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Analysis of the age of data in data backup systems

Apoorv Saxena (UGent) , Dieter Claeys (UGent) , Herwig Bruneel (UGent) and Joris Walraevens (UGent)
(2019) COMPUTER NETWORKS. 160. p.41-50
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Abstract
Cloud infrastructures are becoming a common platform for storage and workload operations for industries. With increasing rate of data generation, the cloud storage industry has already grown into a multi-billion dollar industry. This industry offers services with very strict service level agreements (SLAs) to insure a high Quality of Service (QoS) for its clients. A breach of these SLAs results in a heavy economic loss for the service provider. We study a queueing model of data backup systems with a focus on the age of data. The age of data is roughly defined as the time for which data has not been backed up and is therefore a measure of uncertainty for the user. We precisely define the performance measure and compute the generating function of its distribution. It is critical to ensure that the tail probabilities are small so that the system stays within SLAs with a high probability. Therefore, we also analyze the tail distribution of the age of data by performing dominant singularity analysis of its generating function. Our formulas can help the service providers to set the system parameters adequately. (C) 2019 Elsevier B.V. All rights reserved.
Keywords
Computer Networks and Communications, Data backup, Age of data, Queueing model, Tail distribution, Dominant singularity analysis

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MLA
Saxena, Apoorv, et al. “Analysis of the Age of Data in Data Backup Systems.” COMPUTER NETWORKS, vol. 160, Elsevier, 2019, pp. 41–50, doi:10.1016/j.comnet.2019.05.020.
APA
Saxena, A., Claeys, D., Bruneel, H., & Walraevens, J. (2019). Analysis of the age of data in data backup systems. COMPUTER NETWORKS, 160, 41–50. https://doi.org/10.1016/j.comnet.2019.05.020
Chicago author-date
Saxena, Apoorv, Dieter Claeys, Herwig Bruneel, and Joris Walraevens. 2019. “Analysis of the Age of Data in Data Backup Systems.” COMPUTER NETWORKS 160: 41–50. https://doi.org/10.1016/j.comnet.2019.05.020.
Chicago author-date (all authors)
Saxena, Apoorv, Dieter Claeys, Herwig Bruneel, and Joris Walraevens. 2019. “Analysis of the Age of Data in Data Backup Systems.” COMPUTER NETWORKS 160: 41–50. doi:10.1016/j.comnet.2019.05.020.
Vancouver
1.
Saxena A, Claeys D, Bruneel H, Walraevens J. Analysis of the age of data in data backup systems. COMPUTER NETWORKS. 2019;160:41–50.
IEEE
[1]
A. Saxena, D. Claeys, H. Bruneel, and J. Walraevens, “Analysis of the age of data in data backup systems,” COMPUTER NETWORKS, vol. 160, pp. 41–50, 2019.
@article{8617243,
  abstract     = {{Cloud infrastructures are becoming a common platform for storage and workload operations for industries. With increasing rate of data generation, the cloud storage industry has already grown into a multi-billion dollar industry. This industry offers services with very strict service level agreements (SLAs) to insure a high Quality of Service (QoS) for its clients. A breach of these SLAs results in a heavy economic loss for the service provider.
We study a queueing model of data backup systems with a focus on the age of data. The age of data is roughly defined as the time for which data has not been backed up and is therefore a measure of uncertainty for the user. We precisely define the performance measure and compute the generating function of its distribution. It is critical to ensure that the tail probabilities are small so that the system stays within SLAs with a high probability. Therefore, we also analyze the tail distribution of the age of data by performing dominant singularity analysis of its generating function. Our formulas can help the service providers to set the system parameters adequately. (C) 2019 Elsevier B.V. All rights reserved.}},
  author       = {{Saxena, Apoorv and Claeys, Dieter and Bruneel, Herwig and Walraevens, Joris}},
  issn         = {{1389-1286}},
  journal      = {{COMPUTER NETWORKS}},
  keywords     = {{Computer Networks and Communications,Data backup,Age of data,Queueing model,Tail distribution,Dominant singularity analysis}},
  language     = {{eng}},
  pages        = {{41--50}},
  publisher    = {{Elsevier}},
  title        = {{Analysis of the age of data in data backup systems}},
  url          = {{http://dx.doi.org/10.1016/j.comnet.2019.05.020}},
  volume       = {{160}},
  year         = {{2019}},
}
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