ICSS 2016: Advances in Systems Science pp 123-130 | Cite as
Evaluating Raft in Docker on Kubernetes
Abstract
In computing systems, some applications require high availability. The creation of copies improves availability, but keeping the copies synchronized requires the replication of the application state. Raft is a consensus algorithm that emerged with an easy understanding logic and a consequently well accepted solution. At infrastructure level, containers offer an alternative for replacing traditional virtual machines in cloud providers. This paper (This project was supported by CNPq proc. 401364/2014-3) evaluates the execution of Raft in physical machines and in Kubernetes, a container management system developed by Google and other companies. Results show similar performance for Raft in both environments.
Keywords
Raft Performance Kubernetes Docker ContainersReferences
- 1.Bernstein, D.: Containers and cloud: from lxc to docker to kuber-netes. IEEE Cloud Comput. 1(3), 81–84 (2014)CrossRefGoogle Scholar
- 2.CoreOS. etcd (2016). https://coreos.com/etcd. Accessed 12 May 2016
- 3.Felter, W., et al.: An updated performance comparison of virtual machines, Linux containers. In: International Symposium on Performance Analysis of Systems and Software, pp. 171–172. IEEE (2015)Google Scholar
- 4.GitHub. Raft (2016). http://raft.github.io. Accessed 12 May 2016
- 5.Howard, H., et al.: Raft refloated: do we have consensus? ACM SIGOPS Oper. Syst. Rev. 49(1), 12–21 (2015)CrossRefGoogle Scholar
- 6.Lamport, L.: The part-time parliament. ACM Trans. Comput. Syst. 16(2), 133–169 (1998)CrossRefGoogle Scholar
- 7.Mell, P., Grance, T.: The NIST definition of cloud computing. Technical report Spp. 800–145. Gaithersburg, MD, United States: National Institute of Standards & Technology (2011)Google Scholar
- 8.Moraru, I., Andersen, D.G., Kaminsky, M.: There is more consensus in egalitarian parliaments. In: Proceedings of the Twenty-Fourth Symposium on Operating Systems Principles, pp. 358–372. ACM (2013)Google Scholar
- 9.Ongaro, D.: Consensus: bridging theory and practice. Ph.D. thesis. Stanford University (2014)Google Scholar
- 10.Ongaro, D., Ousterhout, J.: In search of an understandable consensus algorithm. In: USENIX Annual Technical Conference, pp. 305–320 (2014)Google Scholar
- 11.Peinl, R., Holzschuher, F., Pfitzer, F.: Docker cluster management for the cloud - survey results, own solution. J. Grid Comput. 14, 1–18 (2016)CrossRefGoogle Scholar
- 12.Popek, G.J., Goldberg, R.P.: Formal requirements for virtualizable third generation architectures. Commun. ACM 17(7), 412–421 (1974). ISSN:0001–0782MathSciNetCrossRefMATHGoogle Scholar
- 13.Schneider, F.B.: Implementing fault-tolerant services using the state machine approach: a tutorial. ACM Comput. Surv. 22(4), 299–319 (1990)CrossRefGoogle Scholar
- 14.Sill, A.: Emerging standards, organizational Patterns in cloud computing. IEEE Cloud Comput. 2(4), 72–76 (2015). ISSN:2325–6095CrossRefGoogle Scholar
- 15.Toffetti, G., et al.: An architecture for self-managing microservices. In: Proceedings of the 1st International Workshop on Automated Incident Management in Cloud, pp. 19–24. ACM (2015)Google Scholar
- 16.Verma, A., et al.: Large-scale cluster management at Google with Borg. In: European Conference on Computer Systems, p. 18. ACM (2015)Google Scholar