Computer numerical control machine tool information reusability within virtual machining systems

Unlike physical systems consisting of hardware, virtual machines are conceptual computers in logical configurations made of software. Although programs implemented in virtual machine environments have platform-independent characteristics, their performance is low in terms of implementation speed and they cannot implement those parts that are dependent on the platform by themselves. In addition, they have the shortcoming of being unable to reuse those libraries and programs that are written in the native language. In the present paper, a study for provision of native interfaces of smart virtual machines (SVMs) is introduced. The native interfaces of existing systems were analyzed to define a native interface protocol for SVMs, an interface definition language and an interface generator were designed for effective use of the defined native interface protocol, and the operation of the implemented native interface was verified through experiments. The native interface proposed in the present paper consists of items and rules necessary for information transmission between virtual machine environments and native environments, interface definition files necessary to deliver information on native functions to virtual machines, and functions and data types necessary for native environment to receive information from virtual machine environments. Through such native interfaces, virtual machine environment developers can use environment-dependent function without modifying virtual machine environments.

Scheduling in a virtual machine environment is pivotal because of its significant effect on the responsibility of Virtual Machines (VMs). Scheduling of Virtual CPU uses the BOOST mechanism and drives priority inversion and interference to the delay-sensitive VM. To tackle these issues, various techniques and methodologies have been proposed. However, these techniques can excessively delay VMs because they only focus on preferentially processing the high-priority VM. This in turn can violate the Quality of Service (QoS) of a virtual machine with relatively low priority despite the small gap. Therefore, it is imperative to apply a delay bound mechanism. In this paper, we propose a delay-constrained scheduling algorithm to guarantee the QoS of virtual machines running in a real-time virtual machine environment by applying maximum delay. We also define those factors of the proposed algorithm that will affect the system's performance. The proposed algorithm can simultaneously ensure fast response time for high-priority tasks and improve the QoS of a low-priority virtual machine, with minimum overhead.

SummaryVirtual desktop infrastructure (VDI) solutions seek to provide a satisfactory user experience at the client side when accessing remote desktop applications, even from mobile devices with limited capabilities. This paper presents a new approach, improving on previous work by the authors, in which a combination of Virtual Network Computing (VNC) and streaming protocols allowed efficient remote web access to virtualized applications within a cloud architecture. The new approach simplifies virtual machine templates, from which virtual machine instances are deployed, by centralizing software modules, greatly simplifying their management. Our new contribution consists of an integrated solution with specific WebM video encoding modules in charge of application visual output processing, an Hypertext Transfer Protocol (HTTP) streaming server, and a VNC server. The solution can be installed in the hypervisor of the host machines instead of replicating the servers and modules throughout the guest (virtual) machines that run the virtualized applications. Consequently, their implementations are unique and independent of the operating system of the virtual machines. In short, it is not necessary to provide different implementations for different operating systems, which reduces the complexity of virtual machine templates and greatly simplies platform management. To demonstrate our solution, we have modified the Quick Emulator (QEMU)‐Kernel‐based Virtual Machine (KVM) hypervisor source code accordingly. We also present qualitative and quantitative analyses that demonstrate that the new approach is advantageous in terms of software management and quality of experience, compared with our previous work and other well‐known thin clients, contributing to the enhancement of VDI systems. Copyright © 2015 John Wiley & Sons, Ltd.

A virtual machine is a software replica of an underlying real machine. Multiple virtual machines can operate on the same host machine concurrently, without interfere each other. Such concept is becoming valuable in production computing systems, due to its benefits in terms of costs and portability. As they provide a strong isolation between the virtual environment and the underlying real system, virtual machines can also be used to improve the security of a computer system in face of attacks to its network services. This work presents a new approach to achieve this goal, by applying intrusion detection techniques to virtual machine based systems, thus keeping the intrusion detection system out of reach from intruders. The results obtained from a prototype implementation confirm the usefulness of this approach.

Virtual machine (VM) technologies offer lots of benefits such as users' isolation, server consolidation and live migration. However, owing to the overhead incurred by indirect access to physical resources such as GPU, IO devices and VM technologies have not been widely used in high performance computing area. General purpose graphics processing unit (GPGPU) computing solution for virtual machines makes it possible that high performance computing (HPC) applications running on GPU can be ported to virtual machines (VMs) environment. A novel GPU resources management system built on VMs called VMGPURMS and a novel GPU cluster schedule policy are presented to use VM as computing node, and enable users to run jobs in the whole virtual machine environment transparently. Evaluation demonstrates that the efficiency of GPUs with the GPU cluster schedule policy could be improved by 17% over the traditional cluster schedule policy without taking GPU into consideration.

The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method.

Keeping a high-precision time base in cloud clusters is still a big challenge, even using the Precision Time Protocol version 2 (PTPv2) specified in IEEE 1588. One of the main factors on this issue is that too many uncertainties in the network path from the master clock to the slave one, which is likely residing on the Kernel-based Virtual Machine (KVM). The Transparent Clock (TC) of PTPv2 may be useful to solve this issue. However, at present the network I/O path in the virtual machine environment has not been considered in such works, which seriously affect the performance of the synchronization accuracy. In this work, we make improvements in the kernel network stack to manipulate the PTP frames to record the residence time for the path from the physical machine to the virtual machine, making its network I/O architecture an End-to-End Transparent Clock (E2E TC). Although the idea of E2E TC for more accurate synchronization is not new, there is no such implementation publicized for the virtualized environment, as far as we know. The proposed mechanism is specifically tailored to make latency corrections for the neglected routing path between the physical Network Interface Card (NIC) and the virtual machines. Extensive experiments and tests show that this approach makes quite significant improvement of about 50%.

