###### Abstract  
Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques. cloud computing; resource utilization; forecasting; neural networks; GA; PSO 202212126  
the network weights. Training the weights of the network is a complex optimization problem. Swarm and evolutionary algorithms are extensively used for such problems. The use of these techniques is preferred over the traditional mathematical methods [5]. To achieve this goal the proposed model uses hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (GA-PSO) that takes the advantage of the strengths of both algorithms. Hybrid versions of these algorithms have been used in a variety of domains and impressive results are achieved. The applications include antenna array pattern synthesis [6], mining association rules [7], forecasting electricity demand [8], scheduling resources in cloud computing [9], and process planning [10]. Authors in [11] have proved with the simulation that the hybrid version overcomes the disadvantages of the individual algorithms. Resource utilization prediction is studied extensively and rich literature is available [1; 4; 12; 13; 14]. Resource prediction models are influenced by some parameters, such as accuracy, time and memory complexities of the model, handling multiple resources, etc. Accuracy of the resource utilization prediction is a challenge due to multiple types of metrics, e.g., CPU and memory usage, disk I/O, network throughput, etc. There may be some implicit relationships, such as the relationship between CPU and memory usage, and the relationship between disk I/O and memory. It is difficult to find and predict the relationship for each type of resource. In this way, the prediction results will not be suitable for practical use. To solve this problem, the auto-scaler based on cloud resource prediction must have the ability to handle different indicators at the same time to make precise scaling decisions. In this article, we propose a new model for cloud resource utilization prediction using the hybrid GA-PSO algorithm. The prediction method handles multiple resource indicators simultaneously. This method combines the advantages of the two algorithms and proposes a hybrid GA-PSO algorithm to solve the prediction problem. First, the hybrid model is used to train the neural network, and then the neural network is used for resource utilization prediction. The research contribution of the article can be summarized as follows. A state-of-the-art literature review is presented that covers the analysis of different methods for resource utilization prediction in cloud computing. A hybrid model is proposed that uses GA-PSO to train the network to improve prediction accuracy. Comparative analysis of the proposed model with traditional models is presented that leads to future research directions for resource utilization prediction. The article consists of the following sections.