## 1 Introduction
### Related Work
#### 1.2.1 Fuzzy Set Theory Based Approaches  
[PERSON] et al. [8] have developed a fuzzy decision-making system to improve the CPU scheduling algorithm in a multitasking operating system. They used a new formula to calculate the recent CPU utilization by each process. In addition, the algorithm maintains time intervals and recalculates the dynamic priority of the processes upon the arrival of the high-priority process and after the allotted time of the process. They have developed the simulator so that it takes the packet time, the finished value and the arrival time of each finished process as input. Then, the simulator calculates the recent CPU utilization of each process using the formula. A FIS generates a fuzzy dynamic priority (dpi) for each process. In accordance with the dpi of each process in descending order, the process at the head of the queue is selected to run on the processor. The proposed performance evaluation method by [PERSON] et al. [9] measures the online computer systems using the modeling occurrences of failed computer hardware units based on fuzzy set theory. In this method, it is mandatory to use subjective possibilities and probabilities when there is not enough information about some parameters of a model. Information can be obtained subjectively from experts or very little data. They present the fuzzification model and performance evaluation of online computing systems with failure based on fuzzy set theory [9]. [PERSON] et al. [10] proposed and developed a fuzzy controller for utilization management. Their developed fuzzy controller can easily adapt its control actions, i.e., rules that consider the real-time system behavior. This means the controller could manage the system workload.  
A self-adaptive algorithm for predicting the cloud resources using a fuzzy neural network has been proposed by [PERSON] et al. [11]. They used the combined algorithms of Fuzzy C-means and the subtractive fuzzy clustering algorithm to optimize the characteristics of convergence and learning speed. Their fuzzy neural network learning algorithm is optimized using a self-adjusting learning rate and impulse weight, which improves reliability and performance in real time. [PERSON] et al. [7] proposed an approach aiming to develop a model one step before CPU utilization prediction based on the clusterized selection of instances in the past steps. Proposed adaptive network-based fuzzy inference (ANFIS) predicts using the Naive Bayesian Network controller. They used the C-means clustering method, which estimates the next step direction in the time series. The second proposed approach by [PERSON] et al. [12] shows the future of CPU usage prediction based on a mixture of ANFIS models to compute short-term accuracy and mid-term reliable prediction. [PERSON] [13] presented an attempt to apply neuro-fuzzy in the development and realization of a rule-based planning algorithm to eliminate the disadvantage of known planning algorithms. The decision maker on the basis of fuzzy data was asked to calculate the new priority of all CPU processes in accordance with the priority of the process and the time of its execution.