Abstract
Cloud computing has been one of the disruptive technologies to change the traditional application operation for the last decades. The success of Cloud boosts ever more newly-built data centers. Although these data centers are distributed all around the world, the computing resources are managed in a relatively centralized manner within one big data center. For a specific small area, the centralized Cloud lacks the dispersion to satisfy the requirements of collaborative applications, e.g., the nearest data center might still be too far to satisfy the network latency. Through spreading the computing resources at the edge of the network, the emerging Edge computing can complete the data processing before uploading to Cloud. However, Edge computing still stays at the conceptual and experimental stage. Trust and incentive model are missing to motivate the Edge node and micro Cloud owners to share the computing infrastructure resources for building a more generalized and decentralized ecosystem. Traditional method of building trust through authority is not applicable in current edge environment, which is more like peer-to-peer relationship between the customer and provider. To tackle this issue, ALLSTAR is proposed, which is a blockchain-based approach to enhance the trust for equally combining all the Cloud and Edge resources to be seamlessly leveraged by the application. The ALLSTAR approach is a systematic solution to realize decentralized resource management, including Cloud and Edge resource sharing and trading, and target at building the trustworthy ALLSTAR ecosystem. In this paper, we first analyze the challenges of utilizing distributed Cloud and Edge resources, and describe the overall architecture of ALLSTAR, including the related key techniques, detailed application development and operations processes as well as the new business model. Moreover, an empirical study on the permissioned blockchain evaluation is conducted. The study not only demonstrates the ALLSTAR approach is feasible but also provides insights of which blockchain to choose when constructing such an ecosystem.
Similar content being viewed by others
Notes
For simplicity, Sawtooth, Iroha, Fabric, Besu, and Ethereum are used in the following text.
References
Buterin V, et al. (2021) A next-generation smart contract and decentralized application platform. Accessed 09 Jun 2021 https://github.com/ethereum/wiki/wiki/White-Paper
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comp Syst 25(6):599–616
Castro M, Liskov B, et al. (1999) Practical byzantine fault tolerance. In: 1999USENIX Symposium on Operating Systems Design and Implementation (OSDI), pp 173–186
Faniyi F, Bahsoon R (2015) A systematic review of service level management in the cloud. ACM Comput Surv 48(3):1–27
Guo Y, Wang S, Zhou A, Xu J, Yuan J, Hsu CH (2019) User allocation-aware edge cloud placement in mobile edge computing. Softw-Pract Exp 50(5):489–502
Higuchi T, Dressler F, Altintas O (2018) How to keep a vehicular micro cloud intact
Hu Y, Zhou H, de Laat C, Zhao Z (2018) Ecsched: Efficient container scheduling on heterogeneous clusters. In: 2018 European Conference on Parallel Processing (Euro-Par). Springer, pp 365–377
Hu Y, Zhou H, de Laat C, Zhao Z (2020) Concurrent container scheduling on heterogeneous clusters with multi-resource constraints. Futur Gener CompSyst 102:562–573
Jeferry K, Kousiouris G, Kyriazis D, Altmann J, Ciuffoletti A, Maglogiannis I, Nesi P, Suzic B, Zhao Z (2015) Challenges emerging from future cloud application scenarios. Procedia Comput Sci 68:227–237
Koulouzis S, Martin P, Zhou H, Hu Y, Wang J, Carval T, Grenier B, Heikkinen J, de Laat C, Zhao Z (2020) Time-critical data management in clouds: Challenges and a dynamic real-time infrastructure planner (DRIP) solution. Concurr Comput-Pract Exp 32(16):e5269
Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Accessed 09 Jun 2021: https://bitcoin.org/bitcoin.pdf
Nakashima H, Aoyama M (2017) An automation method of SLA contract of web APIs and its platform based on blockchain concept. In: 2017 IEEE International Conference on Cognitive Computing (ICCC). IEEE, pp 32–39
Nofer M, Gomber P, Hinz O, Schiereck D (2017) Blockchain. Bus Inf Syst Eng 59 (3):183–187
Qian H, Andresen D (2016) Automate scientific workflow execution between local cluster and cloud. Int J Networked Distrib Comput 4(1):45–54
Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: Vision and challenges. IEEE Internet Things J 3(5):637–646
Shi Z, Zhou H, Surbiryala J, Hu Y, de Laat C, Zhao Z (2019) An automated customization and performance profiling framework for permissioned blockchains in a virtualized environment. In: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, pp 404–410
Teerapittayanon S, McDanel B, Kung HT (2017) Distributed deep neural networks over the cloud, the edge and end devices. In: IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, pp 328–339
Venkateswaran S, Sarkar S (2018) Architectural partitioning and deployment modeling on hybrid clouds. Softw-Pract Exp 48(2):345–365
Wang N, Varghese B, Matthaiou M, Nikolopoulos DS (2017) Enorm: A framework for edge node resource management. IEEE Trans Serv Comput 13(6):1086–1099
Wang X, Yeo CS, Buyya R, Su J (2011) Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Futur Gener Comp Syst 27(8):1124–1134
Zheng Z, Xie S, Dai HN, Chen X, Wang H (2018) Blockchain challenges and opportunities: A survey. Int J Web Grid Serv 14(4):352–375
Zhou H, Hu Y, Su J, de Laat C, Zhao Z (2018) Cloudsstorm: An application-driven framework to enhance the programmability and controllability of cloud virtual infrastructures. In: 2018 IEEE International Conference onCloud Computing (CLOUD). Springer, pp 265–280
Zhou H, Hu Y, Ouyang X, Su J, Koulouzis S, de Laat C, Zhao Z (2019) Cloudsstorm: A framework for seamlessly programming and controlling virtual infrastructure functions during the DevOps lifecycle of cloud applications. Softw-Pract Exp 49(10):1421–1447
Zhou H, Ouyang X, Ren Z, Su J, de Laat C, Zhao Z (2019) A blockchain based witness model for trustworthy cloud service level agreement enforcement. In: 2019 IEEE International Conference on Computer Communications (INFOCOM). IEEE, pp 1567–1575
Uriarte RB, Zhou H, Kritikos K, Shi Z, Zhao Z, De Nicola R (2020) Distributed service-level agreement management with smart contracts and blockchain. Concurr Comput-Pract Exp p e5800
Ziafat H, Babamir SM (2018) Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c-mean and MOLP, vol 48
Acknowledgements
This research is partially funded by the EU Horizon 2020 research and innovation program under grant agreements No. 825134 (ARTICONF), No. 824068 (ENVRI-FAIR), and No. 862409 (BLUECLOUD). This work is also supported by the National Key Research and Development Program of China under grant 2018YFB0204301 and the Natural Science Foundation of Hunan Province under grant No. 2020JJ3042. We thank MOG Technologies for providing the crowd journalism usecase. The author, Zeshun Shi, is also sponsored by China Scholarship Council.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article article is part of the Topical Collection: Special Issue on Blockchain for Peer-to-Peer Computing
Guest Editors: Keping Yu, Chunming Rong, Yang Cao, and Wenjuan Li
Huan Zhou and Zeshun Shi contributes equally to this paper.
Rights and permissions
About this article
Cite this article
Zhou, H., Shi, Z., Ouyang, X. et al. Building a blockchain-based decentralized ecosystem for cloud and edge computing: an ALLSTAR approach and empirical study. Peer-to-Peer Netw. Appl. 14, 3578–3594 (2021). https://doi.org/10.1007/s12083-021-01198-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-021-01198-z