BGSA: Broker Guided Service Allocation in Federated Cloud
•A broker based resource allocation model for Federated Cloud environment has been proposed.•A genetic algorithm for constraint based multi-objective optimization has been proposed.•Simulated results show the effectiveness of proposed model. Cloud brokering has emerged a new service paradigm because...
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Veröffentlicht in: | Sustainable computing informatics and systems 2021-12, Vol.32, p.100609, Article 100609 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •A broker based resource allocation model for Federated Cloud environment has been proposed.•A genetic algorithm for constraint based multi-objective optimization has been proposed.•Simulated results show the effectiveness of proposed model.
Cloud brokering has emerged a new service paradigm because of the proliferation of a large number of cloud service providers. Cloud brokering attracts users because it aggregates and extends computing services from multiple cloud service providers. Service providers offer services with varying capabilities, technologies and pricing models which creates a heterogeneous service environment. Cloud users are also increasing day by day with varied requirements. Users prefer services from multiple service providers in order to fulfill their money and application requirements. Therefore, service allocation becomes one of most challenging task to fulfill all the requirements of users within resource constraints. Service allocation problem in multiple cloud environment has been studied as combinatorial optimization problem. Broker guided service allocation increases application resilience, provides unified billing and simplifies service management. In this paper we have proposed a broker guided service allocation (BGSA) model for Federated Cloud. BGSA employs a genetic algorithm based technique for service allocation. BGSA optimizes service allocation and derives well distributed set of non-dominated solutions. Simulation based evaluation of BGSA has been done and obtained results are compared with other two multi-objective algorithms. Observed results improve both execution time and total utilization. |
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ISSN: | 2210-5379 |
DOI: | 10.1016/j.suscom.2021.100609 |