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.

Exploiting Cloud Computing and Web Services to Achieve Data Consistency, Availability, and Partition Tolerance in the Large-Scale Pervasive Systems

This article presents a new comprehensive approach to realize a sufficient trade-off between the CAP properties (i.e., consistency, availability, and partition tolerance) in the large-scale pervasive information systems. To achieve these critical properties, the capabilities of both cloud computing and web services were exploited in developing the components of the proposed approach. These components include a cloud-based replication architecture for ensuring high data availability and achieving partition tolerance, a web services-based middleware for maintaining the eventual consistency, and a data caching scheme to enable the mobile computing elements to conduct update transactions during the disconnection periods. The evaluation of the performance aspects revealed that the proposed approach is able to achieve a load balance, lower propagation delay, and higher cache hit ratio, as compared to other baseline approaches.