Admission control and resource provisioning in fog-integrated cloud using modified fuzzy inference system

Fog-integrated cloud (FiC) contains a fair amount of heterogeneity, leading to uncertainty in the resource provisioning. An admission control manager (ACM) is proposed, using a modified fuzzy inference system (FiS), to place a request based on the request's parameters, e.g., CPU, memory, storage, and few categorical parameters, e.g., job priority and time sensitivity. The ACM considers the extended three-layer architecture of FiC. FiC nodes are classified into three computing nodes: fog node, aggregated fog node, and cloud node using modified FiS model. For performance study, extensive simulation experiments have been carried out on real Google trace. Different batches on the number of relevant rules are created and compared on metrics of job execution time, memory overhead, accuracy, and hit ratio with the modified rules. The proposed work has also been compared with the state of the art. The results have been encouraging and exhibit the benefits of the proposed model apart from being it lightweight with reduced number of rules, especially suited for the FiC.