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.

Mobile Edge Computing (MEC) provides users with low-latency and highly responsive services by deploying Edge Stations (ESs) close to applications. However, the limited battery life, computational power, and communication transmission ca-pabilities of mobile devices make it impractical to deploy compu-tation tasks solely on a single node. Instead, dynamic allocation of computational resources according to user mobility is considered to be an effective solution. To address the challenge, we develop a user mobility-aware and destination prediction-based method, SASpre, for edge service preallocation. Experiments demonstrate that SASpre beats its peers in terms of user coverage rates, service times, and service responsiveness.

Mobile edge computing is an emerging computing paradigm boosting resource-demanding and delay-sensitive applications through deploying computing infrastructures at the edge of the Internet nearby mobile requesters and users. In an Internet of Vehicles (IoV) environment, Vehicular Edge Computing (VEC) is capable of exploiting network edge devices, in terms of, e.g., Roadside Units (RSUs), for predictive content caching for optimizing quality-of-experience (QoE) of nearby content requesters based on content popularity analysis, it remains a great challenge to accurately predict content popularity of mobile requesters and appropriately cache required content with low miss rate accordingly in a VEC environment with high user mobility and dynamics. To address this challenge mentioned above, in this paper, we propose predictive content popularity-aware approach, i.e., KM_SVD++, to edge caching in an VEC environment. The proposed approach is capable of achieving high hit rate of mobile content requestors in VEC and low latency of content delivery by leveraging a Kalman filtering model for predicting locations of vehicles and a SVD++ one for yielding decisions for cache deployment and replacement. We conduct extensive simulations as well to prove its effectiveness.

Mobile edge computing(MEC) is an innovative technology that deploys computing resources around the demand side to provide near-request and responsiveness-guaranteed computing and storage services. A major attention paid by related works in this direction is mobility, where mobile traces of both edge users and servers are analyzed and exploited for accommodating offloading and migration requests for computation resources in a highly dynamic MEC environment. Our research in this work suggests that information of user interests, in terms of points of interest (POI), can be exploited in conjunction with mobility as well and proposes a hybrid method for for interest-informed and mobility-aware service migration path selection(HIMS). It synthesizes a trajectory prediction model and user interests prediction one for selecting target servers and reliable service migration paths. Experimental results demonstrate that our approach outperforms traditional methods across multiple performance metrics, especially those with sole input of mobility.

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.

The mobile edge computing (MEC) paradigm places traditional digital infrastructure next to mobile networks and thus drives substantial improvements in performance and latency for mobile computing cases like gaming, video streaming, and IoT. However, it remains a great challenge to provide a effictive and performance guaranteed strategies for services offloading and migration in the MEC environment. Most existing solutions in this direction tend to consider task offloading as a offline decision making process by employing transient positions of users as model inputs. In this work instead, we consider a predictive-trajectory-aware task offloading strategy called PreMig. Simulations clearly demonstrate that our proposed strategy outperforms traditional ones in terms of effective service rate and migration overhead.

Recently, the vehicular edge computing (VEC) paradigm becoming an emerging solution for offloading computation-intensive tasks in the vehicular environment. However, pure edge resources can be limited and insufficient when vehicles and users are in great numbers. Thus, intelligent and efficient task deployment strategies for hybrid and layered edge infrastructures are in high need. In this paper, we propose a novel deployment approach for vehicular applications with multiple interdependent tasks in a hybrid three-layer edge computing infrastructure. We consider that each application can be divided into multiple interdependent tasks, and tasks can be deployed to different layers for execution. We propose an efficient multiple tasks deploying algorithm (MTDA) for yielding high-quality deployment solutions through prioritizing applications for meeting deadline constraints and tasks for meeting dependency constraints and simulative results clearly demonstrate that our proposed method outperforms traditional ones in terms of average application completion time and deadline meeting rate.