Low Earth orbit (LEO) satellites enable complex Earth observation tasks (e.g.,remote sensing and cooperative monitoring) by leveraging large-scale satellite-generated Earth imageries and state-of-the-art machine learning (ML) techniques. However, due to restricted downlink bandwidth and spotty connectivity, it is infeasible for the satellites to transmit all the imageries to ground stations for ML model training. To address this issue, we use federated learning (FL) to mitigate the significant overhead of raw data transmission only by enabling model parameter exchange. Traditional FL requires a central server for model parameter aggregation, which is impractical for distributed LEO satellite constellation due to the difficulty of identifying a suitable central satellite. To tackle such challenge, we take the unique topological characteristics of the LEO satellite constellation to design a decentralized FL framework that enables efficient model aggregation in LEO satellite networks without a central server. The framework can avoid the reliability and communication bandwidth problems of the central server in centralized FL. To mitigate the straggler effect and address the statistical heterogeneity, we then propose a novel offloading framework for decentralized FL in LEO satellite networks to aid the collaboration among multiple satellites for resource sharing. Based on it, we derive a satellite-centric threshold-based offloading strategy and a system-wide greedy-based iterative offloading decision making algorithm, in order to achieve delay and accuracy optimization under the computation and communication power constraints. Theoretical analysis demonstrates that the proposed framework contributes to the high training performance of the global model. Extensive experiments based on realistic datasets show that the proposed framework can reduce the system delay by up to 41% on average and improve the global model accuracy by up to 9.39% compared with benchmark policies.

With the rapid development of the Internet, more and more computing-intensive applications with high requirements on computing delay and energy consumption have emerged. Recently, the use of mobile-edge computing servers for auxiliary computing is considered as an effective way to reduce latency and energy consumption. In addition, applications such as autonomous driving will generate a large number of repetitive tasks. Using a cache to store the computational results of popular tasks can avoid the overhead caused by repetitive processing. In this article, we study the problem of computation offloading for users in multiple regions. The optimization goal is expressed as choosing offloading strategies and caching strategies to minimize the total delay and energy consumption of all regions. We first use the deep reinforcement learning (DRL) deep deterministic policy gradient (DDPG) framework to solve the problem of computational offloading in a single region. We also show the inefficiency of existing collaborative caching approaches in multiple regions, and propose a new collaborative caching algorithm (CCA) to improve the overall cache hit rate of the system. Finally, we integrate the DDPG and CCA algorithms to form a holistic efficient caching and offloading strategy for all regions. The simulation results show that the proposed algorithm can significantly improve the cache hit rate, and has an excellent performance in reducing the total system overhead.

Summary With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M‐All and M‐Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight‐based algorithm (M‐Weight) and a mobility prediction–based heuristic algorithm (M‐Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.

Live Virtual Machine (VM) migration offers a couple of benefits to cloud providers and users, but it is limited within a data center. With the development of cloud computing and the cooperation between data centers, live VM migration is also desired across data centers. Based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration framework called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central base image repository, live VM migration and further performance optimizations are made possible. The results from an extensive experiment show that CBase is able to support VM migration efficiently, outperforming existing solutions in terms of total migration time and network traffic.

Live Virtual Machine (VM) migration across data centers is an important support technology which will bring benefits to both cloud providers and users. At present, VM storage migration becomes the bottleneck of this technology as it explores only the static data feature of image files without much consideration on data semantics. In this paper, we propose a new space-efficient VM image structure-three-layer structure. According to different functions and features, the data of a VM are separated into an Operating System (OS) layer, a Working Environment (WE) layer, and a User Data (UD) layer. On basis of three-layer image structure, we design a novel VM storage migration system-LayerMover. It tries to improve the migration performance through optimizing current data deduplication methods. To evaluate the potential performance gains of LayerMover, we explore the similarity features of WE layer data. Our experimental results show that the similarity ratio between different images can reach 70%. Our experiments about the VM storage migration performance of LayerMover show that it can be 27% faster than current migration methods for twolayer image structures under our experimental environments. When migrating a VM in a second round or migrating a batch of VMs, LayerMover will see more performance improvement.