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End-to-end Resiliency Analysis Framework for Cloud Storage Services

Ghosh, A and Lakshmi, J (2023) End-to-end Resiliency Analysis Framework for Cloud Storage Services. In: UNSPECIFIED, 24 October 2023-27 october 2023, Bangalore, India, pp. 134-141.

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Official URL: https://doi.org/10.1109/PRDC59308.2023.00025


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. © 2023 IEEE.

Item Type: Conference Paper
Publication: Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society.
Keywords: Cloud storage; Petri nets; Stochastic models, Analysis frameworks; Cloud storage services; Crucial parameters; Data volume; End to end; Operational scale; Resiliency; Stochastic Petri Nets; Storage services; User data, Stochastic systems
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 04 Mar 2024 10:09
Last Modified: 04 Mar 2024 10:09
URI: https://eprints.iisc.ac.in/id/eprint/84059

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