Search Results (141)

In IIoT edge computing, multi-edge server collaborative scheduling faces two core issues due to random task arrivals, heterogeneous resources, and complex topology: traditional model-driven methods cannot make dynamic decisions in dynamic environments, and conventional MARL fails to characterize inter-node topological dependencies and load correlations. To address this, this paper investigates the joint optimization of task offloading, computing resource allocation, and SFC orchestration in IIoT, constructs a cloud-edge-end collaborative architecture, and models the problem as a POMDP to minimize the overall system cost under multiple constraints. A graph-guided value-decomposition MARL method is proposed, which extracts spatial topology and neighborhood-load features of edge nodes via a GNN and combines them with the QMIX framework to realize multi-agent centralized training and distributed execution. Simulations show that the algorithm converges stably under different server scales and task loads, significantly outperforms benchmark algorithms, and can suppress performance degradation in high-load scenarios, demonstrating its robustness and scalability in complex industrial environments. Full article

The explosive growth of temporal graph data has led to significant training overheads for Dynamic Graph Neural Networks (DGNNs), a bottleneck primarily driven by massive data movement between host processors and storage arrays across conventional PCIe I/O buses. While near-data processing with Computational Storage Devices (CSDs) can alleviate this bottleneck, a single CSD is inherently incapable of meeting the terabyte-scale capacity requirements and complex sequence modeling demands of modern large-scale DGNNs. Horizontal scaling with multi-CSD clusters over standard PCIe topologies presents a viable, cost-effective solution, yet our in-depth profiling identifies two critical architectural bottlenecks in naive multi-CSD architectures: host-bounced memory copies significantly compromise inter-device communication efficiency, and sparse graph sampling frequently exceeds the capacity of the tightly constrained local DRAM of CSDs, resulting in excessive flash I/O and performance degradation. To address these interconnected bottlenecks, we propose M-DGNN, a hardware–software co-designed architecture optimized for standard PCIe interconnects. First, M-DGNN orchestrates direct peer-to-peer (P2P) DMA dataflows for inter-CSD hidden state exchange, completely bypassing host operating system intervention and reducing the context-switching overhead. Second, we design a host-assisted caching strategy with a Host-Pinned Memory Extension (HPME) mechanism, which leverages host-pinned memory as an asynchronous DMA extension pool to shield resource-constrained CSDs from high-latency flash I/O during structural subgraph sampling. Extensive experimental evaluations across seven large-scale dynamic graph datasets demonstrate that M-DGNN delivers up to a 6.2× end-to-end speedup over the state-of-the-art DGNN systems. This work establishes an efficient, scalable near-data computing paradigm for large-scale DGNN training. Full article

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency. Full article

The emergence of edge computing introduces significant opportunities to improve real-time responsiveness and reduce latency by deploying computational resources closer to end users, at the edge, compared to traditional centralized cloud computing. However, stochastic and fluctuating workloads pose challenges in maintaining Quality of Service, often leading to resource fragmentation, service node saturation, and energy inefficiencies. In addition, imbalances in service node utilization, arising from either under-utilization or over-utilization, degrade the overall system performance and lead to unnecessary operational costs. Furthermore, finding an optimal balance between total latency cost and load balancing in different network topologies remains a significant challenge. In this research, we propose and evaluate a workload-aware orchestration framework that integrates short-term workload forecasting with dynamic resource scaling to efficiently manage edge node infrastructure under dynamic processing demands. The framework employs heuristic schemes that consider both workload distribution and service proximity throughout the edge network to optimize the distribution of edge users’ service requests across service nodes. Simulation results on grid and irregular edge network topologies, utilizing both synthetic and real-world dataset, demonstrate that the proposed framework and the integrated heuristics outperform other benchmark approaches. Specifically, our framework achieves up to 20% lower load imbalance variance, maintains high resource utilization, decreases system reconfigurations and increases service reliability, providing a robust, low-overhead and adaptive solution for dynamic orchestration in edge computing environments and infrastructures. Full article

