Availability Enhancement of Riak TS Using Resource-Aware Mechanism

Abstract

The dependability and elasticity of various NoSQL stores in critical application are still worth studying. Currently, the cluster and backup technologies are commonly used for improving NoSQL availability, but these approaches do not consider the availability reduction when NoSQL stores encounter performance bottlenecks. In order to enhance the availability of Riak TS effectively, a resource-aware mechanism is proposed. Firstly, the data table is sampled according to time, the correspondence between time and data is acquired, and the real-time resource consumption is recorded by Prometheus. Based on the sampling results, the polynomial curve fitting algorithm is used to constructing prediction curve. Then the resources required for the upcoming operation are predicted by the time interval in the SQL statement, and the operation is evaluated by comparing with the remaining resources. Using the real hydrological sensor dataset as experimental data, the effectiveness of the mechanism is experimented in two aspects of sensitivity and specificity, respectively. The results show that through the availability enhancement mechanism, the average specificity is 80.55% and the sensitivity is 76.31% which use the initial sampling dataset. As training datasets increase, the specificity increases from 80.55% to 92.42%, and the sensitivity increases from 76.31% to 87.90%. Besides, the availability increases from 40.33% to 89.15% in hydrological application scenarios. Experimental results show that this resource-aware mechanism can effectively prevent potential availability problems and enhance the availability of Riak TS. Moreover, as the number of users and the size of the data collected grow, our method will become more accurate and perfect.