Consistency in Cloud-based database systems

Mahfoud, Zohra; Nouali-Taboudjemat, Nadia

doi:10.31449/inf.v43i3.2650

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The information of context that influences the choice of consistency depends on the nature of the applications and their needs. For example [12], [13], [33]:…”

Section: Context Descriptormentioning

confidence: 99%

“…Policy-based architectures are commonly used to implement adaptive systems in different domains, such as the Internet of Things for vehicular Cloud [34] and disaster situations [35] . This kind of architecture is characterized by the particularity of separating the policy from other mechanisms of adaptation, which allows supporting dynamic adapting of the policy itself at runtime [30], [33] .…”

Section: A Policy-based Architecture For Dynamic Adaptive Consistencymentioning

confidence: 99%

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Enhancing Data Consistency via a Context-Aware Dynamic Adaptive Model

Mahfoud,

Nouali-Taboudjemat

2024

IJCDS

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This paper introduces a new model for managing data consistency in large-scale, geo-distributed storage systems, pivoting on the concept of dynamic adaptive consistency. Recognizing the challenges in balancing consistency and availability in such systems, we propose an innovative, context-aware model that categorizes operations into "consistent blocs." This categorization allows for a more granular and efficient management of consistency levels, representing a notable improvement over adaptive approaches that employ a uniform consistency model across all operations or apply a single consistency level to each operation independently. Our model dynamically adapts these blocs' consistency levels in response to real-time changes in the operational context; this can include, for example, variations in network latency, data access patterns, and workload intensity, ensuring optimal data consistency tailored to current conditions. Our approach extends traditional adaptive consistency models by introducing more flexibility. A middleware architecture achieves this goal by introducing an Adaptation Manager that dynamically adjusts consistency levels. We implement this model and evaluate its performance using the YCSB benchmark on a Cassandra cluster. Our results reveal significant flexibility in expressing users' requirements and prompt responsiveness in the dynamic adaptation of the policy. Our proposition holds significant benefits for applications where rapid adaptation to context is crucial.

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“…The information of context that influences the choice of consistency depends on the nature of the applications and their needs. For example [12], [13], [33]:…”

Section: Context Descriptormentioning

confidence: 99%

Section: A Policy-based Architecture For Dynamic Adaptive Consistencymentioning

confidence: 99%

Enhancing Data Consistency via a Context-Aware Dynamic Adaptive Model

Mahfoud,

Nouali-Taboudjemat

2024

IJCDS

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“…Software as a Service (SaaS), which represents the largest cloud market, in which the task of managing software is moved to third-party services. Cloud computing has been treated from different aspects such as: security [22], load balancing [24], storage [7] and consistency [21].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Performance Analysis of Resource Provisioning in Cloud Computing using Probabilistic Model Checking

Debbi

2021

IJCAI

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Cloud computing consists of an advanced set of technologies that allow cloud providers to offer computing resources such as infrastructure, platforms and applications to be accessible over the Internet as services. Cloud computing relies on virtualization of resources in the cloud data centers, where a set of Virtual Machines (VMs) are deployed on Physical Machines (PMs) to provision and serve user requests. Due to the dynamic nature of cloud environments and complexity of resources virtualization, as well as the diversity of user's requests, developing effective techniques to evaluate and analyze the performance of cloud centers has become highly required. In this paper, we propose the use of probabilistic model checking as an effective framework for the evaluation and the performance analysis of resource provisioning in the cloud. Based on an analytical model for resource provisioning in Infrastructure-as-a-Service (IaaS) cloud, we build a stochastic model using the probabilistic model checker PRISM and analyze it against a useful set of probabilistic and reward properties that help to measure and analyze cloud performance in an efficient way.Povzetek: Analizirane so razne komponente računanja v oblaku, npr. modeliranje in performance virov.

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