Manufacturing firms face intensifying pressure to achieve sustainability while remaining competitive under environmental stress, rapid technological change, and institutional uncertainty—challenges that are particularly acute in developing economies. Although Industry 5.0 has emerged as a human-centric and sustainability-oriented industrial paradigm, limited research explains how it can be systematically operationalized to enhance sustainable business performance. This study addresses this gap by developing an integrative conceptual framework linking Industry 5.0, Smart Circular Economy (SCE), and Green Human Resource Management (GHRM) within manufacturing contexts. Drawing on resource-based, dynamic capability, and institutional perspectives, the framework conceptualizes Industry 5.0 as a strategic digital orientation that enables circular resource orchestration and sustainability-aligned human capital systems. SCE and GHRM are positioned as complementary operational mechanisms that translate Industry 5.0 principles into organizational capabilities. Innovation capability is introduced as a mediating dynamic capability explaining how technological and human resource investments generate environmental, social, and economic performance outcomes. Digital maturity and policy support are incorporated as contextual moderators shaping transformation pathways in developing economies. The proposed model advances sustainability-oriented industrial transformation theory by integrating previously fragmented research streams into a coherent socio-technical capability architecture. It also offers actionable insights for managers and policymakers seeking to align digital industrial development with long-term sustainability objectives under conditions of institutional heterogeneity. Full article

Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and distributed edge environments. Moreover, as the number of mobile users, tasks, and distributed computing resources (edge/cloud servers) increases, the task orchestration process becomes more complex due to the expanded decision space and the need to efficiently allocate heterogeneous resources under latency and capacity constraints. As the decision space grows, exhaustive-search-based orchestration becomes computationally infeasible. Clustering approaches often rely on proximity-only grouping, while learning-based solutions require extensive training and parameter tuning. To address these challenges, this paper proposes a Multi-Criteria Hierarchical Clustering-based Task Orchestrator (MCHC-TO), a novel framework that integrates multi-criteria decision making with divisive hierarchical clustering for preference-aware and adaptive workload orchestration. Edge servers are first evaluated using multiple decision criteria, and the resulting preference rankings are exploited to form hierarchical preference-based clusters. Incoming tasks are then assigned to the most suitable cluster based on task requirements, enabling efficient resource utilization and dynamic decision-making. Extensive simulations conducted using an edge computing simulator demonstrate that the proposed MCHC-TO framework consistently outperforms benchmark approaches, achieving reductions in average service delay and task failure rate of up to 48% and 92%, respectively. These results highlight the effectiveness of combining multi-criteria evaluation with hierarchical clustering for robust and dynamic task orchestration in MEC environments. Full article

Global supply chains face intensifying pressures from disruption, regulatory complexity, and sustainability mandates, requiring a shift toward more resilient and adaptive coordination. While artificial intelligence (AI) and blockchain have been recognised as complementary enablers, their implementation remains largely fragmented, existing as isolated tools linked by manual data exchange rather than integrated, programmable logic. This paper addresses this orchestration gap by proposing the Dynamic Resource Orchestration Framework for AI-Blockchain Integrated Supply Chains (DROF-AIBC). Grounded in Resource Orchestration Theory (ROT) and Dynamic Capabilities Theory (DCT), the framework provides a theoretical foundation for the strategic and autonomous orchestration of digital resources. Unlike classic supply chain orchestration, which focuses on the linear coordination of physical assets and legacy systems, DROF-AIBC conceptualises an “intelligent conductor” as a coordination mechanism combining AI-driven analytics and smart contract-based execution. This mechanism supports the configuration, optimisation, and monitoring of resources in response to changing external signals, effectively closing the loop between analytical insights and verifiable execution. The paper further substantiates how this autonomous capability serves as a foundational roadmap for the Industry 5.0 paradigm, embedding human-centricity through Explainable AI (XAI) to provide a “provenance of logic”, promoting circular economy sustainability, and fostering systemic resilience in turbulent environments. The framework aims to provide both a theoretical foundation and a practical roadmap for orchestrating AI and blockchain to advance resilient, sustainable and adaptive supply chains. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty. Full article

Strategic alliances have assumed a pivotal role in the growth and competitiveness of organisations, especially in contexts of rapid technological change and high environmental complexity. Drawing on the Dynamic Capabilities View (DCV), this study aims to analyse the impact of strategic alliance management on technological, marketing, and new product development capabilities, considering the mediating role of dynamic capabilities. This research is based on a sample of 200 Portuguese firms, predominantly SMEs, using Structural Equation Modelling (SEM) to test a conceptual model composed of six hypotheses. The results demonstrate that effective alliance management is positively associated with dynamic capabilities, which in turn function as a pivotal mechanism for integrating and reconfiguring resources. Specifically, the findings reveal that these dynamic capabilities (exploration and exploitation) are fundamental to strengthening marketing and technological skills. Notably, technological capability did not yield a significant direct impact on new-product development, suggesting that in this service-intensive context, marketing capabilities and the overall orchestration of dynamic routines are more critical to innovation success. This research offers empirical evidence of how strategic alliances strengthen the competitiveness of SMEs in peripheral EU economies, highlighting that innovation stems from a configuration of integrative capabilities rather than technological assets alone. Full article

