Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm

D. Rambabu*
Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.
Periodicity:January - June'2022
DOI : https://doi.org/10.26634/jcc.9.1.18566

Abstract

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.

Keywords

Cloud Computing, Replication, Data Mining, Quality of Service.

How to Cite this Article?

Rambabu, D. (2022). Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm. i-manager’s Journal on Cloud Computing, 9(1), 17-25. https://doi.org/10.26634/jcc.9.1.18566

References

[1]. Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., & Zomaya, A. Y. (2013). Energy-efficient data replication in cloud computing datacenters. In Globecom 2013 Workshop-Cloud Computing Systems, Networks, and Applications, 445-450.
[2]. Cheng, C. W., Wu, J. J., & Liu, P. (2009). QoS-aware, access-efficient, and storage-efficient replica placement in grid environments. The Journal of Supercomputing, 49(1), 42-63. https://doi.org/10.1007/s11227-008-0221-1
[3]. Dabrowski, C. (2009). Reliability in grid computing systems. Concurrency and Computation: Practice and Experience, 21(8), 927-959. https://doi.org/10.1002/cpe.1410
[4]. Dong, X., El-Gorashi, T., & Elmirghani, J. M. (2011). Green IP over WDM networks with data centers. Journal of Lightwave Technology, 29(12), 1861-1880. https://doi.org/10.1109/JLT.2011.2148093
[5]. Gill, N. K., & Singh, S. (2016). A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers. Future Generation Computer Systems, 65, 10-32. https://doi.org/10.1016/j.future.2016.05.016
[6]. Horri, A., Sepahvand, R., &Dastghaibyfard, G. H. (2008). A hierarchical scheduling and replication strategy. IJCSNS International Journal of Computer Science and Network Security, 8(8), 30-35.
[7]. Huang, C. Q., Li, Y., Wu, H. Y., Tang, Y., &Luo, X. (2014). Modeling and maintaining the reliability of data replica ser vice in cloud storage systems. Journal of Communication, 35(10), 89-97. https://doi.org/10.3969/j.issn.1000-436x.2014.10.011
[8]. Lin, J. W., Chen, C. H., & Chang, J. M. (2013). QoSaware data replication for data-intensive applications in cloud computing systems. IEEE Transactions on Cloud Computing, 1(1), 101-115. https://doi.org/10.1109/TCC.2013.1
[9]. Liu, G., Shen, H., & Chandler, H. (2016). Selective data replication for online social networks with distributed datacenters. IEEE Transactions on Parallel and Distributed Systems, 27(8), 2377-2393. https://doi.org/10.1109/TPDS.2015.2485266
[10]. Long, S. Q., Zhao, Y. L., & Chen, W. (2014). MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster. Journal of Systems Architecture, 60(2), 234-244. https://doi.org/10.1016/j.sysarc.2013.11.012
[11]. Mansouri, N., & Javidi, M. M. (2018). A new prefetching-aware data replication to decrease access latency in cloud environment. Journal of Systems and Software, 144, 197-215. https://doi.org/10.1016/j.jss.2018.05.027
[12]. Mansouri, N., Rafsanjani, M. K., &Javidi, M. M. (2017). DPRS: A dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simulation Modelling Practice and Theory, 77, 177-196. https://doi.org/10.1016/j.simpat.2017.06.001
[13]. Qu, Y., & Xiong, N. (2012). RFH: A resilient, faulttolerant and high-efficient replication algorithm for distributed cloud storage. In 2012, 41st International Conference on Parallel Processing, 520-529. https://doi.org/10.1109/ICPP.2012.3
[14]. Sun, D. W., Chang, G. R., Gao, S., Jin, L. Z., & Wang, X. W. (2012). Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. Journal of Computer Science and Technology, 27(2), 256-272. https://doi.org/10.1007/s11390-012-1221-4
[15]. Sun, H., Xiao, B., Wang, X., & Liu, X. (2017). Adaptive trade off between consistency and performance in data replication. Software: Practice and Experience, 47(6), 891-906. https://doi.org/10.1002/spe.2462
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.