Quantifying Eventual Consistency with PBS

Data replication results in a fundamental trade-off between operation latency and consistency. At the weak end of the spectrum of possible consistency models is eventual consistency, which provides no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough ” for practitioners given its latency and availability benefits. In this work, we explain this phenomenon and demonstrate that, despite their weak guarantees, eventually consistent systems regularly return consistent data while providing lower latency than their strongly consistent counterparts. To quantify the behavior of eventually consistent stores, we introduce Probabilistically Bounded Staleness (PBS), a consistency model that provides expected bounds on data staleness with respect to both versions and wall clock time. We derive a closed-form solution for version-based staleness and model real-time staleness for a large class of quorum replicated, Dynamo-style stores. Using PBS, we measure the trade-off between latency and consistency for partial, non-overlapping quorum systems under Internet production workloads. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering large latency benefits. 1

Estimating data divergence in cloud computing storage systems

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaMany internet services are provided through cloud computing infrastructures that are composed of multiple data centers. To provide high availability and low latency, data is replicated in machines in different data centers, which introduces the complexity of guaranteeing that clients view data consistently. Data stores often opt for a relaxed approach to replication, guaranteeing only eventual consistency, since it improves latency of operations. However, this may lead to replicas having different values for the same data. One solution to control the divergence of data in eventually consistent systems is the usage of metrics that measure how stale data is for a replica. In the past, several algorithms have been proposed to estimate the value of these metrics in a deterministic way. An alternative solution is to rely on probabilistic metrics that estimate divergence with a certain degree of certainty. This relaxes the need to contact all replicas while still providing a relatively accurate measurement. In this work we designed and implemented a solution to estimate the divergence of data in eventually consistent data stores, that scale to many replicas by allowing clientside caching. Measuring the divergence when there is a large number of clients calls for the development of new algorithms that provide probabilistic guarantees. Additionally, unlike previous works, we intend to focus on measuring the divergence relative to a state that can lead to the violation of application invariants.Partially funded by project PTDC/EIA EIA/108963/2008 and by an ERC Starting Grant, Agreement Number 30773