6 research outputs found
A Holistic Abstraction to Ensure Trusted Scaling and Memory Speed Trusted Analytics
No full textIn 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.American Council on Science and Educatio
Brainit: A Generic IT Core Mechanism for Continuous Growth-Flow in Dynamic Chaotic Context
No full textAmerican Council on Science and EducationBrain CAPEX (Capital Expenses) is for free to the human being but the OPEX (Operational Expenses) is not. Since, the fluctuations on critical nutrition for brain makes it complicated to grow via optimal path as continues progress due to the chaotic OPEX changes. Fortunately, intelligent systems are able to adapt the change dynamically up to varying chaotic context, by keeping trustworthiness of the whole system via available distributed resources and algorithms. However, increasing number of nodes in the system inflates complexity of swarm behavior due to computation and memory limitations. Drastic progress saved in the emerging edge devices, can enable to produce innovative trusted AI/ML algorithms at run-time, which can help to make massive analytics at the edge nodes in (near) real time. In spite of this, keeping the system resilient require real-time updates in different system layers. As another critical milestone, increased scalability and faster in memory processing speed can be accomplished via big data technologies and ledger base chained structures in some manner. In order to keep high performance of the total system, mission/safety/operation critical applications require to be verified by critical check-points. Thereby, end-to-end trust mechanism and swarm controller methods can improve trusted scalability of the intelligent systems analytical functions and resources. So that, the dynamic holistic views can ensure trustworthiness in chaotic context with the brainIT generic IT core mechanism for continuous growth in massive-chaos, which ensures to keep local/global legal constraints-based risk minimization via 5G connected hybrid-cloud systems within the observed socio-dynamic parameters with minimized optimal OPEX costs. © 2025 Elsevier B.V., All rights reserved
End-to-End Trusted Execution Environment (TEE) with Dynamic Holistic View Based Throughput Maximization Approach
No full textAmerican Council on Science and EducationDrastic progress and improvements on classical behavior modelling approaches; such as, cellular automata, chaotic systems, hierarchical block diagram modeling methods enabled to avoid of cumborsomism while adapting the dynamism at massive scale at some extend. However, persisting and ensuring the trust for varying contexts with an E2E trust mechanism require dynamic holistic views to adapt the dynamism at massive scale with extended data locality to the edges in trusted scalable manner. Initial observations for data exchange over a hybrid-cloud node, instead of cell unit scenario in 5G environment with the trust mechanism is promising to meet zero latency requirement of MEC (Multi-access/Mobile Edge Computing) edge units thanks to the improvements provided via memory-centric system design paradigms. It shows that data can be transmitted over a hybrid-cloud node rather than cell units can maximize total system throughput of emerging hybrid-clouds, which have 5/6G connectivity and strong quantum back-end units with the E2E trusted execution environment (TEE) and dynamic holistic views. By that means, it is promising to utilize MEMCA hybrid-cloud as massive scale cyber-intelligence system within the national security legal constraints with the E2E TEE, which have maximized total system throughput via dynamic holistic views of the observed chaotic context. So that, we can say that efficient utilization of MEMCA hybrid-cloud to national security systems as digital dynamics core mechanism can port the massive chaos in socio-dynamics to massive-growth via the dynamic feedback controller structures and embedded check-points to the available physical locations within the (near) real-time cyber intelligence mechanisms with maximized total system throughput values. © 2025 Elsevier B.V., All rights reserved
MEMCA for satellite and space data: MEMCA [Memory centric analytics] for satellite and space data
No full textTrusted Distributed Artificial Intelligence (TDAI)
No full textAs the diversity of components increases within the intelligent systems, trusted interactivity also becomes critical challenge for the system components and nodes. Furthermore, emerging SDN (Software Defined Networking) features are also utilized to assure its resiliency and robustness in a dynamic context and monitored by trusted multi-agents' system to maximize trustworthiness of the system components and the deployed context. However, it is not feasible to deploy the intelligent mechanisms at massive scale with the state-of-the-art architectural design paradigms. Therefore, we define three main architectures (central, decentral/autonomous/embedded, distributed/hybrid) as a basis for TDAI methodology to ensure end-to-end trust in holistic AI system life-cycle. Thanks to such a trusted multi-agents-based trust monitoring mechanism, we will be able to overcome hardware limitations and provide flexible and resilient end-to-end trust mechanism for trusted AI models and emerging massive scale intelligent systems. Finally, we evaluated our TDAI Methodology in CCAM (Connected, Cooperative, Autonomous Mobility) domain of a smart-city to monitor its system trust and user behaviors. By that means, it is exploited as a mean of decision-making mechanism to be deployed either manually or automatically (example of anomalies detection etc.). Such a mechanism improves total system performance and behavioral anomaly detection and risk minimization algorithms over the distributed nodes of a given AI system. Furthermore, smartness features are also improved with human-like intelligence abilities at massive scale thanks to the promising performance of TDAI at real-life deployment experiments to maximize trust factor of the dynamically observed context of the smart-cities during the monitored time-span. © 2013 IEEE
