Mobile edge systems require efficient resource synchronization to support coordination in dynamic, infrastructure-less environments, where traditional cloud-based approaches incur high overhead and limited robustness. We present PrudentCaster, a tunable decentralized broadcast gossip framework that improves synchronization efficiency by minimizing redundant transmissions. The key idea is to guide forwarding using a local, MLST-inspired structure, enabling selective dissemination while preserving high coverage without global coordination. We further introduce a unified metric that captures trade-offs among coverage, redundancy, latency, and bandwidth. Evaluation in both simulation and a small-scale robotic testbed shows that PrudentCaster improves synchronization efficiency by up to 1.6 × in real deployments and up to 6 × in large-scale scenarios.

Large-scale scenarios, such as drone-based search and rescue, often require seamless access to devices distributed across hierarchical edge clusters. However, existing multi-cluster communication solutions designed for cloud environments cannot be directly applied to edge environments due to resource constraints, network limitations, and privacy concerns. To address these challenges, this paper introduces EdgeConnector, a lightweight middleware specifically designed to enable seamless and efficient cross-cluster device access in edge environments. EdgeConnector consists of components deployed across superior and subordinate clusters and employs a compact mechanism leveraging eXpress Data Path (XDP) for highly efficient device communication between clusters. The middleware was evaluated in both real-world and simulated environments. A real-world case study demonstrates its practicality and usability, while experimental results from the simulated environment highlight its superior performance. Specifically, EdgeConnector achieves an 80% reduction in latency and a 90% reduction in CPU usage under high-load conditions compared to the leading existing solution for cross-cluster service access.

Microservice architecture design highly relies on expert experience and may often result in improper service decomposition. Moreover, a microservice architecture is likely to degrade with the continuous evolution of services. Architecture measurement is thus important for the long-term evolution of microservice architectures. Due to the independent and dynamic nature of services, source code analysis based approaches cannot well capture the interactions between services. In this paper, we propose a trace analysis based microservice architecture measurement approach. We define a trace data model for microservice architecture measurement, which enables fine-grained analysis of the execution processes of requests and the interactions between interfaces and services. Based on the data model, we define 14 architectural metrics to measure the service independence and invocation chain complexity of a microservice system. We implement the approach and conduct three case studies with a student course project, an open-source microservice benchmark system, and three industrial microservice systems. The results show that our approach can well characterize the independence and invocation chain complexity of microservice architectures and help developers to identify microservice architecture issues caused by improper service decomposition and architecture degradation.