# Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning
## 2 Related Work  
In the sequel, we describe some viewpoints regarding resource allocation in the cloud environments. In [3], the cloud data centre network was first modelled under the energy dissipation constraints. Then, the authors used the cooperative co-evolution evolutionary algorithms (CCEAs) for solving the resulting complex problem.. The authors in [36] addressed the challenge of virtual machine scheduling in cloud environments by presenting a new model that can analyse the distribution of the server CPU and airflow temperatures. They proposed GRANITE--a holistic scheduling method for optimising the total power dissipation of the data centre (DC). The researchers in [37] addressed the problem of virtual machine scheduling in cloud environments by introducing a discrete-time Markov chain model to predict future resource usage. In [38], [PERSON] et al. introduced a resource allocation methodology for reducing the joint SLA violations and power usage in cloud-based data centres. Their proposed algorithm comprises three sub-algorithms, which are improved for consolidating virtual machines in a dynamic manner. A machine-learning-based fast and accurate thermal prediction model was proposed in [39] to aid the resource management system's online decision. The author also proposed an energy-efficient VM scheduling algorithm to minimise peak temperature in the data centre. In [40], [PERSON] et al. investigated a bio-inspired resource allocation algorithm (cuckoo search), which is aware of the reliability of energy consumption for controlling cloud resources including networks, storage, cooling infrastructures, and servers. [PERSON] et al. in [41] developed a simple model to represent the heat rejection system and energy usage in a small DC setup. The cooling system parameters (setpoints), IT workload, and load balancing features of the model are managed by an agent, which uses RL-based machine learning techniques. Their main contribution was a holistic approach for data centre management. The inputs of their proposed method were the logs associated with cloud services and metrics related to both the IT hardware and facility. In [42], the authors addressed the load balancing problem under the constraint of achieving the minimum latency in fog networks. To solve the mentioned problem, the authors proposed a decision-making methodology, which is based on RL techniques. The mentioned methodology can obtain the optimum offloading strategy under the constraints in which both the activation/transition and reward functions are unknown. The authors in [43] tried to improve multi-data centre systems using machine learning. They considered energy efficiency and tried to minimise the usage pattern of the cloud while maintaining the required user QoS levels. [PERSON] et al. used cloud federation for profit maximisation for the joint satisfaction of user requirements and networking demands [22]. In [32], the authors proposed a resource assignment strategy according to the cooperative game and principle of uncertainty for better cloud resource utilisation and end user satisfaction. In the paper [44], the authors proposed the DCloud concept for time sliding and bandwidth scaling to increase the cloud manager benefits and reduce the costs associated with different tenants. The work in [45] is a survey paper about resource assignment in the cloud environment and its associated challenges. The authors in [46] proposed a tier-centric optimal cloud resource assignment methodology for addressing the problem of early and fast provisioning of information-technology-related resources in enterprise-level systems. The authors in [47] used bio-inspired methods for virtual machine allocation to demanding cloud users. By considering the fairness among cloud users and the cloud system's resource utilisation, the authors in [48] developed a fair and efficient cloud resource assignment strategy based on game theory. In [28], a dynamic bin-packing (DBP) methodology was adopted by [PERSON] et al. for cloud resource assignment.