Kernel-based Virtual Machine Research Articles

IoT IP Overlay Network Security PerformanceAnalysis with Open Source Infrastructure Deployment

Some of the most deployed infrastructures nowadays are Overlay Networks (ONs). They consist of hardware and software components designed to establish private and secure communication channels, typically over the Internet. ONs are among the most reliable technologies for achieving this objective and represent the next-generation solution for secure communication. In this paper, we analyze important network performance metrics (RTT, bandwidth) while varying the type of Overlay Network used for interconnecting traffic between two or more hosts (within the same data center, in different data centers in the same building, or over the Internet). These networks establish connections between KVM (Kernel-based Virtual Machine) instances rather than the typical Docker/LXC/Podman containers. The first analysis will assess network performance as it is, without any overlay channels. The second will establish various types of channels without encryption, and the final one will encapsulate overlay traffic via IPsec (Transport mode), where encrypted channels like VTI are not already available for use. The obtained performance is demonstrated through a comprehensive set of traffic-simulation campaigns.

Comparison of Glomerular Filtration Rate Equations in a Rural New Mexico Cohort: Results from the COMPASS Study.

The NKF-ASN Task Force recommends accurate kidney function estimation avoiding biases through racial adjustments. We explored the use of multiple kidney function biomarkers and hence estimated glomerular filtration rate (eGFR) equations to improve kidney function calculations in an ethnically diverse patient population. Prospective community cohort study. rural New Mexico clinic with patients > 18 yo. Markers of kidney function, IDMS-Creatinine (SCr), chemiluminescence Beta-2 Microglobulin (B2M), Nephelometry-calibrated ELISA Cystatin C (CysC), inflammation, glucose tolerance, demographics, BUN/UACR from the baseline visit of the COMPASS cohort, were analyzed by Kernel-based Virtual Machine learning methods. Among 205 participants, the mean age was 50.1, 62% were female, 54.1% Hispanic American and 30.2% Native American. Average kidney function biomarkers were: SCr 0.9 mg/dl, B2M 1.8 mg/L, and CysC 0.7 mg/dl. The highest agreement was observed between SCr and B2M-based eGFR equations [mean difference in eGFRs: (4.48 ml/min/1.73m2], and the lowest agreement between B2M and CysC-based eGFR equations (-24.75 ml/min/1.73m2). There was no pattern of association between the differences in eGFR measures and gender. In the continuous analyses, the absolute eGFR value (p<2 x 10-16) and serum albumin (p =6.4 x 10-5) predicted the difference between B2M- and SCr-based e-GFR. The absolute eGFR value (p<2 x 10-16) and age (p =7.6 x 10-5) predicted the difference between CysC- and SCr-based e-GFR. Relatively small sample size, elevated inflammatory state in majority of study participants and no inulin excretion rate measurements. B2M should be strongly considered as a kidney function biomarker fulfilling the criteria for the NKF-ASN. B2M's eGFR equation does not need adjustment for gender or race and showed the highest agreement with SCr-based eGFR equations.

Scalable Computing Infrastructure for Online and Blended Learning Environments

With the growing popularity of online learning and blended learning, as well as the rapid development of cloud computing and big data technology, scalable computing infrastructure has become an indispensable part of building a modern education platform. Method: Five experiments were conducted to test the scalability and reliability of computing infrastructure based on online and blended learning environments. The experiments include the performance comparison of online learning platforms based on different virtualization technologies, the performance comparison of online and hybrid learning environments under different loads, the comparison of online learning experiences under different bandwidth constraints, the system stability test under different user numbers, and the comparison of access speeds in different regions. Result: The experimental results showed that on an online learning platform using the KVM (Kernel-based Virtual Machine) interface, when the number of concurrent users is 99, the response time is 100.9ms, and the CPU (Central Processing Unit) utilization rate is 60.9%. Under low load conditions, the concurrent access volume is 200; the response time is 50ms, and the throughput is 10.3. When accessing locally, the latency is 9.19ms; the download speed is 500.3KB/s; the network throughput is 399.8KB/s. Conclusion: Exploring the scalability, reliability, performance, stability, and access speed of online learning platforms is crucial for improving platform competitiveness and ensuring user experience.

