A Data-Assisted Reliability Model for Carrier-Assisted Cold Data Storage Systems

TL;DR

This paper introduces a reliability model for carrier-assisted cold data storage systems, accounting for carrier aging and providing bounds on system availability, with real data used for lifetime prediction.

Contribution

It develops a generalized non-homogenous Markov model incorporating carrier aging and offers practical bounds on system availability for cold storage.

Findings

Carrier presence significantly impacts system reliability.

Timely maintenance of carriers enhances long-term availability.

Field data enables accurate lifetime prediction of storage carriers.

Abstract

Cold data storage systems are used to allow long term digital preservation for institutions' archives. The common functionality among cold and warm/hot data storage is that the data is stored on some physical medium for read-back at a later time. However in cold storage, write and read operations are not necessarily done in the same exact geographical location. Hence, a third party assistance is typically utilized to bring together the medium and the drive. On the other hand, the reliability modeling of such a decomposed system poses few challenges that do not necessarily exist in other warm/hot storage alternatives such as fault detection and absence of the carrier, all totaling up to the data unavailability issues. In this paper, we propose a generalized non-homogenous Markov model that encompasses the aging of the carriers in order to address the requirements of today's cold data storage systems in which the data is encoded and spread across multiple nodes for the long-term data retention. We have derived useful lower/upper bounds on the overall system availability. Furthermore, the collected field data is used to estimate parameters of a Weibull distribution to accurately predict the lifetime of the carriers in an example scale-out setting. In this study, we numerically demonstrate the significance of carriers' presence and the key role that their timely maintenance plays on the long-term reliability and availability of the stored content.