Loading...

Elaris Computing Nexus

Elaris Computing Nexus


Evaluating Performance and Fault Tolerance of a Linearizable Distributed Cache in Comparison to Redis Cluster


Elaris Computing Nexus

Received On : 10 February 2025

Revised On : 18 March 2025

Accepted On : 20 April 2025

Published On : 30 April 2025

Volume 01, 2025

Pages : 032-040


Abstract

We present the experimental findings of a proposed linearizable cache system, which is subsequently compared to those of Redis Cluster, a widely utilized distributed caching solution. Both were implemented using Docker to establish a distributed environment with failure management. The evaluation encompassed measuring read and write requests, analyzing latency percentiles (P25, P50, P80, P90, P99), and assessing fault tolerance concerning disk failure, network delay, network partition, and node failure. The results indicate that the linearizable cache outperforms Redis Cluster in terms of read latency but incurs higher write latency due to strong consistency. It maintains consistency during a majority node failure, unlike Redis Cluster, which leads to data loss and inconsistencies. The scalability seemed promising with 3, 5, and 7 nodes; nevertheless, a storage constraint was encountered in the proposed system as the cluster size expanded.

Keywords

Linearizable Cache, Redis Cluster, Distributed Systems, Fault Tolerance, Performance Evaluation, Docker, Scalability, Consistency Guarantees, Latency, Benchmarking.

  1. R. Alubady, M. Salman, and A. S. Mohamed, “A review of modern caching strategies in named data network: overview, classification, and research directions,” Telecommunication Systems, vol. 84, no. 4, pp. 581–626, Sep. 2023, doi: 10.1007/s11235-023-01015-3.
  2. T. Salah, M. J. Zemerly, N. C. Y. Yeun, M. Al-Qutayri, and Y. Al-Hammadi, “The evolution of distributed systems towards microservices architecture,” 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 318–325, Dec. 2016, doi: 10.1109/icitst.2016.7856721.
  3. D. Wilson, “Architecture for a fully decentralized Peer-to-Peer collaborative computing platform,” 2015. doi: 10.20381/ruor-4170.
  4. S. Androutsellis-Theotokis and D. Spinellis, “A survey of peer-to-peer content distribution technologies,” ACM Computing Surveys, vol. 36, no. 4, pp. 335–371, Dec. 2004, doi: 10.1145/1041680.1041681.
  5. Z. Huo et al., “A metadata cooperative caching architecture based on SSD and DRAM for file systems,” in Lecture notes in computer science, 2015, pp. 31–51. doi: 10.1007/978-3-319-27122-4_3.
  6. M. Kowarschik and C. Weiß, “An overview of cache optimization techniques and Cache-Aware numerical algorithms,” in Lecture notes in computer science, 2003, pp. 213–232. doi: 10.1007/3-540-36574-5_10.
  7. Y. Meng, M. A. Naeem, R. Ali, Y. B. Zikria, and S. W. Kim, “DCS: Distributed Caching Strategy at the edge of vehicular sensor Networks in Information-Centric Networking,” Sensors, vol. 19, no. 20, p. 4407, Oct. 2019, doi: 10.3390/s19204407.
  8. S. Borst, V. Gupta, and A. Walid, “Distributed Caching Algorithms for Content Distribution Networks,” 2010 Proceedings IEEE INFOCOM, pp. 1–9, Mar. 2010, doi: 10.1109/infcom.2010.5461964.
  9. F. Petrot, A. Greiner, and P. Gomez, “On cache coherency and memory consistency issues in NOC based shared Memory Multiprocessor SOC architectures,” 2022 25th Euromicro Conference on Digital System Design (DSD), pp. 53–60, Jan. 2006, doi: 10.1109/dsd.2006.73.
  10. Mahmood, R. Exel, H. Trsek, and T. Sauter, “Clock Synchronization over IEEE 802.11—A survey of Methodologies and Protocols,” IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 907–922, Dec. 2016, doi: 10.1109/tii.2016.2629669.
  11. S. Goel, P. Miesing, and U. Chandra, “The impact of illegal Peer-to-Peer file sharing on the media industry,” California Management Review, vol. 52, no. 3, pp. 6–33, May 2010, doi: 10.1525/cmr.2010.52.3.6.
  12. H. Salhi, F. Odeh, R. Nasser, and A. Taweel, “Benchmarking and performance analysis for Distributed cache Systems: A Comparative case study,” in Lecture notes in computer science, 2017, pp. 147–163. doi: 10.1007/978-3-319-72401-0_11.
  13. W. Khan, T. Kumar, C. Zhang, K. Raj, A. M. Roy, and B. Luo, “SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review,” Big Data and Cognitive Computing, vol. 7, no. 2, p. 97, May 2023, doi: 10.3390/bdcc7020097.
  14. Tzenetopoulos, M. Gazzetti, D. Masouros, C. Pinto, S. Xydis, and D. Soudris, “Disaggregated RDDs: Extending and Analyzing Apache Spark for Memory Disaggregated Infrastructures,” 2024 IEEE International Conference on Cloud Engineering (IC2E), pp. 107–117, Sep. 2024, doi: 10.1109/ic2e61754.2024.00019.
  15. H. Salhi, F. Odeh, R. Nasser, and A. Taweel, “Open Source In-Memory Data Grid Systems: Benchmarking Hazelcast and Infinispan,” ICPE ’17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, pp. 163–164, Apr. 2017, doi: 10.1145/3030207.3053671.
  16. P. Kathiravelu, “An elastic middleware platform for concurrent and distributed cloud and MapReduce simulations,” arXiv (Cornell University), Jan. 2016, doi: 10.48550/arxiv.1601.03980.
  17. Agarwal, J. Hennessy, and M. Horowitz, “Cache performance of operating system and multiprogramming workloads,” ACM Transactions on Computer Systems, vol. 6, no. 4, pp. 393–431, Nov. 1988, doi: 10.1145/48012.48037.
  18. Y. Chen, F. Raab, and R. Katz, “From TPC-C to big Data Benchmarks: a functional workload model,” in Lecture notes in computer science, 2013, pp. 28–43. doi: 10.1007/978-3-642-53974-9_4.
  19. Sanka, M. H. Chowdhury, and R. C. C. Cheung, “Efficient High-Performance FPGA-REDIS Hybrid NoSQL caching System for blockchain scalability,” Computer Communications, vol. 169, pp. 81–91, Jan. 2021, doi: 10.1016/j.comcom.2021.01.017.
CRediT Author Statement

The author reviewed the results and approved the final version of the manuscript.

Acknowledgements

Author(s) thanks to University of Science and Technology of China for research lab and equipment support.

Funding

No funding was received to assist with the preparation of this manuscript.

Ethics Declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Availability of Data and Materials

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Author Information

Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.

Corresponding Author



Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/

Cite this Article

Fengbin Sun, “Evaluating Performance and Fault Tolerance of a Linearizable Distributed Cache in Comparison to Redis Cluster”, Elaris Computing Nexus, pp. 032-040, 30 April 2025, doi: 10.XXXXX/ECN/2025004.

Copyright

© 2025 Fengbin Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.