Blockchain Based Cloud Management Architecture for Maximum Availability

  1. Alberto Arias Maestro 1
  2. Oscar Sanjuan Martinez 1
  3. Ankur M. Teredesai 2
  4. Vicente García-Díaz 3
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

  2. 2 University of Washington Tacoma
    info

    University of Washington Tacoma

    Tacoma, Estados Unidos

    ROR https://ror.org/05n8t2628

  3. 3 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

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Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Título del ejemplar: Special Issue on AI-driven Algorithms and Applications in the Dynamic and Evolving Environments

Volumen: 8

Número: 1

Páginas: 88-94

Tipo: Artículo

DOI: 10.9781/IJIMAI.2023.02.002 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Contemporary cloud application and Edge computing orchestration systems rely on controller/worker design patterns to allocate, distribute, and manage resources. Standard solutions like Apache Mesos, Docker Swarm, and Kubernetes can span multiple zones at data centers, multiple global regions, and even consumer point of presence locations. Previous research has concluded that random network partitions cannot be avoided in these scenarios, leaving system designers to choose between consistency and availability, as defined by the CAP theorem. Controller/worker architectures guarantee configuration consistency via the employment of redundant storage systems, in most cases coordinated via consensus algorithms such as Paxos or Raft. These algorithms ensure information consistency against network failures while decreasing availability as network regions increase. Mainstream blockchain technology provides a solution to this compromise while decentralizing control via a fully distributed architecture coordinated through Byzantine-resistant consensus algorithms. This research proposes a blockchain-based decentralized architecture for cloud resource management systems. We analyze and compare the characteristics of the proposed architecture concerning the consistency, availability, and partition resistance of architectures that rely on Paxos/Raft distributed data stores. Our research demonstrates that the proposed blockchain-based decentralized architecture noticeably increases the system availability, including cases of network partitioning, without a significant impact on configuration consistency.

Referencias bibliográficas

  • M. Schwarzkopf, A. Konwinski, M. Abd-El-Malek, and J. Wilkes, “Omega, Flexible, scalable schedulers for large compute clusters”, in Proceedings from the European Conference on Computer Systems, Prague, Czech Republic, 2013, pp. 351-364, doi: 10.1145/2465351.2465386.
  • G. Dasher, I. Envid, and B. Calder, “Architectures for Protecting Cloud Data Planes”, Google, Mountain View, CA, USA, 2022. Accessed: Nov. 15, 2022. Available: https://arxiv.org/abs/2201.13010, doi: 0.48550/ arXiv.2201.13010.
  • A. Kumar, S. Avinash Kumar, V. Dutt, A. Dubey, S. Narang, “A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies”, International Journal of Interactive Multimedia and Artificial Intelligence, In Press, pp. 1-8, 2021, doi: 10.9781/ ijimai.2021.11.004.
  • G. Zhang, X. Chen, L. Zhang, B. Feng, X. Guo, J. Liang, Y. Zhang, “STAIBT: Blockchain and CP-ABE Empowered Secure and Trusted Agricultural IoT Blockchain Terminal”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 5, pp. 66-75, 2022, doi: 10.9781/ ijimai.2022.07.004.
  • A. Berenberg, and B. Calder, “Deployment Archetypes for Cloud Applications”, ACM Computing Surveys, vol. 55, no. 3, pp. 1-48, 2022, doi:10.48550/arXiv.2105.00560.
  • S. Sebastio, R. Ghosh, and T. Mukherjee, “An availability analysis approach for deployment configurations of containers”, IEEE Transactions on Services Computing, vol. 14, no. 1, pp. 16-29, 2018, doi:10.1109/TSC.2017.2788442.
  • E. Brewer, “Spanner, truetime and the cap theorem”, Google, Mountain View, CA, USA, 2022. Accessed: Nov. 15, 2022. Available: https://research.google/pubs/pub45855.
  • P. Bailis, and K. Kingsbury, “The network is reliable: An informal survey of real-world communications failures”, Queue, vol. 12, no. 7, pp. 20-32, 2014, doi:10.1145/2639988.2655736.
  • L. Lamport, “The part-time parliament”, ACM Transactions on Computer System, vol. 16, no. 2, pp 133-169, 1998, doi:10.1145/3335772.3335939.
  • V. Gramoli, “From blockchain consensus back to Byzantine consensus”, Future Generation Computer Systems, vol. 107, no. C, pp. 760-769, 2020, doi:10.1016/j.future.2017.09.023.
  • D. Bernstein, “Cloud Foundry Aims to Become the OpenStack of PaaS”, in IEEE Cloud Computing, vol. 1, no. 2, pp. 57-60, 2014, doi:10.1109/ MCC.2014.32.
  • B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. Joseph, R. Katz, S. Shenker, and I. Stoica, “Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center”, 8th USENIX Symposium on Networked Systems Design and Implementation, vol. 11, pp. 22-22, 2011.
  • N. Naik, “Building a virtual system of systems using docker swarm in multiple clouds”, IEEE International Symposium on Systems Engineering (ISSE), pp. 1-3, 2016, doi: 10.1109/SysEng.2016.7753148.
  • B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, “Borg, omega, and Kubernetes”, Communications of the ACM, vol. 59, no. 5, pp. 50-57, 2016, doi:10.1145/2890784.
  • S. Davidson, “Optimism and consistency in partitioned distributed database systems”, ACM Transactions on Database Systems (TODS), vol. 9, no. 3, pp. 456-481, 1984, doi:10.1145/1270.1499.
  • H. Howard, M. Schwarzkopf, A. Madhavapeddy, and J. Crowcroft, “Raft refloated: Do we have consensus?”, ACM SIGOPS Operating Systems Review, vol 49, no. 1, pp. 12-21, 2015, doi:10.1145/2723872.2723876.
  • B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, “Borg, Omega, and Kubernetes: Lessons learned from three containermanagement systems over a decade”, Queue, vol. 14, no. 1, pp. 70–93, 2016, doi:10.1145/2898442.2898444.
  • J. Hellerstein, and P. Alvaro, “Keeping CALM: when distributed consistency is easy”, Communications of the ACM, vol. 63, no. 9, pp. 72- 81, 2020, doi: 10.48550/arXiv.1901.01930.
  • J. Yang, J. Dai, H. B. Gooi, H. Nguyen and A. Paudel, “A Proof-of-Authority Blockchain Based Distributed Control System for Islanded Microgrids”, IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 8287- 8297, 2022, doi:10.1109/TII.2022.3142755.
  • P. K. Sharma, M. Chen and J. H. Park, “A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT”, IEEE Access, vol. 6, pp. 115-124, 2018, doi: 10.1109/ACCESS.2017.2757955.
  • G. Nguyen, and K. Kim, “A survey about consensus algorithms used in blockchain”, Journal of Information processing systems, vol. 14, no. 1, pp. 101-128, 2018, doi:10.3745/JIPS.01.0024.
  • I. Weber, V. Gramoli, A. Ponomarev, M. Staples, R. Holz, A. Tran, and P. Rimba, “On availability for blockchain-based systems”, IEEE 36th Symposium on Reliable Distributed Systems (SRDS), Hong Kong, China, pp. 64-73, 2017, doi:10.1109/SRDS.2017.15.
  • G. Carrara, L. Burle, D. Medeiros, C. Vinicius, and D. Mattos, “Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking”, Annals of Telecommunications, vol. 75, no. 3, pp. 163-174, 2020.
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