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Cloud data storage: a queueing model with thresholds

Author

Listed:
  • Apoorv Saxena

    (Ghent University)

  • Dieter Claeys

    (Ghent University
    Flanders Make)

  • Bo Zhang

    (LYFT)

  • Joris Walraevens

    (Ghent University)

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.

Suggested Citation

  • Apoorv Saxena & Dieter Claeys & Bo Zhang & Joris Walraevens, 2020. "Cloud data storage: a queueing model with thresholds," Annals of Operations Research, Springer, vol. 293(1), pages 295-315, October.
    • Handle: RePEc:spr:annopr:v:293:y:2020:i:1:d:10.1007_s10479-019-03279-y
    DOI: 10.1007/s10479-019-03279-y
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    References listed on IDEAS

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    1. Warren B. Powell & Pierre Humblet, 1986. "The Bulk Service Queue with a General Control Strategy: Theoretical Analysis and a New Computational Procedure," Operations Research, INFORMS, vol. 34(2), pages 267-275, April.
    2. Ioannis Dimitriou, 2016. "Queueing analysis of the DRX power saving mechanism in fault-tolerant 3GPP LTE wireless networks," Annals of Operations Research, Springer, vol. 239(2), pages 521-552, April.
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