When Load Rebalancing Does Not Work for Distributed Hash Table

12/30/2020 ∙ by Yuqing Zhu, et al. ∙ 0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 4

page 5

page 6

page 7

page 8

page 9

page 10

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.