SLA-aware task scheduling and data replication for enhancing provider profit in clouds
| Autor: | Amel Khelifa, Faouzi Ben Charrada, Tarek Hamrouni, Riad Mokadem |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique (LIPAH), Faculté des Sciences Mathématiques, Physiques et Naturelles de Tunis (FST), Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM), Optimisation Dynamique de Requêtes Réparties à grande échelle (IRIT-PYRAMIDE), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, CentraleSupélec, Université de Tunis El Manar (UTM) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Data replication
Computer science Distributed computing Cloud computing 02 engineering and technology Bottleneck Profit (economics) Scheduling (computing) 0202 electrical engineering electronic engineering information engineering Economic model Service Level Agreement [INFO]Computer Science [cs] General Environmental Science business.industry Quality of service Triadic concept Service level objective 020206 networking & telecommunications Replication (computing) Correlation Task scheduling Data access Cloud provider General Earth and Planetary Sciences Profit 020201 artificial intelligence & image processing business |
| Zdroj: | Procedia Computer Science Procedia Computer Science, Elsevier, 2020, 176, pp.3143-3152. ⟨10.1016/j.procs.2020.09.174⟩ KES |
| ISSN: | 1877-0509 |
| Popis: | International audience; To deliver the required QoS, the cloud provider is asked to efficiently execute the tenants’ tasks and manages a huge amount of distributed and shared data. Hence, task scheduling and data replication are interdependent techniques that can improve the overall system performance and guarantee efficient data accessing. These operations must also preserve the economic profit of the cloud provider, which is very challenging. In this paper, we present a novel combination between a scheduling algorithm called Bottleneck Value Scheduling (BVS) algorithm with a dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). Our aim is to improve data access effectiveness in order to meet service level objectives in terms of response time S LORT and minimum availability S LOMA, while preserving the provider profit. Simulation results demonstrate that the proposed scheduling and replication strategies offer better performance compared to existing strategies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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