Using Distributed Ledgers To Build Knowledge Graphs For Decentralized Computing Ecosystems
Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.
Authors
- Ahmed Khalid (ORCID: https://orcid.org/0000-0002-6778-5661)
- Tarek Zaarour (ORCID: https://orcid.org/0000-0002-9807-4109)
- Ahmed H. Zahran (ORCID: https://orcid.org/0000-0003-3405-0324)
- Preeja Pradeep (ORCID: https://orcid.org/0000-0001-6708-4964)
Institutions
- University College Cork (IE)
- DELL (Ireland) (IE)
Publication Details
- Published
- 2024-10-20
- DOI
- https://doi.org/10.1145/3627673.3679644
- Citations
- 2
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 1.06