An Experimental Evaluation of Machine-to-Machine Coordination Middleware: Extended Version

TL;DR

This paper evaluates two middleware solutions, ZooKeeper and DPWS with Raft, for M2M coordination in IoT, focusing on fault tolerance, scalability, and flexibility in resource-constrained environments.

Contribution

It compares cloud-oriented ZooKeeper with DPWS using Raft consensus for IoT, demonstrating DPWS's suitability for resource-limited M2M scenarios.

Findings

DPWS with Raft provides adequate performance for IoT applications.

DPWS offers greater flexibility compared to ZooKeeper.

ZooKeeper is effective for cloud data centers but less suited for resource-constrained IoT devices.

Abstract

The vision of the Internet-of-Things (IoT) embodies the seam- less discovery, configuration, and interoperability of networked devices in various settings, ranging from home automation and multimedia to autonomous vehicles and manufacturing equipment. As these ap- plications become increasingly critical, the middleware coping with Machine-to-Machine (M2M) communication and coordination has to deal with fault tolerance and increasing complexity, while still abiding to resource constraints of target devices. In this report, we focus on configuration management and coordi- nation of services in a M2M scenario. On one hand, we consider Zoo- Keeper, originally developed for cloud data centers, offering a simple file-system abstraction, and embodying replication for fault-tolerance and scalability based on a consensus protocol. On the other hand, we consider the Devices Profile for Web Services (DPWS) stack with replicated services based on our implementation of the Raft consensus protocol. We show that the latter offers adequate performance for the targeted applications while providing increasing flexibility.