A Collaborative Cache Strategy in Satellite-Ground Integrated Network Based on Multiaccess Edge Computing

Multiaccess edge computing (MEC) provides users with a network environment and computing storage capacity at the edge of the network, ensuring a deterministic service with low delivery delay. This paper introduces a new satellite-ground integrated collaborative caching network architecture based on MEC and studies the caching strategy. On the ground side, the edge nodes (ENs) are deployed to the user side to form a hierarchical collaborative cache mode centered on the base station. On the satellite side, we utilize intelligent satellite ENs to precache and multicast the highly popular contents, reducing the initial content delivery delay. Under the constraints of the user demand and storage capacity, we study the deployment and cache scheme of ENs and establish the delivery delay minimization problem. To solve the problem, we propose a content update decision parameter for content cache update and transform the problem into improving the hit rate of ENs. Simulation results show that the proposed MEC network architecture and content caching scheme can increase the caching system hit rate to 64% and reduce the average delay by 32.96% at most.

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive services, ensemble learning-based services can, in natural, leverage the distributed computation and storage resources at edge devices to achieve efficient data collection, processing, and analysis. Collaborative caching has been applied in edge computing to support services close to the data source, in order to take the limited resources at edge devices to support high-performance ensemble learning solutions. To achieve this goal, we propose an adaptive in-network collaborative caching scheme for ensemble learning at edge. First, an efficient data representation structure is proposed to record cached data among different nodes. In addition, we design a collaboration scheme to facilitate edge nodes to cache valuable data for local ensemble learning, by scheduling local caching according to a summarization of data representations from different edge nodes. Our extensive simulations demonstrate the high performance of the proposed collaborative caching scheme, which significantly reduces the learning latency and the transmission overhead.