalable Availability Model for Infrastructure-asa-Service Cloud”, DSN.
  12. Ramabhadran, S. and Pasquale, J., 2006, “Analysis of Long-Running Replicated Systems”, In the Proceedings of 25th IEEE International Conference on Computer Communications, pp. 1--9.
  13. Sun, D. W. and et.al., Mar. 2012,”Modeling a Dyanmic Data Replicatin Strategy to Increase System Availability in Cloud Computing Environments“, Journal of Computer Science and Technology 27(2):256-272. DOI 10.1007/s11390-012-1221-4.
  14. Trivedi, K. S. and Sahner R., March 2009, “SHARPE at the age of twenty two,” ACM Sigmetrics Performance Evaluation Review, vol . 36, no. 4, pp. 52-57.
  15. Vo, H. T., Chen, C., Oo, B. C., September 13-17 2010, “Towards Elastic Transactional Cloud Storage with Range Query Support”, International Conference on Very Large Data Bases , Singapore.
  16. Wei, Q. and et. al., 2010 “CDRM: A Cost-effective Dynamic Replication Management Scheme for Cloud Storage Cluster”, IEEE International Conference on Cluster Computing.
  17. Ye, Y. and et. al., 2010 “Cloud Storage Design Based on Hybrid of Replication and Data Partitioning”, 16th International Conference on Parallel and Distributed Systems.
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Paper Citation


in Harvard Style

Myint J. and Hunger A. (2013). Modeling a Load-adaptive Data Replication in Cloud Environments . In Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-8565-52-5, pages 511-514. DOI: 10.5220/0004374805110514


in Bibtex Style

@conference{closer13,
author={Julia Myint and Axel Hunger},
title={Modeling a Load-adaptive Data Replication in Cloud Environments},
booktitle={Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2013},
pages={511-514},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004374805110514},
isbn={978-989-8565-52-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Modeling a Load-adaptive Data Replication in Cloud Environments
SN - 978-989-8565-52-5
AU - Myint J.
AU - Hunger A.
PY - 2013
SP - 511
EP - 514
DO - 10.5220/0004374805110514