On Optimizing Replica Migration in Distributed Cloud Storage Systems

TL;DR

This paper introduces CRANE, a novel scheme for efficient replica migration in distributed cloud storage, reducing migration time and network traffic while maintaining data availability.

Contribution

CRANE is a new replica migration scheme that optimizes data transfer, minimizes network congestion, and integrates with existing placement algorithms in cloud storage systems.

Findings

Reduces replica creation time by up to 30%.

Decreases inter-data center network traffic by 25%.

Maintains minimal data availability during migration.

Abstract

With the wide adoption of large-scale Internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand, thanks to its seemingly limitless capacity, high availability and faster access time. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica placement systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures by (1) minimizing the time needed to copy the data to the new replica location, (2) avoiding network congestion, and (3) ensuring a minimal availability of the data. Our results show that, compared to swift (the OpenStack project for managing data storage), CRANE is able to minimize up to 30% of the replica creation time and 25% of inter-data center network traffic, while ensuring the minimum required availability of the data.