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Data replication schemes in cloud computing: a survey

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Abstract

In recent years, cloud storage systems have emerged as a promising technology for storing data blocks on various cloud servers. One of the main mechanisms in cloud storage systems is data replication, for which various solutions are proposed. Data replication's main target is achieving higher performance for data-intensive applications by addressing some critical challenges of this criterion, such as availability, reliability, security, bandwidth, and response time of data access. However, to the best of the author’s knowledge, there is no systematic, comprehensive, and complete survey in the cloud data replication despite its impacts and maturity. This paper presents a comprehensive survey and classification of state-of-the-art data replication schemes among different existing cloud computing solutions in the form of a classical classification to define current schemes on the topic and present open issues. The presented classification comprises three main classes; data deduplication schemes, data auditing schemes, and data handling schemes. A complete comparative comparison of the replication schemes highlights their main properties, such as utilized classes, type of the scheme, the place of implementation, evaluation tools, and their advantages and weaknesses. Finally, open issues and future uncovered or weakly covered research challenges are discussed, and the survey will be concluded.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Ali Shakarami, Mostafa Ghobaei-Arani & Hamid Shakarami

  2. Department of Computing and IT, Global College of Engineering and Technology, Muscat, Oman

    Ali Shahidinejad

  3. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Mohammad Masdari

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  1. Ali Shakarami
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Appendix 1: Data-driven topic detection for replication schemes in cloud computing topics

Appendix 1: Data-driven topic detection for replication schemes in cloud computing topics

In this appendix, extracting frequently occurring topics from the set of selected papers utilizing a replication schemes is explained. To do so, non-negative matrix factorization (NMF) is utilized for topic modeling [149]. The main reason of selecting this approach is its lack of requiring critical parameters making it more encouraging for such applications [150]. NMF factorizes matrix X (n × m) that can be roughly rewritten by UV such that both U (n × k) and V (k × m) are non-negative.

In this study, we have used the selected research articles from scientific journals and conferences in order to detect topics related to replication schemes in cloud computing. Abstracts and keywords of the selected review papers are extracted from Scopus database [151]. We consider the title, abstract, and keyword of each article as a single document. Then, stop-word removing, punctuation removing, and remove words with a frequency less than two are done in pre-processing step, and then, as a result, a frequency matrix with TFIDF (i.e., matrix X which contains words weight for documents) was composed.

Regarding \({X}_{m.n}\cong {U}_{m.k}\times {V}_{k.n}\), we can assume X consists of k different clusters such that each row of U will represents one research paper (i.e. one document) and each element in the row represent the degree of each topic in the paper. On the other side of the multiplication, each row of V represents a topic and each, element of the row represents the degree of each term in the topic.

Applying this approach, we could easily detect the best topics with normalizing the values and thresholds. We did a number of experimental results over different number of clusters (k = 3, 4, 5, 6, 7, 8, 9, 10) and thresholds for values of V and U matrices (0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5). All the achieved results are manually checked with a subset of our papers (i.e., 20 papers are randomly chosen from the paper list) in order to verify and determine the best-achieved results. Finally, we informed that three clusters and 0.2 thresholds performed the best results regarding the classification accuracy. These three detected topics include data deduplication, data auditing, and replication handling. Therefore, these three topics are considered as the main categories of replication schemes.

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Shakarami, A., Ghobaei-Arani, M., Shahidinejad, A. et al. Data replication schemes in cloud computing: a survey. Cluster Comput 24, 2545–2579 (2021). https://doi.org/10.1007/s10586-021-03283-7

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