With the rapid evolution of artificial intelligence (AI) technologies, supporting computing-intensive and latencysensitive applications in resource-constrained environments has become increasingly challenging. In response, we propose APPOMNLS, a multi-objective optimization approach for Satellite Edge Computing (SEC) that targets application response latency, energy consumption, and on-time completion rates. It integrates a proximal policy optimization (PPO) with a self-attention mechanism under a multi-neighborhood local search framework. The Transformer-based self-attention module enhances the PPO network's representational capability, while multi-neighborhood local search switches flexibly between global and local exploration of the solution space. Experiments based on Iridium-NEXT constellation Two-Line Element (TLE) data demonstrate that our approach clearly outperforms its peers in terms of terminal response speed, energy efficiency and on-time application completion rates. APPO-MNLS brings value to SEC with the capability of guaranteeing reliable and effective global satellite network service.

As massive data is generated by Internet of Things (IoT) devices, user-end devices are required to implement computation-intensive functionalities, including multi-sensory data processing and analysis, sophisticated system control schemes, and artificial intelligence. Mobile Edge Computing (MEC) is a significant technology that has the potential to extend the computation and storage capacities of user-end devices by the decentralization of required resources and contents near users and at the edge. A crucial challenge in this direction is the development of a smart mechanism to effectively cache contents upon Edge Servers (ESs) near users for high effectiveness and low latency of content delivery with the constraints on computational and storage capacities of ESs. This study employs a queuing model for analyzing total request delay and interprets the content caching problem as an adversarial semi-bandits problem. We propose an Online Self-feedback Adversarial Semi-bandits Learning (OSAL) algorithm that incorporates a dual-layer learning architecture for dynamically generating caching strategies and maximizes the long-term reward. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods across various performance metrics in a real-world multi mobile-user content caching case.

The growing need for applications that require significant computational power and high responsiveness has significantly driven the advancement of multi-access edge computing (MEC), with satellite edge computing (SEC) emerging as a formidable solution for regions where devices are marooned in areas with sparse computational resources. We present a study on enhancing task scheduling and resource allocation efficiency under the SEC framework, introducing a novel system model that simulates a heterogeneous network characterized by variable bandwidths, channel gains, and transmission powers. We propose a tailored SEC architecture that addresses stringent latency requirements and devise a dynamic scheduling method that adjusts task priorities based on urgency. Our experiments, grounded in realistic parameters from the Iridium and OneWeb satellite constellations, demonstrate the efficacy of our algorithm. The findings underscore significant improvements in managing the SEC landscape, providing robust solutions that enhance overall system performance and reliability in global service networks.