Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 978-980· 0 citations· 12 references
Abstract
The emergence of AI-native 6G networks necessitates efficient and proactive resource management mechanisms to support highly dynamic and data-intensive services. Existing approaches typically employ independent prediction models for mobility, handover, and channel quality, leading to redundant data processing and limited exploitation of cross-layer dependencies. In this paper, we propose a shared data intelligence–driven multi-task prediction framework that jointly models mobility, handover, and channel quality indicator (CQI) within a unified learning architecture. By leveraging a common feature space and a single processing pipeline, the proposed framework simultaneously generates multiple correlated predictions, thereby reducing computational overhead. A correlation analysis using real-world datasets demonstrates that mobility, CQI, and handover events exhibit inherent inter-dependencies, justifying the use of a shared representation. Furthermore, a processing time comparison shows that the proposed approach achieves approximately 10.12% reduction compared to conventional independent prediction models by eliminating redundant feature extraction and repeated model execution. These results validate that shared data intelligence is an effective and scalable solution for efficient multi-task prediction in real-time 6G network environments.
: Quality of Service (QoS) prediction in service-oriented computing supports candidate service identification and resource scheduling. However, the limited single-source data dimension fails to capture dynamic characteristics such as network fluctuations, spatiotemporal migration, and user behavior coupling. This study integrates three types of heterogeneous information to construct a unified embedding space, achieves cross-source alignment of semantically heterogeneous data through a hierarchical architecture, introduces differentiated dynamic weight allocation among three data sources, captures nonlinear evolution patterns through temporal joint representations, and employs multi-source context-guided sparse compensation to address missing entries in the service invocation matrix. Experimental results demonstrate that the proposed method significantly outperforms single-source models in both prediction accuracy and robustness under complex scenarios.
Zhenzhen Liu· Academic Journal of Computin...· 0 citations
To address the challenges of heterogeneous multi-source data, inadequate collaborative modeling of temporal and topological features, and low efficiency in dual-task optimization in performance prediction and bottleneck localization for cloud-native microservice systems, this paper proposes a Multi-Modal and Multi-Scale Temporal Propagation model (M[Formula: see text]TP). The model achieves unified representation of logs, time-series metrics, and call-chain data through a Multi-modal Heterogeneous Embedding Module, simultaneously captures dynamic evolutionary patterns and service topological dependencies via a Temporal-Graph Joint Learning Module, and implements joint optimization of performance prediction and bottleneck localization using a Dual-Task Collaborative Decoding mechanism. Experiments on two public datasets, GAIA and PetShop, demonstrate that the M[Formula: see text]TP model achieves [Formula: see text] scores of 0.95 and 0.96 for performance prediction, F1 scores of 0.93 and 0.94 for bottleneck localization, and inference latencies as low as 7.1ms and 6.5ms, respectively, outperforming 8 baseline models including LSTM, Informer, and GAT. Ablation studies validate the effectiveness of each core component, and case studies confirm the model's capability in capturing performance fluctuations and identifying root-cause services accurately. The proposed model can effectively support cloud-native AIOps and provide a solid technical foundation for proactive monitoring and fault diagnosis of microservice systems.
Fan Xu, Wenjie Jiang· Scientific Reports· 0 citations
The rapid expansion of heterogeneous data, machine learning workloads, and concurrent organizational requirements has increased the need for data lake architectures capable of supporting scalable, efficient, and intelligent multi-tenant orchestration. Conventional data lake designs primarily emphasize storage scalability, while comparatively less attention is given to adaptive workload management, tenant-aware resource allocation, exploration–exploitation decisions, and uncertainty-sensitive orchestration. This research develops a conceptual AI-enabled architecture for multi-tenant data lakes by integrating principles from reinforcement learning, multi-armed bandit optimization, dynamic programming, distributional learning, and entropy-based decision processes. The proposed architecture separates data ingestion, tenant isolation, metadata management, intelligent orchestration, resource allocation, and execution layers while using AI-based decision mechanisms to continuously adapt scheduling and resource policies. The theoretical foundation is derived exclusively from the supplied literature, including work on bandit optimization, dynamic programming, reinforcement learning, Monte-Carlo tree search, entropic regularization, and data-oriented learning environments. The architecture is positioned as a framework for improving workload placement, resource efficiency, fairness, and resilience in heterogeneous multi-tenant environments. The analysis indicates that combining adaptive exploration with value-based decision mechanisms can provide a stronger orchestration model than static policies, although computational overhead, training instability, tenant fairness, and limited empirical validation remain important constraints. The research contributes an integrated conceptual model for applying AI-driven decision intelligence to scalable multi-tenant data lake orchestration.
