  Distributed hash table (DHT) is the foundation of many widely used storage
systems, for its prominent features of high scalability and load balancing.
Recently, DHT-based systems have been deployed for the Internet-of-Things (IoT)
application scenarios. Unfortunately, such systems can experience a breakdown
in the scale-out and load rebalancing process. This phenomenon contradicts with
the common conception of DHT systems, especially about its scalability and load
balancing features. In this paper, we investigate the breakdown of DHT-based
systems in the scale-out process. We formulate the load rebalancing problem of
DHT by considering the impacts of write workloads and data movement. We show
that, the average network bandwidth of each node and the intensity of the
average write workload are the two key factors that determine the feasibility
of DHT load rebalancing. We theoretically prove that load rebalancing is not
feasible for a large DHT system under heavy write workloads in a node-by-node
scale-out process.
