## 1 Introduction
An overview is given of hypervisor technologies and their impact on latency when executing processes in virtual environments. By applying the proposed algorithm, the network quality measurements from virtual environments become more reliable, allowing for a better understanding of unexpected latencies. In [16], a two-stage genetic mechanism for load balancing virtual machine hosts (VMHs) in cloud computing is developed. Different methods are focused on the current limitations of VMHs without considering the future loads, limiting their effectiveness in real environments. The proposed approach integrates two genetic-based techniques. Firstly, performance models of virtual machines (VMs) are generated using gene expression programming (GEP), which allows for the prediction of the loads of VMHs after load balancing. Secondly, the genetic algorithm considers both current and future loads of VMHs to optimize the assignment of VMs to VMHs, facilitating load balancing through VM migration. The performance of the proposed methods is evaluated in a real cloud computing environment, Jnet, where they are implemented as a centralized load-balancing mechanism. The experimental results demonstrate that the proposed method surpasses previous approaches, including heuristics and statistical regression, in terms of performance and effectiveness. Here, how the monitoring data are collected through centralized or decentralized monitoring architecture is explained. In centralized monitoring, there is a chance of a single point of failure, which is not the best choice. In centralized monitoring, the server is run on a physical machine on the cloud control node; moreover, the updates of every single node to the centralized server increase bandwidth utilization. In decentralized monitoring architecture, the chance of a single point of failure has been eliminated, and each node shares information with its peer when required; thus, there is no need for a single ample storage requirement. Here, the network utilization is also controlled. Different types of clouds were identified while conducting a literature review [17]. A single organization mainly operates a private cloud, and all the operations performed in it are by the organization itself; the public cloud is open and can be used by the local public; the community cloud is shared by a group of people that have the same shared concerns; the hybrid cloud consists of two or more different clouds (public, private, or community). Many researchers have worked on VM monitoring, but the critical issue still remains: the steal time information. In [3], the author emphasized that the importance of CPU steal time is a severe issue as its interference hits the subscriber in terms of time loss; an experiment was performed on Amazon EC2 m3. CPU steal time has seriously impacted medium model instances and the runtime workload. The study says that there is no way this loss can be monitored (i.e., CPU steal time). Time constraint is a severe issue for VMs; without knowing the importance of the work running a specific VM, the CPU delays it; this issue has to be handled very seriously [18]. Cloud monitoring data are collected from a node. There are two types of data collection: timely driven or event-driven. There are two modes: a server will request or the node will send the data. Generally, the three-cloud monitoring solution includes (i) a push model, (ii) a pull model, and (iii) a hybrid model. VMs and containers in cloud monitoring solutions are part of the operating systems. As the cloud environment speed is too fast, when creating VMs the time is impossible to measure. In [8], the author presents a new monitoring paradigm where no monitoring agent is involved within the host VM. No extra software is used in monitoring with NFM. The author tested over 1000 different types of Linux for NFM; these worked perfectly well. In [19], the author proposed a hybrid push protocol for monitoring. The name hybrid is suggested for this protocol as it uses event-driven and regular push protocols.