## 3 Literature Review
The model entails feedback loops which go through the developed iterative QoS prediction framework and PSO-centered runtime decision algorithm. However, this model is centered on software as a service paradigm of the CC, without considering the infrastructure as a service aspect. Further, a solution for dynamic resource allocation based on improved power management and optimized task scheduling has been presented for to address the problem of high energy consumption due to inefficient resource scheduling [31]. The solution is based on a dynamic resource table maintenance algorithm and prediction strategy for improved response and task completion time. However, the service level agreement for power management in CC may not have been considered. Ref. [24] proposes ant colony-based virtual machine consolidation (ACS-VMC) for green CC to improve energy efficiency. Similarly, [32] applied a multi-objective CSO (MO-CSOA) to minimize PMs to improve energy efficiency. Ref. [32] used VM Consolidation in cloud data centers based on ACO (VMC-ACO) for the reduction of energy consumption in the cloud data centers. Ref. [33] modified PSO (MPSO) for VM reallocation to improve the energy efficacy of the system. Ref. [34] proposed a prediction based on host selection for energy efficiency for VM. Ref. [35] applied Reinforcement Learning to improve the efficiency of energy utilization in the cloud data center [36]. A Whale optimization algorithm has been employed for handling poor resource usage and high operational cost of the cloud system in the process of task scheduling [37]. Further, a multi-objective optimization model is used to improve the computing resources and performance of the cloud system. Similarly, a quality service-focused cloud scheduler that uses deep reinforcement learning has been proposed for handling large task scheduling [38]. The deep learning approach is used for managing decision-making problems in terms of task allocation to the virtual machine. Further, Ref. [39] suggested a deep adversarial imitation reinforcement learning architecture. The framework is used for scheduling time-constrained cloud tasks. In this, user requests are scheduled in such a way as to maximize task success rate while also reducing task response time. A deep Q-learning strategy for task scheduling in cloud computing setup has been proposed to improve the quality of service [40]. The Q-learning approach is introduced for the purpose of addressing the problem of directed acyclic graph jobs in cloud data centers [41]. An Energy-Efficient Dynamic Resource Management has been proposed to address the issue of energy consumption using a dynamic _VM_ migration strategy and k-means clustering technique. However, the technique focuses on quality of service and make-span to reduce the DCenergy consumption and resource under-utilization. Similarly, an energy-efficient virtual machine migration approach with SLA conservation in cloud computing has been proposed by [42]. The technique focuses on SLA violations based on the number of _VM_ migrations allocated on an individual host [43]. Interference Attentive Genetic Algorithm (IAGA) based VM allocation strategy has been proposed by [44] to reduce SLAV and performance degradation on the IaaS platform. Likewise, resource utilization management has been proposed using an agent-based algorithm in an IoT distributed environment [45]. Furthermore, a review of various numerical and hybridization of bio-inspired optimization techniques has been proposed in order to provide a consolidated platform for future research [46]. The inefficiency of the existing nature-inspired algorithms to meet-up in solving the challenge of resource consolidation calls for a promising ideal solution.