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A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments
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DOI：10.1109/ACCESS.2021.3100316.png)
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摘要
En 中文
Allocating and managing resources while considering the quality of service is considered a fundamental and complex research problem in a cloud environment. An optimal resource allocation optimizes several parameters such as optimizing cost and resource utilization or maximizing any quality parameters. However, to ensure better customer service, Cloud Service Providers (CSPs) should consider most of the quality attributes while allocating resources to the cloud infrastructures. Existing research does not evaluate trust as a quantitative attribute, thus a trade-off between trust and performance in resource allocation is also absent in the research area. We propose a model to consider both trust and delay in this paper. The trust of a CSP is quantitatively estimated through some attributes and metrics. Availability, reliability, data integrity, and efficiency are considered to estimate the trust. The objective is to maximize the trust of the allocation while minimizing the communication delay. The proposed joint optimization model combines the previous credentials of the CSPs and the present resource constraints. To solve the problem heuristically, a genetic algorithm is applied. The model uses a number of parameters that provide the flexibility to adapt several service requirements. The effectiveness and applicability of the proposed approach are demonstrated through experiments. The results ensure that the effectiveness in the estimation of trust evaluation for different CSPs with the proposed attributes. Moreover, integrating trust in the resource allocation model allocates appropriate resources while enhancing the trust and reducing the communication delay in the overall allocation.
Keyword:
Resource management
Reliability
Cloud computing
Delays
Optimization
Servers
Measurement
Cloud computing
trust evaluation
resource allocation
availability
reliability
optimization
genetic algorithm
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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