## 1 Introduction  
In recent years, many companies have been migrating to cloud services, which rely on shared resources instead of on-premises or individual on-site servers for application management. This shift away from the traditional IT infrastructure is moving organizations beyond the physical boundaries of their premises. As the cloud computing infrastructure expands, monitoring the system for optimal performance becomes increasingly important. By doing so, companies can proactively identify potential issues and mitigate any negative impact on their operations. Cloud technology meets the organization's needs by offering Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). Monitoring is one of the critical issues in cloud services [1]. The monitoring of a system is an essential need of an organization. Nowadays, this can be performed in many ways, but the critical issue is the delay, which consumes time. In [2], it is shown that monitoring a virtual machine that runs on a single host server and sharing resources with four other VMs on the same host does not mean that they all use an equal 25% of the host server. It is allowed to use more than its proportion. If, at that time, another VM requires memory it has to wait until the hypervisor is scheduling tasks between the VMs; the time that the virtual machine waits for the host server is called steal time. In [3], a study was conducted on the Amazon EC2 m3 medium model server to determine the delay and its impact on the actual world situation, and the results proved that it is highly affected by the CPU steal time because of the lack of monitoring parameters, which are also impacted by the steal time. In a cloud compute node, the VMs work as a process. In [1], the work conducted so far concerns the non-intrusive monitoring of how the hypervisor schedules the task. It would greatly facilitate cloud monitoring if we could add information about the monitoring of the CPU steal time. As discussed in [4], several tools exist, but we cannot capture the steal time. Gartner's report [5] predicts that in 2020, the cloud computing share of the market will be USD 266.4 billion, which is an increase of USD 227.8 billion, or 17%, from 2019. Significant growth will be observed in the field of SaaS and will increase to USD 116 billion in 2020 and USD 99.5 billion in 2019. This increase of 16% was the highest. According to the SRVR ETF fact sheet, by the end of 2019 the ETF's top five holdings were Equinix, 15.3%; American Tower, 13.2%; Crown Castle, 14.2%; GDS, 5.7%; and Lamar Advertising, 5.1%. Equinix spends around USD 17 billion per year on data centers, which is more than Google, Microsoft, and Amazon, which spend around USD 10 billion annually. This shows how large companies move their servers to the cloud [6]. This explosive growth in cloud computing will increase the chance of network failure and will cost millions in a few seconds; so, cloud monitoring is much more critical. A recent study showed that around two or more monitoring tools are used by more than 70% of companies to monitor their cloud data [7]. Several problem factors can occur in cloud monitoring. There are several causes of the problem, and to form the problem into the shape of a statement, we must make it more generalized. Firstly, we examined the event's issues and obstacles and found its causes. The monitoring system used currently involves overheads while monitoring the VM. Cloud monitoring systems use agents, also called intrusive cloud monitoring solutions [1,8,9]. An invasive monitoring solution inserts agents; this causes the VM to carry a greater burden due to the agents' involvement. Agent-based monitoring, which utilizes the VM's maximum power, can cause problems when the owner does not wish for its insertion, as it takes all the power because of load and delay factors. Some operations are very severe, and the owner does not want a high level of delay. If this is the case, then this monitoring system for the VM completely fails.