Characterizing Container Performance in Edge Computing

Edge computing reduces latency and network congestion by offloading computation from the cloud to edge devices. However, deploying software across heterogeneous edge platforms presents portability and dependency challenges. Containerization with Docker offers lightweight isolation but introduces overheads on resource-constrained devices. In this work, we empirically evaluate Docker performance on Raspberry Pi nodes using microbenchmarks (CPU, memory, network) and macrobenchmarks including AI workloads with sensor/camera interaction, compression, and encryption tasks. Our measurements highlight key limitations: insecure sensor access, spin-up delays for short tasks, and isolation-performance tradeoffs. These insights inform container use for stable, long-running edge workloads and guide future tuning for real-time, resource-constrained environments.