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Cloud data storage : a queueing model with thresholds

Apoorv Saxena (UGent) , Dieter Claeys (UGent) , Bo Zhang and Joris Walraevens (UGent)
(2020) ANNALS OF OPERATIONS RESEARCH. 293(1). p.295-315
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Abstract
In the past decade, cloud platforms have become a standard across the industry for data storage and operations. Such platforms offer high quality of service in terms of reliability and ease of setup at an effective cost. With exponentially high rates of increase of data storage requirements, data is now increasingly stored in clouds. However, there are limited studies which analyze the processes performing the storage operations. Queueing models offer a very natural way of modeling these storage processes. The data packets waiting for storage form a queue which is served by a storage server. Since data packets are transmitted to the cloud in batches for efficiency, this storage server is modelled as a batch server. The storage server goes into sleep mode in between data transmission periods which are, in turn, modelled as vacations. The storage service is resumed after a vacation if there are enough packets in backlog or enough time has elapsed since last storage. This is modelled as restarting thresholds in our model. Analyzing this model helps us evaluate the quality of service (QoS) of storage processes in terms of measures such as backlog size and probability of a new connection to cloud server. These measures are then used to define a user cost function and QoS constraints, and compute optimal storage parameters.
Keywords
Management Science and Operations Research, General Decision Sciences, Cloud data storage, Stochastic modeling, Queueing theory, Batch service, POWER SAVING MECHANISM, SERVICE QUEUE, STRATEGY

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MLA
Saxena, Apoorv, et al. “Cloud Data Storage : A Queueing Model with Thresholds.” ANNALS OF OPERATIONS RESEARCH, vol. 293, no. 1, 2020, pp. 295–315, doi:10.1007/s10479-019-03279-y.
APA
Saxena, A., Claeys, D., Zhang, B., & Walraevens, J. (2020). Cloud data storage : a queueing model with thresholds. ANNALS OF OPERATIONS RESEARCH, 293(1), 295–315. https://doi.org/10.1007/s10479-019-03279-y
Chicago author-date
Saxena, Apoorv, Dieter Claeys, Bo Zhang, and Joris Walraevens. 2020. “Cloud Data Storage : A Queueing Model with Thresholds.” ANNALS OF OPERATIONS RESEARCH 293 (1): 295–315. https://doi.org/10.1007/s10479-019-03279-y.
Chicago author-date (all authors)
Saxena, Apoorv, Dieter Claeys, Bo Zhang, and Joris Walraevens. 2020. “Cloud Data Storage : A Queueing Model with Thresholds.” ANNALS OF OPERATIONS RESEARCH 293 (1): 295–315. doi:10.1007/s10479-019-03279-y.
Vancouver
1.
Saxena A, Claeys D, Zhang B, Walraevens J. Cloud data storage : a queueing model with thresholds. ANNALS OF OPERATIONS RESEARCH. 2020;293(1):295–315.
IEEE
[1]
A. Saxena, D. Claeys, B. Zhang, and J. Walraevens, “Cloud data storage : a queueing model with thresholds,” ANNALS OF OPERATIONS RESEARCH, vol. 293, no. 1, pp. 295–315, 2020.
@article{8617269,
  abstract     = {In the past decade, cloud platforms have become a standard across the industry for data storage and operations. Such platforms offer high quality of service in terms of reliability and ease of setup at an effective cost. With exponentially high rates of increase of data storage requirements, data is now increasingly stored in clouds. However, there are limited studies which analyze the processes performing the storage operations. Queueing models offer a very natural way of modeling these storage processes. The data packets waiting for storage form a queue which is served by a storage server. Since data packets are transmitted to the cloud in batches for efficiency, this storage server is modelled as a batch server. The storage server goes into sleep mode in between data transmission periods which are, in turn, modelled as vacations. The storage service is resumed after a vacation if there are enough packets in backlog or enough time has elapsed since last storage. This is modelled as restarting thresholds in our model. Analyzing this model helps us evaluate the quality of service (QoS) of storage processes in terms of measures such as backlog size and probability of a new connection to cloud server. These measures are then used to define a user cost function and QoS constraints, and compute optimal storage parameters.},
  author       = {Saxena, Apoorv and Claeys, Dieter and Zhang, Bo and Walraevens, Joris},
  issn         = {0254-5330},
  journal      = {ANNALS OF OPERATIONS RESEARCH},
  keywords     = {Management Science and Operations Research,General Decision Sciences,Cloud data storage,Stochastic modeling,Queueing theory,Batch service,POWER SAVING MECHANISM,SERVICE QUEUE,STRATEGY},
  language     = {eng},
  number       = {1},
  pages        = {295--315},
  title        = {Cloud data storage : a queueing model with thresholds},
  url          = {http://dx.doi.org/10.1007/s10479-019-03279-y},
  volume       = {293},
  year         = {2020},
}

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