Dr. Faisal Al- Harbi, Dr. Sara Al- Qahtani· Frontiers in Emerging Multid...· 0 citations
A novel hybrid framework that combines a Graph Attention Network (GAT)-based fault prediction model with a Generative Adversarial Network (GAN)-driven task migration decision model is proposed, achieving substantial reductions in task execution time and energy consumption while improving predictive accuracy and resource efficiency.
The rapid growth of artificial intelligence (AI), machine learning, and heterogeneous data sources has increased the need for scalable data-lake architectures capable of supporting concurrent workloads, diverse data types, and dynamically changing computational requirements. Conventional data-management approaches often struggle to provide adequate isolation, resource elasticity, governance, and intelligent workload coordination in multi-tenant environments. This research proposes a conceptual scalable multi-tenant framework for AI-driven big data lake management and processing in which tenant-aware orchestration, workload classification, resource allocation, data governance, and AI-assisted decision mechanisms operate as integrated architectural components. The framework is theoretically grounded in scalable data-lake orchestration, formal reasoning, explainable decision-making, and resource-aware computational management. Particular emphasis is placed on separating tenant-level policies from shared infrastructure while maintaining efficient utilization of storage and processing resources. The architectural rationale is informed by research on rule-based learning, formal verification, satisfiability solving, explainability, and computational reasoning. The proposed framework further incorporates principles associated with multitenant data-lake orchestration for AI workloads (Goyal, 2025). Analytical findings indicate that a policy-aware, AI-driven orchestration layer can improve workload prioritization, resource utilization, isolation, and operational transparency compared with static allocation models. The study also identifies limitations associated with governance complexity, model dependence, computational overhead, and fairness across tenants. The resulting framework provides a research foundation for scalable, intelligent, and explainable big data lake management.
Anh Minh Nguyễn, Nam Hoang Tran· International Journal of Int...· 0 citations
Cloud data migration has become an essential requirement for modern enterprises due to the rapid growth of cloud computing and data-intensive applications. However, achieving reliable and efficient migration remains challenging because of heterogeneous data types, fluctuating network bandwidth, diverse migration tools, and the need to preserve data consistency throughout the migration process. Most existing approaches evaluate migration tools independently, assume static network conditions, and provide limited support for adaptive optimization and verifiable migration outcomes. This paper presents AMF-CloudForge, a unified machine learning-driven framework that integrates migration state analysis, intelligent scheduling, consistency preservation, and real-time adaptive management into a single end-to-end architecture. The proposed framework begins with Polytype Migration State Encoding (PMSE), which performs comprehensive micro-benchmarking across different data types, file sizes, and network conditions to generate Migration State Tensors and Tool Efficiency Scores that accurately characterize migration behavior. These learned representations are then utilized by the Topology-Aware Diffusion Scheduler (TADS) to optimize chunking, routing, and migration tool selection by considering dynamic network topology, bandwidth variations, and transfer costs. To ensure data integrity and consistency, the Causal Delta with Erasure-coded Commitment (CDEC) module combines causal dependency tracking, adaptive erasure coding, and cryptographic quorum verification to guarantee reliable and verifiable data commits without data loss or metadata inconsistencies. Furthermore, the Uncertainty-Quantified Twin Supervisor (UQTS) continuously monitors the migration process through a probabilistic digital twin, enabling risk-aware scheduling adjustments and real-time Service Level Objective (SLO) verification under changing system conditions.Together, these components form a fully automated migration framework capable of policy-driven execution and machine-verifiable migration reporting. Experimental evaluation on heterogeneous cloud workloads demonstrates that the proposed framework reduces overall migration makespan by 35–45%, sustains effective bandwidth utilization above 85%, and limits unrecoverable data loss to approximately 10⁻¹¹ per 10 TB of migrated data. By integrating machine learning-based performance modeling with adaptive scheduling, cryptographic consistency verification, and intelligent runtime supervision, the proposed framework transforms cloud data migration from a static, tool-centric process into a reliable, adaptive, and continuously optimized cloud service.
S. Sapate, G. Pathak· Journal of Intelligent Decis...· 0 citations