Cloud storage services are becoming increasingly complex with the stored data volume and operational scale. The complexity is the result of the ever-growing components with various functionalities being involved for service rendition, exposing the service to numerous failures or disruptions. In such scenarios, resiliency becomes one of the most crucial parameters to evaluate how well-equipped the system is to withstand the effect of disruptions and maintain a reliable service. The existing evaluations primarily focus on the resiliency of stored user data, which is insufficient to project the storage service level resiliency. This work proposes an end-to-end resiliency framework for cloud storage services that enables the assessment of the overall resiliency. The framework is based on three properties – service expectations, crucial components for service rendition, and their resiliency to meet the expectations while facing various disruptions. The framework is used to model the resiliency of two distinct and well-known storage services, OpenStack Swift and CephFS, as Stochastic Petri Nets. The models enable the effective quantification of system resiliency through the achieved service reliability.

A cloud storage system requires multiple functional and management layers to render a global-scale storage solution. Providing a reliable service through this complex architecture becomes a challenging task. Moreover, the mere complexity of the system makes it difficult to identify the critical components for maintaining a reliable service. While data redundancy has been the predominant factor in improving reliability, it becomes crucial to understand if it is sufficient for the reliability of a cloud storage service. This work proposes a resiliency evaluation method that identifies the components necessary for storage service rendition and the ability of the system to absorb the effect of their failures and minimize the impact on service. Using this method, the resiliency of two distinct cloud storage services, OpenStack Swift and CephFS, are evaluated. The evaluation has revealed that data access requests may get delayed (up to 8x of mean response time) and even fail due to the lack of resiliency for access path components, even when there is enough user data redundancy. The lack of effort is evident while maintaining the consistency of critical internal data components resulting in reduced reliability. Even for stored data, some common failures result in loss of recoverability. The work concludes with some general observations and possible solutions that will help to improve storage service reliability.