Tunable consistency guarantees of selective data consistency model

Tunable consistency guarantees in big data stores help in achieving optimized consistency guarantees with improved performance. Commercial data stores offer tunable consistency guarantees at transaction level where the user specifies the desired level of consistency in terms of number of participating replicas in read and write consensus. Selective data consistency model applies strict consistency to a subset of data objects. The consistency guarantees of data attributes or objects are measured using an application independent metric called consistency index (CI). Our consistency model is predictive and helps in expression of data consistency as a function of known database design parameters, like workload characteristics and number of replicas of the data object. This work extends the causal relationships presented in our earlier work and presents adaptive consistency guarantees of this consistency model. The adaptive consistency guarantees are implemented with a consistency tuner, which probes the consistency index of an observed replicated data object in an online application. The tuner uses statistically derived threshold values of an optimum time gap, which, when padded in a workload stream, guarantees a desired value of consistency index for the observed data object. The tuner thus works like a workload scheduler of the replicated data object and pads only the required time delay between the requests in such a way that desired level of consistency is achieved with minimal effect on performance metrics like response time.