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Neoloader 0.523/8/2023 ![]() 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. ![]() 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 adaptive consistency guarantees are implemented with a consistency tuner, which probes the consistency index of an observed replicated data object in an online application. This work extends the causal relationships presented in our earlier work and presents adaptive consistency guarantees of this consistency model. 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. The consistency guarantees of data attributes or objects are measured using an application independent metric called consistency index (CI). Selective data consistency model applies strict consistency to a subset of data objects. 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. ![]() ![]() Tunable consistency guarantees in big data stores help in achieving optimized consistency guarantees with improved performance. We aim at presenting a more comprehensive and empirical study in the future. The paper concisely explains the protocol and discusses its feasibility. To respect the essence of EC, the protocol gives priority to high availability, and thus Byzantine detection is performed off the critical path on a consistent data offset. In this work in progress paper, we introduce a new secure highly available protocol for the EC model that assumes a fraction of replicas and any client can be Byzantine. Classical BFT state machine replication protocols cannot solve this problem due to the blocking nature of consensus, something at odd with the availability via replica divergence in the EC model. However, a Byzantine replica (i.e., arbitrary or malicious) can hamper the eventual convergence of replicas to a global consistent state, thus compromising the entire service. Although, this model has shown to be promising and greatly adopted by industry, the state of the art only assumes that replicas can crash and recover. A survey of this kind not only provides the user/researcher with a comparative performance analysis of the approaches but also clarifies the suitability of these for candidate cloud systems.Įventual consistency (EC) is a relaxed data consistency model that, driven by the CAP theorem, trades prompt consistency for high availability. This paper will establish a set of comparative criteria and then make a comparative analysis of existing adaptive consistency approaches. Although there is a rich literature on adaptive consistency approaches in cloud storage, there is a need to classify as well as regroup the approaches based on their strategies. Therefore, an adaptive consistency strategy is needed to tune, during runtime, the consistency level depending on the criticality of the requests or data items. In contrast, weaker consistency enhances availability and performance but increases data staleness. In this context, strong consistency guarantees data freshness but affects directly the performance and availability of the system. Hence, the data consistency is a major challenge in distributed systems. High data availability is achieved by replicating data in different servers in order to reduce access time lag, network bandwidth consumption and system unreliability. NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases due to their high security and availability with a low cost.
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