Toward higher-level abstractions based on state machine for cloud resources elasticity - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Information Systems Année : 2020

Toward higher-level abstractions based on state machine for cloud resources elasticity

Résumé

With the dynamic nature of cloud applications and rapid change of their resource requirements, elasticity over cloud resources has to be effectively supported. It represents the ability to dynamically adjust cloud resources that applications use in order to adapt to their varying workloads, while maintaining the desired quality of service. However, implementing elasticity is still challenging task for cloud users as heterogeneous and low-level interfaces are provided to manage cloud resources. To alleviate this, we believe that elasticity features should be provided at resource description level. In this paper, we propose a new Cloud Resource Description Model called cRDM, which is based on State Machine formalism. Using this model, we aim at representing cloud resources while considering their elasticity behavior over the time without referring to any low level interfaces or cloud provider technical constraints. We also propose a software system based on this new specification to support the elasticity-aware orchestration of cloud resources by exploiting the underlying cloud orchestration tools and APIs. We rely on a real use case to demonstrate the applicability of the proposed system and conduct a set of experiments proving the productivity and expressiveness of the cRDM model in comparison to existing solutions. The resulted findings of our evaluation shows the efficiency of our proposal.

Domaines

Informatique

Dates et versions

hal-03122711 , version 1 (27-01-2021)

Identifiants

Citer

Hayet Brabra, Achraf Mtibaa, Walid Gaaloul, Boualem Benatallah. Toward higher-level abstractions based on state machine for cloud resources elasticity. Information Systems, 2020, 90, pp.101450:1-101450:20. ⟨10.1016/j.is.2019.101450⟩. ⟨hal-03122711⟩
38 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More