Efficient online migration mechanism for memory write-intensive virtual machines

Online migration of virtual machines (VMs) is indispensable for system maintenance as it helps to achieve several resource management objectives such as load balancing, proactive fault tolerance, green operation, and resource management of data centers. The migration efficiency and reliability are two major challenges in the online migration of memory write-intensive VMs. For example, pre-copy migration transfers a large amount of data and takes a long time to migrate. This study proposes an efficient and reliable adaptive hybrid migration mechanism for memory write-intensive VMs. The mechanism optimizes the data transfer mode of the common migration method and improves the performance of conventional hybrid migration. First, the virtual machine (VM) memory data to be migrated are divided into dynamic and static data based on the bitmap marking method, and the migration efficiency is improved through parallel transmission based on different networks. Second, to accelerate the migration reliability, an iterative convergence factor is proposed to evaluate the current system load state and adaptively calculate the switching time of the migration mode for adaptive hybrid migration based on the convergence factor. Through adaptive hybrid migration can achieve migration completed successfully, shorten the post-copy migration duration, and minimize the impact on the performance of VMs. Finally, this paper implements the system prototype based on a kernel-based virtual machine (KVM), and experiments are performed using multiple memory write-intensive load VMs. The results show that the proposed migration algorithm can significantly improve migration performance and complete migration quickly to solve the pre-copy migration failure problem with a memory write-intensive load. Compared with the traditional hybrid migration with only one round of pre-copy, the proposed migration algorithm reduces the total migration time and transmits data by 23.2% and 26.7%, respectively.

Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.

High availability of kernel-based virtual machine using nested virtualization

- KVM (Kernel-based Virtual Machine) is one of the popular, free and open-source virtual machine managers for Linux distributions. Many people or companies use plain KVM virtualization, but in many cases, they have no measures in place for ensuring availability if a host fails. The existing method to set up a high availability cluster using basic KVM virtualization is a difficult procedure for someone unfamiliar with the process. A growing number of powerful strategies are furnished with network interconnection and are related to the Internet of Things (IoT). In this research work proposes a simple method for ensuring the high availability of KVM hypervisor with the help of the nested virtualization technique. In the proposed work, the KVM hypervisor will run on another high availability enabled hypervisor. For the performance evaluation, the parameters like CPU performance, I/O performance and memory performance were evaluated. That got the better results in the proposed system than others. The proposed evaluation demonstrates that the overhead in relations of network throughput and CPU accomplishment is low so it related that the proposed concept is a evocative step in the direction of additional resilient forthcoming IoT devices.

KHV: KVM-Based Heterogeneous Virtualization

A KVM (Kernel-based Virtual Machine) is subject to the complexity of the Linux kernel and the difficulty and cost of safety certification; thus, it is not popularized in embedded high-reliability scenarios. This paper proposes a KVM-based Heterogeneous Virtualization (KHV), which is independent of hardware virtualization (KVM mandatory virtualization), follows the principle of static partitioning, localizes the hypervisor, and inherits the KVM software ecosystem. KHV balances the demands of static partitioning and flexible sharing in the embedded system. The paper implemented KHV on the RISC-V Xuantie C910 CPU-based SoC and conducted a performance comparison with KVM. The experiment shows that KHV is 50% smaller than KVM in terms of fluctuation, and KHV makes the guest OS have the same performance as the bare-metal OS in scheduler benchmarks, whereas KVM dropped an average of 28%.

