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A comparative analysis of adaptive consistency approaches in cloud storage

Abdennacer Khelaifa 1 Saber Benharzallah 2 Laid Kahloul 1 Reinhardt Euler 3 Abdelkader Laouid 4 Ahcène Bounceur 5
3 Lab-STICC_UBO_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
5 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases due to their high security and availability with a low cost. High data availability is achieved by replicating data in different servers in order to reduce access time lag, network bandwidth consumption and system unreliability. Hence, the data consistency is a major challenge in distributed systems. In this context, strong consistency guarantees data freshness but affects directly the performance and availability of the system. In contrast, weaker consistency enhances availability and performance but increases data staleness. Therefore, an adaptive consistency strategy is needed to tune, during runtime, the consistency level depending on the criticality of the requests or data items. Although there is a rich literature on adaptive consistency approaches in cloud storage, there is a need to classify as well as regroup the approaches based on their strategies. This paper will establish a set of comparative criteria and then make a comparative analysis of existing adaptive consistency approaches. A survey of this kind not only provides the user/researcher with a comparative performance analysis of the approaches but also clarifies the suitability of these for candidate cloud systems.
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https://hal.univ-brest.fr/hal-02501749
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Submitted on : Monday, March 16, 2020 - 12:38:47 PM
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Abdennacer Khelaifa, Saber Benharzallah, Laid Kahloul, Reinhardt Euler, Abdelkader Laouid, et al.. A comparative analysis of adaptive consistency approaches in cloud storage. Journal of Parallel and Distributed Computing, Elsevier, 2019, 129, pp.36-49. ⟨10.1016/j.jpdc.2019.03.006⟩. ⟨hal-02501749⟩

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