Table 1. Related work.

Ref. model	Domain	Parameters measured	Discussion
(Monir, AbdelKader & EI-Horbaty, 2019)	MEC	SLA was evaluated by computing users’ opinion in service provider’s processing cost, storage, maintenance and execution time.	Trust evaluation results were totally dependent upon service users’ feedback opinion, which may led to less reliable trust results.
(Ma & Li, 2018)	EC	Trust was measured by evaluating deployed data security and privacy mechanisms in terms of resource identity, performance and quality of service.	Trust updating and sharing was not addressed, which weakens the trust evaluation efficiency of the model.
(Deng et al., 2020)	MEC	A reputation-based trust evaluation model and management for service providers was introduced that measured trust in terms of identity verification, deployed hardware capabilities (CPU, memory, disk, online time) and behavior.	Trust results were derived from service consumers’ previous interactions’ ratings. Unfortunately, such users’ ratings may not be trustworthy enough.
(Ruan, Durresi & Uslu, 2018)	MEC	Service provider’s trustworthiness is measured according to its performance per transaction with a service user. A degree of confidence measure is associated accordingly that shows user expectation of service provider future behavior.	The model depended on users’ ratings, who could have different perspectives which may negatively affect trust evaluation accuracy. Monitoring and comparing such ratings in user-provider relationships is time consuming and may produce redundant data.
(Khan, Chan & Chua, 2018)	CC	Service providers’ quality of service was evaluated in terms of service availability, response time and throughput.	Fuzzy rules were used to predict future behavior of a cloud service provider. The model helped service users in their service cost estimation.
(Akhtar, 2014)	CC	Service provider performance was evaluated in terms of infrastructure (response time and resource utilization with respect to the number of users) and application performance (in terms of; response time to a user, volume of data linked and processing migration).	Service provider performance evaluation was computed using fuzzy logic. Results managed to conclude the service provider performance level.