## 3 Literature Review  
Several researchers have explored the use of Natured-Inspired optimization approaches to solve data center resource consolidation issues. These have paved the way for devising a better approach to solving energy consumption in the Cloud data center. Researchers that have contributed immensely to the use of nature-inspired optimization approaches are explored. Ref. [23] applied the reassignment algorithm (GeNePi) to reduce the consumption of energy and maximize the use of resources. The GeNePi is found to improve energy efficiency and maximize the utilization of resources. The green cloud data center has been presented [24] using the ACO system. The approach uses dynamic _VM_ migration to move the under-loaded PMs to the averagely loaded PMs so that some PMs will switch off, stand by, and/or hibernate while reducing energy consumption and increasing data center performance. The approach uses two regression models to predict the CPU utilization of the PMs to reduce SLA violation and energy usage. This has led to the achievement of near-optimal solutions based on the specified objective function. However, the migration has been done considering CPU utilization while other components are still working, which also consumes energy. The approach produces sub-optimal results in terms of resource utilization and energy consumption. The high demand by users to access resources and process data has contributed to the inefficiency of the algorithms used, which led to higher consumption of energy in the data centers. There are other research works that used the ACO system to propose solutions for data center consolidation [25; 26]. In this case, the nature-inspired algorithm runs around a local solution continuously without any progress, otherwise reaching the iteration limits. Hence, if the value remains constant for a series of runs, then the algorithm traps in a local minimum after some generation. Consolidation led to inefficient resource utilization and energy wastage. In other words, trapping is a serious problem of nature-inspired algorithms in resource management at the cloud data center. Furthermore, the proposed approaches did not take into consideration of the SLA violation due to the migration of VMs. Thus,addressing resource allocation optimization problems using a consolidation technique that avoids local optima entrapment is still an active research area. In addition, a cost-centric ant colony optimization that focuses on resource allocation over cloud infrastructure is proposed for minimizing network cost and execution time [27]. Therefore, the network cost and execution time are considered as the fitness function for achieving improved performance. However, the SLA violation constraint has not been considered. Another approach, which is based on a gravitational search algorithm improved using fuzzy for resource allocation in CC, has been suggested to handle nonlinear problems due to the exponential time for checking search space [28]. Masses have been generated by incorporating a sequence of tasks assigned to all machines. Thus, each mass position is considered a solution to the problem. Fuzzy logic is employed to determine the number of masses that affect each other during the implementation of the gravitational search algorithm. A resource allocation strategy that employed a hybridized Cuckoo Search Optimization (CSO) and Shuffled Frog Leaping Algorithm (SFLA) has been proposed [29]. The CSO is used to initialize, create, and evaluate fitness function execution. SFLA is used to achieve quick convergence and simpler implementation to achieve lower execution times during task allocation. However, service level agreement has not been considered in achieving less computation time. A resource allocation concept, which is self-adaptive, considering cloud-based software as a service using the iterative quality of service (QoS) Prediction model has been proposed to minimize high overhead due to frequent machine migration [30].