Coded Data Rebalancing for Distributed Data Storage Systems with Cyclic Storage

TL;DR

This paper introduces efficient coded rebalancing schemes for cyclic balanced distributed storage systems, significantly reducing communication load during node changes with manageable file sizes, compared to previous exponential-size schemes.

Contribution

It presents novel coded rebalancing schemes for cyclic balanced databases with cubic file-size requirements, improving over prior exponential schemes, and provides bounds showing near-optimal performance.

Findings

Coded schemes reduce communication load during node removal.

Uncoded scheme is optimal for node addition.

Achievable loads are within a multiplicative gap of the lower bound.

Abstract

We consider replication-based distributed storage systems in which each node stores the same quantum of data and each data bit stored has the same replication factor across the nodes. Such systems are referred to as balanced distributed databases. When existing nodes leave or new nodes are added to this system, the balanced nature of the database is lost, either due to the reduction in the replication factor, or the non-uniformity of the storage at the nodes. This triggers a rebalancing algorithm, that exchanges data between the nodes so that the balance of the database is reinstated. The goal is then to design rebalancing schemes with minimal communication load. In a recent work by Krishnan et al., coded transmissions were used to rebalance a carefully designed distributed database from a node removal or addition. These coded rebalancing schemes have optimal communication load, however, require the file-size to be at least exponential in the system parameters. In this work, we consider a cyclic balanced database (where data is cyclically placed in the system nodes) and present coded rebalancing schemes for node removal and addition in such a database. These databases (and the associated rebalancing schemes) require the file-size to be only cubic in the number of nodes in the system. We bound the advantage of our node removal rebalancing scheme over the uncoded scheme, and show that our scheme has a smaller communication load. In the node addition scenario, the rebalancing scheme presented is a simple uncoded scheme, which we show has optimal load. Finally, we derive a lower bound for the single node-removal rebalancing for the specific choice of data placements specified by our achievable rebalancing schemes, and show that our achievable rebalancing loads are within a multiplicative gap from the lower bound obtained.