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article

Low Earth orbit (LEO) satellite networks require real-time routing to cope with dynamic topology variations caused by continuous orbital motion. As an alternative to conventional routing approaches, deep reinforcement learning (DRL) has recently gained attention as an effective means for optimizing routing paths. To solve routing problems modeled as a grid-based Markov decision process (grid-based MDP), DRL methods such as CNN-based Dueling DQN have been proposed. However, these approaches are difficult to implement in practice. In particular, the substantial floating-point computation and memory traffic of CNN inference make real-time onboard inference challenging under the stringent power and resource constraints of satellite platforms. To address these constraints, this paper proposes an INT8 quantization and hardware–software co-design framework using heterogeneous SoC FPGA acceleration. We offload compute-intensive CNN inference to the programmable logic (PL), while the processing system (PS) orchestrates overall control and data movement, forming a collaborative PS–PL architecture. Furthermore, we integrate the NITI-style two-pass scaling with PS–PL exponent propagation to preserve end-to-end integer consistency without floating-point conversion. To demonstrate its practical onboard feasibility, we employ standard accelerator implementation choices—such as output-stationary scheduling and on-chip prefetching—and conduct an ablation study over independently tunable axes (PE array size and PS-side buffer reuse) to quantify their incremental contributions. Experimental results show that the proposed PS–PL cooperative scheme dramatically reduces computation time compared to a PS-only reference implementation on the same platform. Full article

As network slicing gains traction in cloud computing environments, efficient management and orchestration systems are required to realize the benefits of this technology. These systems must enable dynamic provisioning and resource optimization of virtualized services spanning multiple network slices. Nevertheless, the common resource overprovisioning practice implemented by service providers leads to the inefficient use of resources, limiting the ability of Mobile Network Operators (MNOs) to rent new network slices to more vertical customers. Hence, efficient resource allocation mechanisms are essential to achieve optimal network performance and cost-effectiveness. This paper proposes a predictive model for network slice resource optimization based on resource sharing between Virtualized Network Functions (VNFs). The model employs deep learning models based on Long Short-Term Memory (LSTM) and Transformers for CPU resource usage prediction and a reactive algorithm for resource sharing between VNFs. The model is powered by a telemetry system proposed as an extension of the 3GPP network slice management architectural framework. The extended architectural framework enhances the automation and optimization of the network slice lifecycle management. The model is validated through a practical use case, demonstrating the effectiveness of the resource sharing algorithm in preventing VNF overload and predicting resource usage accurately. The findings demonstrate that the sharing mechanism enhances resource optimization and ensures compliance with service level agreements, mitigating service degradation. This work contributes to the efficient management and utilization of network resources in 5G networks and provides a basis for further research in network slice resource optimization. Full article

As a sustainability-oriented mode of education, cross-border digital education has distinct advantages, including a low carbon footprint associated with decreased student and staff commute times and expanded accessibility for disadvantaged learners. However, the intrinsic mechanisms by which globally mobile talent, including international students and transnational professionals, utilize their global skills and networks to create sustainable EdTech entrepreneurial initiatives need further investigation. Based on dynamic capability theory and resource orchestration logic, this study examines how human and social capital shape entrepreneurial engagement through resource integration capability (RIC) via PLS-SEM analysis of data collected from 318 transnationally mobile actors. The study finds that neither form of capital has a direct association on entrepreneurial entry; instead, both are associated with entrepreneurial entry indirectly through RIC, allowing mobile talent to combine and allocate knowledge, networks, and digital technologies across institutional and cultural boundaries. The study examines how cross-border EdTech entrepreneurship works towards creating inclusive and equitable quality education, as well as global partnerships, through scalable, adaptable, and low-carbon educational services, while meeting objectives 4 and 17 of the UN Sustainable Development Goals. This study reveals the transformation process centered around RIC, highlighting the need to create innovative ecosystems that transition from talent attraction to talent empowerment. The findings underline the importance of RIC in translating global mobility into sustainable digital education solutions. Full article