Real-Time Performance Analysis and Tuning of Embedded System Virtualization Architecture Based on KVM

Embedded systems are proceeding towards exploiting virtualization technology to have the benefits of Real-Time Operating System (RTOS) and General-Purpose Operating System (GPOS) in the same system. This combination provides both a timely and deterministic behavior and a general-purpose application codebase. There still exist concerns about the real-time responsiveness of RTOS running inside a Virtual Machine (VM). In this paper, the real-time performance of Kernel-based Virtual Machine (KVM) virtualization architecture is analyzed on a multi-core system. Here, a preemptible Linux kernel with the PREEMPT_RT patch is used for RTOS, while a standard Linux kernel is used for GPOS. The interrupt latency inside the real-time guest VM is analyzed by applying various amounts of CPU, memory, and I/O stresses on the guest and host systems. A VM resource monitoring tool ‘VM_stat’ is developed to know the resource usage of the guest VMs, which is useful for effectively tuning the system. Different real-time tuning measures are applied on the host/guest systems and the performance is analyzed.

Deploying Multi-tenant FPGAs within Linux-based Cloud Infrastructure

Cloud deployments now increasingly exploit Field-Programmable Gate Array (FPGA) accelerators as part of virtual instances. While cloud FPGAs are still essentially single-tenant, the growing demand for efficient hardware acceleration paves the way to FPGA multi-tenancy. It then becomes necessary to explore architectures, design flows, and resource management features that aim at exposing multi-tenant FPGAs to the cloud users. In this article, we discuss a hardware/software architecture that supports provisioning space-shared FPGAs in Kernel-based Virtual Machine (KVM) clouds. The proposed hardware/software architecture introduces an FPGA organization that improves hardware consolidation and support hardware elasticity with minimal data movement overhead. It also relies on VirtIO to decrease communication latency between hardware and software domains. Prototyping the proposed architecture with a Virtex UltraScale+ FPGA demonstrated near specification maximum frequency for on-chip data movement and high throughput in virtual instance access to hardware accelerators. We demonstrate similar performance compared to single-tenant deployment while increasing FPGA utilization, which is one of the goals of virtualization. Overall, our FPGA design achieved about 2× higher maximum frequency than the state of the art and a bandwidth reaching up to 28 Gbps on 32-bit data width.

A Performance Analysis of Virtual Mail Server on Type-2 Hypervisors

Virtualization is now the most efficient and successful procedure to use server resources. Virtualization is a technology that will efficiently allow the instructions or programs based on the available hardware. With the help of virtualization, we can achieve benefits like reduced capital and operating costs, minimized downtime, increased productivity, and good disaster recovery, etc. This work compares the virtual mail server performance on 3 different Hypervisor software. The 3 different type-2 hypervisor software we chose to compare were Kernel-based Virtual Machine (KVM), Oracle VirtualBox, and VMware. A computer system that sends and also receives an email over a network either on LAN or the internet is called a Mail Server. Although large ISPs and public email services have their dedicated hardware, in most cases, the web servers and mail servers are hosted on a single machine by virtualization. When a Mail server is implemented on the hypervisor, it is then considered as a virtual mail server. A virtual mail server is good at load balancing and has many more benefits. This paper presents how to implement a virtual mail server and shows the comparative results of the virtual mail server on different type-2 hypervisor software. HIGHLIGHTS Virtualization will efficiently run the instructions or programs based on the available hardware The virtual mail server has more significant advantages than the standard mail server. It helps in achieving faster deployment of mail servers with fewer hardware requirements Comparison of the performance of the virtual mail server on three different Type-2 Hypervisor software: Kernel-based Virtual Machine (KVM), Oracle VirtualBox, and VMware The web servers and mail servers can be hosted on a single machine by virtualization A virtual mail server is best for load balancing and disaster recovery as well

Design innovations for IBM Z hardware management appliance

The IBM z15 supports the new IBM Z Hardware Management Appliance optional feature, which provides redundant Hardware Management Consoles (HMCs) and Support Elements (SEs) that run on redundant physical servers inside the CPC (Central Processor Complex) frame. This eliminates the need for having to manage one or more separate physical servers for HMCs outside of the frame. This article describes the host HMC/KVM (kernel-based virtual machine) and Virtual SE environment, which is completely managed by IBM Z firmware as a true appliance. It also illustrates the firmware integrity monitoring environment for the host HMC/KVM extended to the Virtual SE to provide secure boot protection for firmware and continuous monitoring and the utilization of a shared trusted platform module. It describes how physical system errors are processed by problem analysis firmware running on the Virtual SE, thereby enabling online guided repair instructions running on the Virtual SE to be used for both HMC and SE detected errors. This article will shares the innovative design basics of the IBM Z Hardware Management Appliance feature, which has significant positive impact potential for both clients and IBM.

