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Adaptive measurement method for data popularity in distributed systems
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DOI：10.1007/s10586-016-0637-y.png)
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Distributed systems provide geographically distributed resources for large-scale applications while managing large volumes of data. In this context, replication of data in several sites of the system is an effective solution for achieving interesting performances. A number of data replication strategies have been proposed in the literature. Data popularity is one of the most important parameters taken into consideration by these strategies. It analyzes the historic of the data access pattern, and provides predictions for future data requests. However, measuring data popularity is a challenging task because there are several factors that contribute to the evaluation of data popularity. In this paper, a new adaptive measurement for data popularity in distributed systems is proposed. The proposed measurement covers all factors taken into consideration by previous work of the literature. It also takes into consideration new factors to deal with the dynamic nature of the system so it can adapt to any access pattern. We show that the exploitation of our measurement improves the performances of replication strategies, while offering the possibility to use the data popularity parameter in new contexts in replication management.
Keyword:
Distributed system
Replication strategy
Data popularity
Access pattern
Temporal locality
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