Gibbon: An Availability Evaluation Framework for Distributed Databases

  • Daniel Seybold
  • Christopher B. Hauser
  • Simon Volpert
  • Jörg Domaschka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10574)


Driven by new application domains, the database management systems (DBMSs) landscape has significantly evolved from single node DBMS to distributed database management systems (DDBMSs). In parallel, cloud computing became the preferred solution to run distributed applications. Hence, modern DDBMSs are designed to run in the cloud. Yet, in distributed systems the probability of failures is the higher the more entities are involved and by using cloud resources the probability of failures increases even more. Therefore, DDBMSs apply data replication across multiple nodes to provide high availability. Yet, high availability limits consistency or partition tolerance as stated by the CAP theorem. As the decision for two of the three attributes in not binary, the heterogeneous landscape of DDBMSs gets even more complex when it comes to their high availability mechanisms. Hence, the selection of a high available DDBMS to run in the cloud becomes a very challenging task, as supportive evaluation frameworks are not yet available. In order to ease the selection and increase the trust in running DDBMSs in the cloud, we present the Gibbon framework, a novel availability evaluation framework for DDBMSs. Gibbon defines quantifiable availability metrics, a customisable evaluation methodology and a novel evaluation framework architecture. Gibbon is discussed by an availability evaluation of MongoDB, analysing the take over and recovery time.


Distributed database Database evaluation High availability NoSQL Cloud 



The research leading to these results has received funding from the EC’s Framework Programme HORIZON 2020 under grant agreement numbers 644690 (CloudSocket) and 731664 (MELODIC). We also thank Daimler TSS for the encouraging and fruitful discussions on the topic.


  1. 1.
    Abadi, D., Agrawal, R., Ailamaki, A., Balazinska, M., Bernstein, P.A., Carey, M.J., Chaudhuri, S., Chaudhuri, S., Dean, J., Doan, A., et al.: The Beckman report on database research. Commun. ACM 59(2), 92–99 (2016)CrossRefGoogle Scholar
  2. 2.
    Almeida, R., Neto, A.A., Madeira, H.: Resilience benchmarking of transactional systems: experimental study of alternative metrics. In: PRDC (2017)Google Scholar
  3. 3.
    Avizienis, A., Laprie, J.C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. TDSC 1(1), 11–33 (2004)Google Scholar
  4. 4.
    Baur, D., Seybold, D., Griesinger, F., Tsitsipas, A., Hauser, C.B., Domaschka, J.: Cloud orchestration features: are tools fit for purpose? In: UCC (2015)Google Scholar
  5. 5.
    Brewer, E.: Cap twelve years later: how the “rules” have changed. Computer 45(2), 23–29 (2012)CrossRefGoogle Scholar
  6. 6.
    Brewer, E.A.: Towards robust distributed systems. In: PODC (2000)Google Scholar
  7. 7.
    Cattell, R.: Scalable SQL and NoSQL data stores. ACM Sigmod Rec. 39(4), 12–27 (2011)CrossRefGoogle Scholar
  8. 8.
    Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: SoCC (2010)Google Scholar
  9. 9.
    DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s highly available key-value store. ACM SIGOPS Oper. Syst. Rev. 41(6), 205–220 (2007)CrossRefGoogle Scholar
  10. 10.
    Domaschka, J., Baur, D., Seybold, D., Griesinger, F.: Cloudiator: a cross-cloud, multi-tenant deployment and runtime engine. In: SummerSOC (2015)Google Scholar
  11. 11.
    Domaschka, J., Hauser, C.B., Erb, B.: Reliability and availability properties of distributed database systems. In: EDOC (2014)Google Scholar
  12. 12.
    Domaschka, J., Seybold, D., Griesinger, F., Baur, D.: Axe: a novel approach for generic, flexible, and comprehensive monitoring and adaptation of cross-cloud applications. In: ESOCC (2015)Google Scholar
  13. 13.
    Ford, D., Labelle, F., Popovici, F.I., Stokely, M., Truong, V.A., Barroso, L., Grimes, C., Quinlan, S.: Availability in globally distributed storage systems. In: OSDI (2010)Google Scholar
  14. 14.
    Geraci, A., Katki, F., McMonegal, L., Meyer, B., Lane, J., Wilson, P., Radatz, J., Yee, M., Porteous, H., Springsteel, F.: IEEE standard computer dictionary: Compilation of IEEE standard computer glossaries: 610. IEEE Press (1991)Google Scholar
  15. 15.
    Grolinger, K., Higashino, W.A., Tiwari, A., Capretz, M.A.: Data management in cloud environments: NoSQL and NewSQL data stores. In: JoCCASA (2013)Google Scholar
  16. 16.
    Gunawi, H.S., Hao, M., Suminto, R.O., Laksono, A., Satria, A.D., Adityatama, J., Eliazar, K.J.: Why does the cloud stop computing? Lessons from hundreds of service outages. In: SoCC (2016)Google Scholar
  17. 17.
    Haerder, T., Reuter, A.: Principles of transaction-oriented database recovery. In: CSUR (1983)Google Scholar
  18. 18.
    Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., Koziris, N.: On the elasticity of NoSQL databases over cloud management platforms. In: CIKM (2011)Google Scholar
  19. 19.
    Mell, P., Grance, T.: The NIST definition of cloud computing. Technical report, National Institute of Standards & Technology (2011)Google Scholar
  20. 20.
    Patil, S., Polte, M., Ren, K., Tantisiriroj, W., Xiao, L., López, J., Gibson, G., Fuchs, A., Rinaldi, B.: YCSB++: benchmarking and performance debugging advanced features in scalable table stores. In: SoCC (2011)Google Scholar
  21. 21.
    Pritchett, D.: Base: an acid alternative. Queue 6, 48–55 (2008)CrossRefGoogle Scholar
  22. 22.
    Sadalage, P.J., Fowler, M.: NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Pearson Education, London (2012)Google Scholar
  23. 23.
    Sakr, S.: Cloud-hosted databases: technologies, challenges and opportunities. Cluster Comput. 17(2), 487–502 (2014)CrossRefGoogle Scholar
  24. 24.
    Seybold, D., Domaschka, J.: Is distributed database evaluation cloud-ready? In: ADBIS (2017)Google Scholar
  25. 25.
    Seybold, D., Wagner, N., Erb, B., Domaschka, J.: Is elasticity of scalable databases a myth? In: IEEE Big Data (2016)Google Scholar
  26. 26.
    Tseitlin, A.: The antifragile organization. Commun. ACM 56(8), 40–44 (2013)CrossRefGoogle Scholar
  27. 27.
    Zhong, M., Shen, K., Seiferas, J.: Replication degree customization for high availability. In: EuroSys (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Seybold
    • 1
  • Christopher B. Hauser
    • 1
  • Simon Volpert
    • 1
  • Jörg Domaschka
    • 1
  1. 1.Institute of Information Resource ManagementUlm UniversityUlmGermany

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