Dynamic Network Scheduling for Virtual Routers

Network scheduling is important to satisfy the bandwidth requirements of virtual networks that consist of virtual machines in the end-hosts and the virtual routers connecting them. However, existing studies have focused on developing bandwidth allocation techniques for end-host virtual machines, but do not consider the network performance of virtual routers. In this article, we propose a new network scheduling framework for virtual routers-CreditBank. CreditBank dynamically allocates network resources to virtual routers according to bandwidth requirements, and it adapts to changing network environments without adding significant overhead. CreditBank offers three scheduling policies: minimum bandwidth reservation, weight-based proportional sharing, and hybrid scheduling. In addition, CreditBank supports an efficient work-conserving method to maximize network utilization. We implement CreditBank based on the Xen and Kernel-based Virtual Machine (KVM) hypervisors and evaluate its performance. The evaluation results indicate that CreditBank satisfies bandwidth requirements of the virtual routers while utilizing up to 99% of network resources.

Integrity verification of Docker containers for a lightweight cloud environment

Virtualisation techniques are growing in popularity and importance, given their application to server consolidation and to cloud computing. Remote Attestation is a well-known technique to assess the software integrity of a node. It works well with physical platforms, but not so well with virtual machines hosted in a full virtualisation environment (such as the Xen hypervisor or Kernel-based Virtual Machine) and it is simply not available for a lightweight virtualisation environment (such as Docker). On the contrary, the latter is increasingly used, especially in lightweight cloud platforms, because of its flexibility and limited overhead as compared to virtual machines. This paper presents a solution for security monitoring of a lightweight cloud infrastructure, which exploits Remote Attestation to verify the software integrity of cloud applications during their whole life-cycle. Our solution leverages mainstream tools and architectures, like the Linux Integrity Measurement Architecture, the OpenAttestation platform and the Docker container engine, making it practical and readily available in a real-world scenario. Compared to a standard Docker deployment, our solution enables run-time verification of container applications at the cost of a limited overhead.

PERBANDINGAN OPENVZ DENGAN KERNEL BASED VIRTUAL MACHINE (KVM)

Server virtualization is one system that uses energy and can work simultaneously. In this study the comparison of OpenVZ based hypervisors with kernel virtual machine (KVM) hypervisors will be compared on a virtual private server (VPS) machine. The research method uses PPDIOO developed by Cisco in network system design. The phases in the PPDIOO method are Prepare, Plan, Design, Implement, Operate and Optimize. The results of this study are OpenVZ-based virtual machines are more stable than KVM because the level of efficiency of existing resources is more efficient use of OpenVZ-based virtual machines with data using 0.8% CPU performance and 589mb of memory, the level of use of KVM-based virtual machine resources is greater with 65.4% CPU performance and 2824 mb memory, OpenVZ-based virtual machines that are able to run without large resources, while KVM-based virtual machines while working together require very large resources. Keywords: ,, ,, Kernel-based Virtual Machine (KVM), Virtual Private Server (VPS)

Server Structure Proposal and Automatic Verification Technology on IAAS Cloud of Plural Type Servers

In this paper, we propose a server structure proposal and automatic performance verification technology which proposes and verifies an appropriate server structure on Infrastructure as a Service (IaaS) cloud with bare metal servers, container based virtual servers and virtual machines. Recently, cloud services have been progressed and providers provide not only virtual machines but also new metal servers and container based virtual servers. However, users need to design an appropriate server structure for their requirements based on 3 types quantitative performances and users need much technical knowledge to optimize their system performances. Therefore, we study a technology which satisfies users' performance requirements on these 3 types IaaS cloud. Firstly, we measure performances of a bare metal server, Docker containers, KVM (Kernel based Virtual Machine) virtual machines on OpenStack with virtual server number changing. Secondly, we propose a server structure proposal technology based on the measured quantitative data. A server structure proposal technology receives an abstract template of OpenStack Heat and function / performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology which executes necessary performance tests automatically on provisioned user environments according to the template.

HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.

A performance comparison of linux containers and virtual machines using Docker and KVM

Virtualization is a foundational element of cloud computing. Since cloud computing is slower than a native system, this study analyzes ways to improve performance. We compared the performance of Docker and Kernel-based virtual machine (KVM). KVM uses full virtualization, including $$\times $$ 86 hardware virtualization extensions. Docker is a solution provided by isolation in userspace instead of creating a virtual machine. The performance of KVM and Docker was compared in three ways. These comparisons show that Docker is faster than KVM.

AN EXPERIMENT BASED EXAMINATION OF ENERGY OVERHEAD THROUGH VM MIGRATION

Abstract: The use of cloud computing has increased in recent days with tremendous benefits to users and also provoked to rise in total ownership cost due to swift increase in energy consumption of data centers. Out of various approaches, best of virtual machine placement policies can be exploited to reduce the energy consumption during migration in data centers. The virtual machine live migration during placement has enough potential to reduce energy consumption with certain level of utilization. One of the mechanisms to achieve energy efficiency is to manage the migration time parameter of live migration with appropriate network bandwidth in communication aware connected data centers. In spite of this, virtual machine size and network bandwidth have a great impact on energy consumption of sub systems during virtual machine live migration. To follow this study, in this paper, analysis has been made to see the impact of migration time on energy consumption of sub systems during guest virtual machine live migration. For experimentation, Kernel-based Virtual Machine (KVM) hypervisor and Virt Manager was used to perform live migration on Ubuntu 14.04 Linux machines in various conditions. Afterwards, the noted observations are validated with Pearson’s Correlation Coefficient statistical approach to study the strength of relationship of defined parameters.

Key technique research on desktop virtualization in cloud environment

Desktop Virtualization is an essential problem in cloud manufacturing to make full use of shared resources so as to meet the practical application requirements. In this paper, an efficient virtualization solution for desktop transferring is presented for sharing resources. Our strategy is not limited to transfer desktops between virtual machines (VMs), transmission between virtual and physical machines is also achieved. A three-layer systemic structure based on web environment is proposed in the study. We employ the application program library Libvirt and kernel-based virtual machine (KVM) technique to manage VMs with dynamic customization scheme, which gains better virtualization performance. Moreover, we present two different schemes for desktop graphics transferring. One is based on the transport protocol virtual network computing (VNC), and the other adopts the simple protocol for independent computing environment (SPICE), which both achieve more satisfactory results. The validity of our scheme is demonstrated on some experimental results and systemic qualitative evaluation is also provided.

Performance-aware server architecture recommendation and automatic performance verification technology on IaaS cloud

In this paper, we propose a server architecture recommendation and automatic performance verification technology, which recommends and verifies appropriate server architecture on Infrastructure as a Service (IaaS) cloud with bare metal servers, container-based virtual servers and virtual machines. Recently, cloud services are spread, and providers provide not only virtual machines but also bare metal servers and container-based virtual servers. However, users need to design appropriate server architecture for their requirements based on three types of server performances, and users need much technical knowledge to optimize their system performance. Therefore, we study a technology that satisfies users’ performance requirements on these three types of IaaS cloud. Firstly, we measure performance and start-up time of a bare metal server, Docker containers, KVM (Kernel-based Virtual Machine) virtual machines on OpenStack with changing number of virtual servers. Secondly, we propose a server architecture recommendation technology based on the measured quantitative data. A server architecture recommendation technology receives an abstract template of OpenStack Heat and function/performance requirements and then creates a concrete template with server specification information. Thirdly, we propose an automatic performance verification technology that executes necessary performance tests automatically on provisioned user environments according to the template. We implement proposed technologies, confirm performance and show the effectiveness.