## 2 Literature Review  
Multiple services, such as processing, storage, etc., can be accessed over the Internet using a cloud computing paradigm. The Internet is becoming faster and it is easier to access services of cloud computing. Effective resource management in cloud-based computing can bring improvement in the usage of resources, applications performance, and reduce usage costs. There exists strong literature on cloud resource utilization prediction techniques. This section presents details of the related methods. Researchers in [1] proposed a regression integration method for intelligent resource utilization prediction. The proposed method integrates resource usage and function selection to improve utilization and performance. The results show that the accuracy and execution time of the proposed model are improved as compared to existing models. In addition to the better prediction, the technique reduces failures and results in fault-tolerant scheduling. The scalability of virtualization technology for cloud consumers means an excessive demand or fewer resources with respect to time [4]. Depending on this, the efficient utilization of cloud services becomes even more challenging. The model for cloud resource utilization is time-dependent and influenced by cloud resource usage trends. The authors proposed a Learning Automata (LA) theory-based cloud resource usage prediction algorithm. The algorithm uses prediction modelsto calculate weights for individual models [4]. The proposed algorithm is validated with prediction of load of many VMs. The results show that the proposed method performs better in comparison to other prediction algorithms. Determination of the exact number of resources for cloud computing applications is a complex task. The methods for workload prediction are based on a single model. Researchers in [13] proposed an adaptive method for workload prediction. This method first classifies workloads into various classes and automatically assigns different prediction models based on the characteristics of the workload. Experiments are performed to validate the proposed model. Automated resource provisioning enables flexible services by adjusting available resources to meet service needs. The accurate prediction plays significant role in reducing the power consumption and ensuring QoS and SLA especially for those services that have rigorous QoS requirements in terms of latency or response time. Authors in [14] proposed a new mechanism designed to precisely predict the processing load of distributed servers and estimate the suitable number of resources. The proposed algorithm targets optimization of service response time, SLAs, while reducing over-provisioning of resources to reduce energy consumption and costs. Experimental results show that the proposed model has better accuracy than the other compared models. Host load prediction is important for the improvement of resource allocation and utilization in cloud computing environments. Since the amount of global variance is more than the variance in the grid, its prediction with a certain amount of accuracy remains a challenge in cloud-based systems. In reference [15], the authors applied a concise adaptive and powerful model called Long Short Term Memory (LSTM) that can predict the average load in advance in consecutive future intervals. Two real workloads are used for the evaluation of performance. The experimental results show that the proposed algorithm is more accurate on both datasets as compared to other models. Despite many benefits of cloud computing, the paradigm also faces some challenges. The key issues of cloud computing consist of dynamic resource expansion and power consumption. Inefficient resource provisioning leads to inefficient cloud systems and expensive workload forecasting. A workload prediction model using neural networks and an adaptive differential evolution algorithm is proposed in [16]. The administrators can find the potential problems in the resource reservation plan and change the plan accordingly.