# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 2 Literature Review
However, the solutions for the identified issues were not addressed well. [PERSON] [13] proposed an optimized method for the cloud service process. In order to provide more controlled services to the cloud users, the authors introduced a cloud auditor module and verified the entire process. So, the SLA violation is avoided and the user's satisfaction level is increased. The functionalities of the cloud auditor and its decision-making ability in relation to fulfilling SLAs were not addressed well. [PERSON] et al. [14] introduced the idea of service brokerage for clouds which relies on interoperability and quality-of-service principles. The authors imparted the need for management support and interoperability in the context of cloud service brokerage. However, the trustworthiness of the services was not addressed in their work. [PERSON] et al. [15] presented an approach for multi-cloud service composition using formal concept analysis and social network. Factors such as cost, energy consumption, and trustworthiness of the services are considered here. Though the authors highlighted the potential of context-based service representation for multi-cloud service composition, the practical difficulty with respect to the retrieval of context from multiple clouds was not successfully addressed. [PERSON] and [PERSON] [16] proposed an enhanced QoS-based service composition approach in a multi-cloud environment. In their work, the authors used the PSO algorithm to propose the solution for the service composition problem. The well-known customers only take part in the ranking process and hence the solution to the problem of cold-start is not yet considered in this paper. Effective service provisioning and the consumption of required services among the providers and consumers are the major bottlenecks in the existing cloud computing models. Hence, the authors in [17] proposed a cloud resource orchestration framework specifically focusing on multi-cloud competences. Through this study, the open research issues and the future scope for the cloud services composition have been accounted for. In a multi-cloud environment, the identification of trustworthy services has been a challenging problem. Missed QoS values leads to the improper selection of cloud services, resulting in a time-consuming process. So, the authors in [18] proposed a clustering-based cloud service recommendation for achieving reliability with less time consumption. [PERSON] and [PERSON] [19] proposed an algorithm to employ an adaptive fuzzy-based engine to select the most appropriate data center for user cloud service requests by considering user preferences in terms of cost and performance. However, the factors such as security capabilities of the service provider and security requirements of user applications have to be implemented. In addition, using a multi-objective optimization approach to balance both user cost and the provider's revenue can also be considered. In our previous works [20; 21], the trust level of services is evaluated with a Fuzzy-based MapReduce framework. The concept of Map-Reducer is employed to envisage the trust level of services. As an extension [22], a fuzzy-based intelligent cloud broker (ICB) is proposed to facilitate the service requirements for an inexperienced cloud user. The fuzzy inferencing and decision-making model are employed to perform the effective service recommendation. However, the appropriate evidence regarding the services has not been focused on during the generation of the recommendation. [PERSON] et al. [23] investigated resource allocation in decentralized multi-cloud environments, focusing on ensuring Quality of Service (QoS). It addresses challenges in estimating and dynamically adjusting resources based on workload and parallelism. Given the diversity of service and resource providers, a cooperative strategy is essential for sustainable QoS. The study aims to identify key parameters for developing an effective and rational resource allocation mechanism.