A Latency-Efficient NF Caching and Offloading Strategy for LEO Satellite Edge Computing

Joint Task Offloading and Resource Allocation with Data Caching in UAV-Aided Mobile Edge Computing Networks for Latency-Sensitive Applications

Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.

SCOPE: Spatio-Temporal Collaborative Caching and Proactive Transfer in LEO Satellite Networks

Integrating CDNs into LEO constellations reduces latency but faces fundamental challenges due to high satellite mobility and limited onboard storage. Existing isolated caching strategies fail to cope with topology changes, leading to the cache island effect that degrades hit ratios and overloads Ground-Satellite Links (GSLs). To address this, we propose SCOPE, a Spatio-Temporal Collaborative Caching and Proactive Transfer framework. Spatially, SCOPE organizes satellites into localized collaborative groups using a latency constrained maximal-clique search to ensure intra-group reachability. Furthermore, a uniform partition strategy is applied to resolve overlaps and enforce size constraints, ensuring unique assignment and load balancing. Temporally, to ensure service continuity, SCOPE employs an orbit-aware proactive transfer mechanism. By evaluating Value Density (popularity normalized by size), high-value content is migrated from departing to entering satellites before physical links break. Extensive simulations with Starlink traces demonstrate that SCOPE significantly improves cache hit ratios and reduces latency, while offloading GSL backhaul traffic by over 50% compared to baselines.

Low-Energy Resource Optimization and Task Assignment for Satellite Edge Computing Networks

Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The dynamic network topology and time-varying resource availability in satellite constellations further degrade the quality and stability of SEC services. To address these challenges, we propose a deep learning-based Collaborative Image Feature-extraction Task Optimization (CIFTO) framework. CIFTO dynamically distributes image processing workloads across multiple Low Earth Orbit (LEO) satellites, enabling continuous temporal updates for task allocation while significantly accelerating convergence and reducing computational overhead. By integrating temporal modeling and iterative optimization, CIFTO effectively mitigates the NP-hard nature of satellite task allocation. Furthermore, a lightweight satellite image processing model is designed to meet the strict constraints of on-orbit computation, achieving efficient image inference with minimal parameters. Extensive experimental evaluations demonstrate that the proposed framework ensures timely task completion, substantially lowers system-wide energy consumption, and enhances the adaptability and training efficiency of SEC services.

CHIME: Cost-Constrained Hybrid Popularity-Aware Intelligent Service Caching Framework for MEC

Mobile edge computing (MEC) has emerged as a promising paradigm to support latency-sensitive applications by deploying computing and storage resources closer to end users. Service caching at the network edge is an effective approach to further reduce response latency and improve quality of service (QoS). However, the increasing scale and dynamic nature of user requests pose significant challenges to efficient service caching and updating under limited edge resources and cost budgets. In this article, we investigate the joint optimization problem of service caching and dynamic updating in a cloud–edge–end collaborative architecture, with the objective of minimizing the average service access latency of users. We first construct a comprehensive system model that captures communication latency, service response delay, and the costs associated with service caching and updates. The problem is formulated as a nonlinear integer programming problem and addressed via a two-stage solution framework. Specifically, in the initial caching stage, we employ Lagrangian relaxation to reduce the high-dimensional knapsack problem and design an efficient algorithm integrated with Tabu Search. In the dynamic updating stage, we develop a Monte Carlo tree search (MCTS)-based algorithm enhanced by service popularity awareness, along with a novel service replacement strategy that considers user preference and service reliability. Extensive experiments under various system scales demonstrate that the proposed approach significantly outperforms state-of-the-art methods in terms of average service latency, cost budget satisfaction, and update efficiency.

An Intelligent Content Caching for NDN with Communication-Efficient State Sharing

Edge Computing Control and Task Assignment in Integrated Terrestrial and Non-Terrestrial Networks

Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.