The growth in cloud-native applications has made their usage hard to plan and monitor. The rule-based Kubernete execution is more active and flexible than its peers, which in itself is a challenge when the cloud is evolving in obscure ways. This is the reason behind our framework which utilizes multiple agent deep learning for needs application orchestration. It is an orchestration framework that employs Multi-Agent Reinforcement Learning (MARL) to permit crafted agents to optimize the method and goals simultaneously and adapt to variable parameters. We explain the components of the structure and deep learning models that focus on decision making in the framework architecture. Using a cloud cluster in a simulation, we were able to show that our approach, unlike the other orchestration systems, did greatly better in relation to resource usage, service latency, and operational resilience. This is strong evidence for the intelligent, self-managing cloud architecture we propose.
In the modern era of communication, having multi-dimensional diversity, it becomes very crucial to maintain the secrecy of multi-media messages with the help of a strong cryptosystem algorithm. In the present paper, the application of picture fuzzy matrices and neutrosophic number theory provides a strong hybrid approach capable of dealing with incomplete/uncertain data of real-life problems related to multi-media images and graphs. The proposed methodology of encryption and decryption has generalized the El-Gamal algorithm of cryptography using picture fuzzy matrices and neutrosophic numbers. An illustrative example showing the proposed methodology has been well presented.
The rapid growth of Internet of Things (IoT) devices has led to large-scale continuous data streams that require realtime processing. Traditional cloud-centric architectures fail to meet low-latency and bandwidth efficiency requirements due to network delays and high data transmission overhead. This paper proposes EdgeStream, a lightweight edge-based framework for real-time streaming analytics in IoT environments. The system integrates edge nodes for local processing with a cloud backend for coordination and storage, using MQTT-based communication and distributed processing with anomaly detection. Analytical models for latency, throughput, and bandwidth are developed to evaluate performance. Experimental results, compared to cloudbased systems, show up to 92.8
Combining federated learning (FL) with quantum computing can help create new diagnostic systems with privacy, efficiency, and scalability. This paper introduces the first iteration of Quantum-Assisted Federated Learning (QA-FL). It is a framework to begin addressing the frontiers of FL: communication overhead, Non-Independent and Identically Distributed (non-IID) data-heterogeneities, and gradient leakage. We document the foundational work for the mathematics of QA-FL, propose a first quantum-safe aggregation and hybrid optimization, and suggest efficiency improvement with conceptual tables. Additional comparisons were designed for a time and motion analysis of the quantum instruments described. We close with tangible Noisy Intermediate-Scale Quantum (NISQ)-era implications with a foundational gap of moving QA-FL from a conceptual to practical framework.
Decentralized Autonomous Organizations (DAOs) allow for novel collective governance. However, their reliance on conventional forms of cryptography raises fundamental security concerns, as well as paradoxes in their governance. With the emergence of fault-tolerant quantum computers threatening critical IoT-Blockchain ecosystems, which will shatter today's monetary encryption, this study proposes the first holistic, comprehensive, and conceptualization of a Quantum-Secured DAO (Q-DAO). Such entities will have their core functions organically designed around the foundational elements of quantum theory. Q-DAO design conceptualization transcends the mere addition of post-quantum cryptography and addresses the re-invention of the trustlessness paradigm. It will transform current reliance on a computational assumption to a physical guarantee of trustlessness as defined by the immutable and unassailable laws of nature. The designed system conceptualization will revolve around four pillars: The first is quantum-state governance designed tokens exploiting the no-cloning theorem towards Sybil attacks. The second focuses on a secure and private voting stratagem induced by quantum entanglement. The third introduces a hybrid onchain/quantum channel governance system designed to ensure simultaneous transparency and security of communication. Finally, the fourth emphasizes a novel quantum interference for dispute resolution that overcomes the Code is Law rigidity.
