Persisting Trust in Emerging Hybrid-Clouds and 6G Systems

Distributed ledger-based transaction management approaches and lineage data recording for dynamic management of DAG (Directed Acyclic Graph) structures can achieve trusted scalability of analytical transactions in some manner. Sub-modular and disjoint cluster sets increase connectivity performance of analytical systems. Unified batch/interactive/ad-hoc querying can enable trusted E2E (end-to-end) analytics for massive systems. Trust factor and trust cost optimization reinforce the trustworthiness of the overall system in varying context. However, security and privacy architectures are still context dependent. Dynamic change in a context have to be adapted to all system layers in real-time for a complete E2E trust mechanism and confidence in smart-ecosystems.In this study, we explored dynamic security and privacy aspects of E2E analytical transactions in varying context. Classical behavior modelling approaches; such as, cellular automata, chaotic systems, hierarchical block diagram modeling methods are cumbersome to adapt the dynamism. Persisting and ensuring the trust for varying contexts with an E2E trust mechanism enable to adapt the dynamism at massive scale. Initial arguments for data exchange over a hybrid-cloud node, instead of cell unit scenario in a simulated 5G environment with the trust mechanism, is evaluated. It is promising to meet zero latency requirement of MEC (Multi-access/Mobile Edge Computing) units. So that, we can say that transmitting data over a hybrid-cloud node rather than cell units can maximize mobility of 5/6G ecosystem with the E2E trust mechanism.