Fuzzy Branch and Bound Mechanism for Allocation of Resources in Cloud Computing Environment

Objectives: The main objective of this study is to explore the cloud computing resource allocation problem by analyzing different cloud resource parameters. It seeks to evaluate CPU usage, memory usage, execution time, and energy efficiency, etc., to allocate resources efficiently with the help of the proposed mechanism. Methods: Retrieve the dataset from a reputable repository and normalize the dataset, then fuzzify costs (data values) in the table, compute COG, defuzzify, and then apply optimization methods to measure allocation time. Various assignment methods are compared, a fuzzy branch and bound mechanism is also proposed, and the values of different parameters are computed. Then, the results of the proposed method are better than those of the compared mechanisms. Findings: An analysis of different assignment problems is given, and it is found that there is a requirement for such a mechanism that optimally allocates the resources. This paper presents a fuzzy branch and bound mechanism through which the allocation of resources can be made optimally. The working example shows that allocation through the Fuzzy B&B mechanism is minimum, i.e., 89.3795 per unit, and better than other assignment problem mechanisms such as Hungarian and TSP using the diagonal completion method. Novelty: We use fuzzified data values of the dataset to handle uncertainty, enable adaptive learning, enhance decision-making, and create innovative, flexible, and intelligent systems that process vague or imprecise information effectively. Keywords: Fuzzification, Defuzzification, Hungarian, TSP, Fuzzy B&B