## 2 Literature Review
The proposed algorithm makes the decision based on over-provisioning or under-provisioning of the resources. The extracted knowledge during the process is used to analyze the characteristics of resource utilization. Experimental result shows that the proposed prediction of the model is accurate than the compared methods. One of the key functions of cloud computing is scalability, achieved through appropriate resources scheduling. An important question is whether a resource reservation plan can be defined and used for resource scheduling effectively. The plan can allocate new resources while reserving enough available resources. A resource utilization prediction technique based on neural networks and self-adaptive differential evolution technique is proposed [17]. The proposed technique is capable of selecting the best suitable crossover and mutations. The proposed algorithm is evaluated on benchmark datasets and comparative results are presented. Cloud computing faces a few challenges, including dynamic resource expansion and power consumption. Such transactions make cloud systems fragile and expensive. Researchers in [18] proposed an algorithm to solve the problem of workload prediction in cloud data centers. The workload prediction model was developed using LSTM network. The experimental results show that the proposed method achieves better prediction accuracy as compared to other compared models. In addition, to develop models based on different attributes of the workload, the majority of clouds use only user-defined resource usage thresholds to provide automatic scaling capabilities. Few jobs can process multiple indicators simultaneously to predict resource forecasts. Authors in [19] proposed a new cloud-active automatic scaling system prediction model that combines multiple mechanisms. Correlation between different metrics is evaluated to select suitable inputs. A fuzzification technique is proposed to reduce the fluctuation of monitoring data. The prediction model is based on LSTM. Google trace data are used to evaluate the proposed model. Existing cloud resource scheduling methods mainly focus to bring improvement in resource utilization and power consumption by enhancing traditional heuristic algorithms. The occurrence of peak loads may cause scheduling errors, that may affect the energy efficiency of the scheduling algorithm. The peak loads can produce scheduling errors because no predictive model is able to predict the forthcoming resource usage of the data center by observing the data assembled by the model at the first stage. The effective scheduling algorithms offer a perfect solution to very complex problems while supplying the QoS as well as preventing SLA violations. To solve these problems an algorithm is presented in [20]. The proposed scheduling mechanism is responsible for resource scheduling while minimizing the consumption of energy and guaranteeing QoS. An algorithm predicting the completion time is proposed in [21]. MapReduce packages in cloud computing can personalize the resources allocation and finish the MapReduce jobs in a limited amount of time. Modern systems need cloud users to estimate the number of resources required to run tasks in the cloud. The proposed framework uses scale-out strategy to obtain accurate prediction of the completion time of the jobs. The regression-based performance model can predict and evaluate the execution time of five well-known MapReduce benchmark applications in a private cloud environment. To capture the variability in cloud workloads, an algorithm based on gradient descent and Levenberg-Marquardt (LM) adaptation techniques is proposed in [22]. The authors proposed a sparse framework to adapt the online resource utilization prediction model. The framework uses the concept of sparse networks. Different models are used to introduce sparsity. To achieve the desired level of sparsity, the proposed algorithm eliminates and retrains different parameters. The proposed framework is capable of fast online adaptation of resource utilization prediction models.