Confidence Bounded Replica Currency Estimation

Replicas of the same data item often exhibit varying consistency levels when executing read and write requests due to system availability and network limitations. When one or more replicas respond to a query, estimating the currency (or staleness) of the returned data item (without accessing the other replicas) is essential for applications requiring timely data. Depending on how confident the estimation is, the query may dynamically decide to return the retrieved replicas, or wait for the remaining replicas to respond. The replica currency estimation is expected to be accurate and extremely time efficient without introducing large overhead during query processing. In this paper, we provide theoretical bounds on the confidence of replica currency estimation. Our system computes with a minimum probability p, whether the retrieved replicas are current or stale. Using this confidence-bounded replica currency estimation, we implement a novel DYNAMIC read consistency level in the open-source, NoSQL database, Cassandra. Experiments show that the proposed replica currency estimation is intuitive and efficient. In most tested scenarios, with various query loads and cluster configurations, we show our estimations with confidence levels of at least 0.99 while keeping query latency low (close to reading ONE replica). Moreover, the overheads introduced due to estimation scoring and training are low, incurring only 0.76% to 1.17% of the query processing and replica synchronization time costs, respectively.