In this paper, we establish a two-timescale framework for the joint optimization for the content placement and content delivery problem in satellite-terrestrial edge computing networks (STECN). Our goal is to optimize content placement to improve network performance while ensuring diverse quality of service (QoS) for content delivery. We decouple the problem into two timescales to balance real-time responsiveness and long-term efficiency. Specifically, considering frequent content placement incurs huge traffic cost, we optimize the content placement in order to reduce resource expenses in large timescales. The optimization problem is formulated as an integer linear programming (ILP) problem to improve both traffic efficiency and cache resource utilization. We leverage a heuristic atom search optimization (ASO) approach to address the problem, which yields an optimal strategy with low computational complexity. In small timescales, we model content delivery as a Markov decision process (MDP) to minimize content delivery delays at small timescales while maintaining smooth network traffic. A deep reinforcement learning (DRL) framework is used for policy learning to dynamically adapt to varying network conditions. By considering the correlation between the small and large timescale optimization, we propose a hierarchical solution to jointly address both issues. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

This paper establishes a dual-timescale framework for joint resource slicing and task scheduling in satellite edge computing (SEC) networks. Specifically, to capture network dynamics and task stochasticity at small timescales, we formulate the task scheduling problem as a Markov decision process (MDP) to minimize task delay, network energy consumption, and packet loss. We design a deep reinforcement learning-assisted task scheduling (DRTS) algorithm inspired by the soft actor-critic (SAC) algorithm to learn the scheduling policy. Task processing performance is affected by communication and computing re-sources allocated to respective resource slices. Thus, considering that frequent resource slicing has a significant management over-head, we further optimize resource slices on a larger timescale. To obtain a policy with low complexity, we propose a greedy-based heuristic algorithm. A hierarchical solution is constructed to find the optimal solution due to the correlation between the two timescale problems. Finally, to validate the effectiveness and superiority of the proposed scheme, extensive simulations are performed.

In this paper, we establish a two-timescale framework for the joint service deployment and task scheduling problem in satellite edge computing networks.We aim to optimize the computing performance of networks with diverse quality-of-service (QoS) guarantees for computing tasks. Specifically, to capture the small-timescale network dynamics and task randomness, we formulate the task scheduling problem as a constrained Markov decision process (CMDP) to minimize the energy consumption, load imbalance and packet loss of networks while ensuring the long-term delay. The Lyapunov technique is employed to deal with the delay constraints. A soft actor-critic (SAC)-based deep reinforcement learning (DRL) framework is designed to learn the stationary scheduling policy. We further explore the significant impact of deploying diverse services on the performance of task scheduling in satellite edge computing. Considering that frequent deployment of services will incur huge deployment overhead, we optimize the service deployment on a larger timescale. The optimization problem is modeled as an integer programming problem to improve the service capability of networks and reduce service deployment costs. A heuristic-based atomic orbital search (AOS) approach is proposed to obtain the superior policy with low complexity. Due to the correlation between the problems of two timescales, a hierarchical solution is constructed to iteratively find the excellent solution. Finally, extensive simulations are conducted to validate the effectiveness and superiority of the proposed scheme.

In this paper, we optimize network computational performance and ensure diverse quality of service (QoS) for tasks by developing a dual-timescale joint optimization framework for satellite-terrestrial integrated edge computing networks (STECN). In our architecture, STECN can handle intelligent tasks for the Internet of remote things (IoRT) devices based on multiple configured applications deployed. Specifically, we formulate task scheduling as a Markov decision process (MDP) to minimize network energy consumption and task processing delay at small timescales. A deep reinforcement learning (DRL) framework is designed for policy learning. Recognizing the impact of resource slicing on task scheduling in STECN and the deployment overhead from frequent changes, we further optimize resource slicing at larger timescales. To enhance network service capability under dynamic demand, we establish a resource slice gap index, characterizing the difference between actual resources and service demand. By a heuristic-based artificial electric field (AEF) approach, we obtain an optimal strategy with low complexity. Considering the correlation between two timescales, the optimal solution is found by iteratively constructing a hierarchical solution. In addition, to guarantee the global load balancing of the network, we introduce a self-attention mechanism, which allows the knowledge of other satellites to be taken into account when slicing the satellite resources. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.