When Load Rebalancing Does Not Work for Distributed Hash Table

TL;DR

This paper investigates why load rebalancing fails in large distributed hash table systems during scale-out, especially under heavy write workloads, challenging the assumption of inherent scalability and load balancing in DHTs.

Contribution

It formulates the load rebalancing problem considering write workloads and network factors, and proves that rebalancing is infeasible in large DHTs under heavy write conditions.

Findings

Load rebalancing feasibility depends on network bandwidth and write workload intensity.

Rebalancing fails in large DHTs with heavy write workloads during scale-out.

Theoretical proof of infeasibility of load rebalancing under certain conditions.

Abstract

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.