Aug 2026· Journal of ICT Standardization· 0 citations
TL;DR
An interpretable intelligent scheduling framework, where intelligence refers to state-aware, service-aware, energy-aware, and resilience-aware adaptation rather than purely black-box learning, is presented, indicating that intelligent 5G-Advanced slice scheduling is a promising standards-aligned approach for service-differentiated and dependable smart-grid communications.
Abstract
5G-Advanced network slicing is emerging as a promising communication framework for smart-grid services with diverse latency, reliability, bandwidth, and criticality requirements. In smart-grid communication infrastructures, however, slice scheduling must balance service differentiation with energy efficiency, fairness, and resilience under dynamic operating conditions. This paper presents an interpretable intelligent scheduling framework, where intelligence refers to state-aware, service-aware, energy-aware, and resilience-aware adaptation rather than purely black-box learning. The framework jointly considers slice admission, radio-resource allocation, edge-resource allocation, activity-state control, and disturbed-mode adaptation. The problem is formulated as a dynamic multi-objective scheduling problem incorporating delay, reliability, service utility, energy consumption, fairness, and resilience. On this basis, a hierarchical scheduling method is developed for normal, bursty, and degraded operating conditions. Evaluation under representative smart-grid scenarios, including mixed-service operation, demand-response events, distributed energy resource (DER) coordination surges, and degraded-capacity conditions, shows that the proposed method achieves a better overall balance among service-level agreement (SLA) satisfaction, energy efficiency, fairness, and resilience than benchmark strategies. The results indicate that intelligent 5G-Advanced slice scheduling is a promising standards-aligned approach for service-differentiated and dependable smart-grid communications.
Simulation results show that MoReSP achieves the lowest admission-adjusted system cost across all evaluated scenarios, and demonstrate that MoReSP provides a reliable and balanced scheduling solution for dynamic V-IoT environments.
Muhammad Faisal Siddiqui, Adeel Iqbal· Mathematics· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
Smart-grid sensing digital twins require dynamic resource allocation and collaborative scheduling to keep status updates from feeder segments, substation equipment areas, distributed-energy-resource access points, and alarmed devices fresh enough for cyber-physical synchronization. The difficulty is not only transmitting more data, but coordinating limited wireless resource blocks, feasible resource-block occupancy, and edge-computing capacity so that critical grid states are delivered and processed before they become stale. This paper studies hierarchical freshness-aware scheduling using Age of Information (AoI) as the main timeliness metric. Dynamic regional priorities are modeled as inputs supplied by the grid monitoring and event-management system; no external mobility-domain dataset is used to validate smart-grid sensing. The core freshness scheduling method combines value-network-assisted communication-resource budgeting, masked policy-gradient resource-block scheduling, and priority-aware computation offloading, while selective redundancy is treated as an optional enhancement for high-priority tail-risk tasks. The evaluation is conducted using a scenario-based smart-grid simulation covering normal monitoring, localized alarms, concurrent high-priority events, and priority migration. The results show that the core ValueNet-MaskedPG scheduling solver reduces mean priority-weighted AoI by about 12.6–15.9% compared with uniform first-come-first-served scheduling. When selective redundancy is enabled, high-priority-zone freshness is improved in event-driven scenarios, but the benefit for mean and peak AoI is scenario-dependent and comes at the cost of additional computation copies. The results support the usefulness of hierarchical scheduling under the considered scenario-based settings, while field SCADA/PMU or hardware-in-the-loop validation remains necessary before practical deployment.
Bin Guo, Xingxing Feng, Haitong Gu et al.· Energies· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations
—As cloud computing and data centers become integral to global Information Technology (IT) infrastructure, optimizing routing and resource management in these networks is critical for maintaining performance, scalability, and energy efficiency. This paper presents a structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025. Key advancements include adopting Software-Defined Networking (SDN), machine learning-based routing algorithms, and energy-efficient resource allocation strategies. This paper critically analyzes the challenges posed by scalability, latency, energy consumption, security, and interoperability, alongside the opportunities presented by Artificial Intelligence (AI)-driven autonomous networks, edge computing, and the integration of 5G technologies. Comparative evaluation highlights key trade-offs between performance gains and real-world feasibility, particularly for machine learning and deep reinforcement learning approaches. Furthermore, the study examines emerging trends, including cloud-edge collaboration and multi-objective optimization frameworks. The findings reveal that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations. Overall, this review provides a consolidated perspective on current approaches, open challenges, and promising research directions for next-generation cloud and data center network optimization.
S. Alanazi· Journal of Advances in Infor...· 0 citations