BatchIT: Intelligent and Efficient Batching for IoT Workloads at the Edge

Next-generation stream processing systems for community scale IoT applications must handle complex non-functional needs, e.g. scalability of input, reliability/timeliness of communication and privacy/security of captured data. In many IoT settings, efficiently batching complex workflows remains challenging in resource-constrained environments. High data rates, combined with real-time processing needs for applications, have pointed to the need for efficient edge stream processing techniques. In this work, we focus on designing scalable edge stream processing workflows in real-world IoT deployments where performance and privacy are key concerns. Initial efforts have revealed that privacy policy execution/enforcement at the edge for intensive workloads is prohibitively expensive. Thus, we leverage intelligent batching techniques to enhance the performance and throughput of streaming in IoT smart spaces. We introduce BatchIT, a processing middleware based on a smart batching strategy that optimizes the trade-off between batching delay and the end-to-end delay requirements of IoT applications. Through experiments with a deployed system we demonstrate that BatchIT outperforms several approaches, including micro-batching and EdgeWise, while reducing computation overhead.