A holistic abstraction to ensure trusted scaling and memory speed trusted analytics

Abstract

In this study, a trusted holistic abstraction is proposed and analytically discussed using universal scalability law and Markovian chain Monte-Carlo method. Moreover, a feedback mechanism is modeled to explain the elasticity performance of the proposed distributed system. The system extends the data locality to the edges in a trusted manner and ensures trust while scaling the whole system and increasing the number of nodes. By the help of such a trusted solution, lineage information of the data at the edges enable fault-recovery from an available checkpoint, while maximizing the trustworthiness of the overall system. Innovative distributed data structures, make databases fresh for all scaled nodes by unifying the memory resources; minimize the need to trusted third parties via trusted distributed data structures, which uses checksums of the datum periodically. Hence, multi-layer neural networks and hierarchical tree structures, has confidential data, can be updated and trained dynamically. Searching speed and performance of an object or set of objects in massive systems is maximized while keeping the trustworthiness of the total system. Initial results indicate that the trust cost worth to pay to scale and to keep the performance of the whole system. The System also shows good elasticity in the case of sudden provisioning/de-provisioning of control nodes. The proposed system also has satisfactory resource-allocation capability with efficient clustering thanks to the introduction of distributed ledger-based transaction management and lineage data recording for dynamic management of DAG structures, has sub-modular and disjoint cluster sets. Initial results of micro-blog analytics indicate promising performance of unified batch/interactive/ad-hoc querying with the holistic abstraction. © 2019 IEEE.