Data Consistency Simulation Tool for NoSQL Database Systems

Various data consistency levels have an important part in the integrity of data and also affect performance especially the data that is replicated many times across or over the cluster. Based on BASE and the theorem of CAP tradeoffs, most systems of NoSQL have more relaxed consistency guarantees than another kind of databases which implement ACID. Most systems of NoSQL gave different methods to adjust a required level of consistency to ensure the minimal numbering of the replicas accepted in each operation. Simulations are always depending on a simplified model and ignore many details and facts about the real system. Therefore, a simulation can only work as an estimation or an explanation vehicle for observed behavior. So to create simulation tool, I have to characterize a model, identify influence factors and simply implement that depending on a (modeled) workload. In this paper, I have a model of simulation to measure the consistency of the data and to detect the data consistency violations in simulated network partition settings. So workloads are needed with the set of users who make requests and then put the results for analysis.

[1]  Werner Vogels,et al.  Eventually consistent , 2008, CACM.

[2]  David Bermbach,et al.  Eventual consistency: How soon is eventual? An evaluation of Amazon S3's consistency behavior , 2011, MW4SOC '11.

[3]  Andrew S. Tanenbaum,et al.  Distributed systems: Principles and Paradigms , 2001 .

[4]  Wilson C. Hsieh,et al.  Bigtable: A Distributed Storage System for Structured Data , 2006, TOCS.

[5]  Lorenzo Alvisi,et al.  Consistency , Availability , and Convergence , 2011 .

[6]  Martin Fowler,et al.  NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence , 2012 .

[7]  Yawei Li,et al.  Megastore: Providing Scalable, Highly Available Storage for Interactive Services , 2011, CIDR.

[8]  Marvin Theimer,et al.  Session guarantees for weakly consistent replicated data , 1994, Proceedings of 3rd International Conference on Parallel and Distributed Information Systems.

[9]  Ion Stoica,et al.  Probabilistically Bounded Staleness for Practical Partial Quorums , 2012, Proc. VLDB Endow..

[10]  Kevin Lee,et al.  Data Consistency Properties and the Trade-offs in Commercial Cloud Storage: the Consumers' Perspective , 2011, CIDR.

[11]  Tim Kraska,et al.  MDCC: multi-data center consistency , 2012, EuroSys '13.

[12]  Dan Pritchett,et al.  BASE: An Acid Alternative , 2008, ACM Queue.

[13]  Werner Vogels,et al.  Dynamo: amazon's highly available key-value store , 2007, SOSP.

[14]  Daniel J. Abadi,et al.  Consistency Tradeoffs in Modern Distributed Database System Design: CAP is Only Part of the Story , 2012, Computer.

[15]  Dan Sullivan NoSQL for Mere Mortals , 2015 .

[16]  Prashant Malik,et al.  Cassandra: a decentralized structured storage system , 2010, OPSR.

[17]  Ali Ghodsi,et al.  Bolt-on causal consistency , 2013, SIGMOD '13.