## 2 Related Work  
Currently, many studies have been actively conducted by many researchers, addressing the performance evaluation of virtual machines. In literature, many articles work on increased performance of virtual machines. Most of the literature have used VMM abstraction layer in general to measure the overhead of VMs. In the article [10], the authors reported the degradation of performance in virtual machines. They worked on NUMA architectures, based on the effects of virtualization and tested the architecture with the hypervisors such as KVM and Xen. In [11], the authors measured one of the performance factors, i.e., startup-time of virtual machines in cloud environments. Those time measurements are performed and analyzed among three well-known cloud providers: Amazon EC2, Rackspace, and Microsoft Azure. The authors presented a systematic detailed analysis of cloud computing application efficiency evaluations for scientific workloads [2]. The result said that reliability and cloud efficiency are inadequate for scientific workloads. Infrastructure-as-a-service is the main feature for adopting cloud to cope with scientific workload. In [12], the authors presented a survey on performance overhead of virtual machines. They also discussed how the performance varies from single server virtualization to geo-distributed datacenters. The authors in [13] illustrated the impact of type of workload, processor pinning, configuration, and partial background CPU load. They also investigated and addressed the issues of paired colocated compute-intensive workloads that create interference and reduce the overall performance. OpenStack cloud platform for IaaS implementation evaluates energy efficiency in high-performance computing [14]. In this article, performance impact was evaluated, which is produced by the underlying hypervisors and the IaaS solution by using the HPCC (High-Performance Computing Cluster) and HPL (High-Performance Linpack) [15], and Graph500 benchmarks [16]. The author in [17] evaluated performance of virtual machines and organization of scientific workflow on virtual systems. They conducted a complete benchmark to test the CMS (Compact Muon Solenoid) scientific workloads on virtual machines. The quantitative analysis of the efficiency of Amazon EC2 (Public Cloud) for the workloads of HPC using benchmarks was addressed in [18]. In [19], performance of virtual machines in the cloud is compared with containers usage in the cloud. Virtual machines and Linux containers are compared in terms of network performance and reduction of potential performance overhead. Some literature work quantifies the overhead efficiency of VMs by using a VMM or Xen virtualization layer. To improve the resource utilization in cloud computing environments, swarm optimization-based workload optimization (SOWO) technique is proposed in [20]. In the research, they also used the OpenStack platform and claimed that resource utilization is increased by 50 percent in cloud computing environments. Operating containers on top of virtual machines technique was examined in [21]. In the paper, they followed an observational approach to measure the overhead efficiency provided by the additional virtualization layer in virtual machines by performing different benchmarking tests and implementing programs with the real-world-use case scenarios. In [22], the authors compare the performance of a typical KVM hypervisor to a Docker Linux container by contrasting the performance of VMware Server with the actual physical servers. The extended paravirtualization (XPV) is another approach evaluated for effective virtualization of NUMA architecture [23]. XPV consists of revisiting the interface between the hypervisor and the host OS, and between the host OS and the device runtime libraries (SRLs), so that they can automatically take into account changes in the NUMA topology. The authors in [24] proposed an empirical overview of the success impact of the different resource affinities.