Server cluster systems equipped with virtual machines are widely used to realise scalable and high-performance computing systems. In order to satisfy application requirements like response time, processing loads of virtual machines have to balance with one another in a server cluster. In addition to achieve the performance objectives, the total electric energy of a server cluster to perform application processes has to be reduced. In this paper, the Energy Consumption Laxity Based (ECLB) algorithm is proposed to allocate computation type application processes to virtual machines in a server cluster so that the total electric energy of the server cluster and response time of each process can be reduced. We evaluate the ECLB algorithm compared with basic Round-Robin (RR) algorithm. Evaluation results show the average total electric energy of a server cluster and average response time of each process in the ECLB algorithm can be more reduced than in the RR algorithm.

Transient survivability analysis of a virtualized system (VS) is critical to the wide deployment of cloud services. The existing research of VS availability and/or reliability focused on the steady-state analysis. This paper presents a model and the closed-form solutions to analyze the survivability of both cloud service and VS after a service breakdown occurrence by using continuous-time Markov chain. Service breakdown may be caused by software rejuvenation of virtual machine (VM) and/or VM monitor (VMM), or caused by VM and/or VMM bugs. The VS applies two techniques for improving service survivability: VM failover and live VM migration. The proposed model and the defined survivability metrics not only enable us to quantitatively assess the system survivability but also provide insights on the investment efforts in system recovery strategies. Sensitivity analysis through numerical analysis is carried out to study the impact of key parameters on system survivability.

Software bugs affect system reliability. When a bug is exposed in the field, developers need to fix them. Unfortunately, the bug-fixing process can also introduce errors, which leads to buggy patches that further aggravate the damage to end users and erode software vendors' reputation.

With the help of the computer simulation technology, the virtual machining simulation system of NC machine tool was built based on the integrated simulation module of the UG NX software. Through setting the NC machine parameter of the virtual machining system, the machining process simulation of the typical parts in virtual environment was completed, which implements the collision and interference checking in the process of NC machining and verifies the correctness of NC programs. Finally, the simulation results prove that the assembly modeling method of virtual simulation system for the NC machining is feasible, and the simulation of machining process can effectively determine the mutual interference problem of the machining system, which lays the foundation for effectively preventing the collision accident.

Virtual Infrastructure-as-a-Service (IaaS) clouds are emerging for secondary cloud service providers to manage their own IaaS clouds on top of existing IaaS clouds. In virtual IaaS clouds, guest virtual machines (VMs) run inside cloud VMs provided by existing IaaS clouds. Unlike traditional IaaS clouds, they can be migrated between cloud VMs co-located at the same host. However, the performance of such VM migration is low due to slow virtual networks and doubled system loads. To optimize VM migration between co-located cloud VMs, we propose zero-copy migration for virtual IaaS clouds. Zero-copy migration just relocates the memory image of a guest VM without any copy. To enable live migration with negligible downtime, it first makes the memory of a guest VM share with the destination cloud VM and thereafter completes memory relocation. We have implemented a system called VMBeam for enabling zero-copy migration in Xen. According to our experimental results, zero-copy migration could achieve high migration performance and low system loads.

The workload of a data center has the characteristics of complexity and requirement variability. However, in reality, the attributes of network workloads are rarely used by resource schedulers. Failure to dynamically schedule network resources according to workload changes inevitably leads to the inability to achieve optimal throughput and performance when allocating network resources. Therefore, there is an urgent need to design a scheduling framework that can be workload-aware and allocate network resources on demand based on network I/O virtualization. However, in the current mainstream I/O virtualization methods, there is no way to provide workload-aware functions while meeting the performance requirements of virtual machines (VMs). Therefore, we propose a method that can dynamically sense the VM workload to allocate network resources on demand, and can ensure the scalability of the VM while improving the performance of the system. We combine the advantages of I/O para-virtualization and SR-IOV technology, and use a limited number of virtual functions (VFs) to ensure the performance of network-intensive VMs, thereby improving the overall network performance of the system. For non-network-intensive VMs, the scalability of the system is guaranteed by using para-virtualized Network Interface Cards (NICs) which are not limited in number. Furthermore, to be able to allocate the corresponding bandwidth according to the VM’s network workload, we hierarchically divide the VF’s network bandwidth, and dynamically switch between VF and para-virtualized NICs through the active backup strategy of Bonding Drive and ACPI Hotplug technology to ensure the dynamic allocation of VF. Experiments show that the allocation framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%.

Task allocation and load balancing are vital issues in cloud computing, especially in the Infrastructure as a Service (IaaS) model, where resources and virtual machines (VMs) are inadequate. The current paper presents a new concept of improving cloud resource management and at the same time bolstering VM resilience against the possible threats of resource significantly. The optimization of cloudlets placement in virtual machines using Quantum-Driven Adaptive Surfer Optimization Algorithm (QDASOA) is conducted based on intertwining quantum-driven principles and surfer-based methods. QDASOA is able to minimize makespan, execution time, and have cloudlet deadlines met so as to reduce attack surfaces that can occur due to resource contention and underutilization. The optimized cloudlet allocation that is produced by QDASOA is the base of the following optimization. It was based on this that the Nebula Dynamic ANFIS Resource Balancing Algorithm (NDARBA) dynamically balances the load between data centers. This strategy also deals with uncertainties and makes sure that SLA will be achieved because of adapting to dynamic workload and current situation of the system. The simulation includes the testing of a variety of cloudlet environments (with set-ups of 10 to 1000 cloudlets), and virtual machine environments (set-ups of 2 to 6 VMs), with variable task lengths and deadlines to give consideration of actual cloud scenarios. The experimental evidence indicates that a key performance indicators, such as the shortened makespan, execution time and increased use of the resources are significantly increased, and additionally, that it increases the system robustness to possible VM-specific threats in comparison to both the tradition model and the current state of the art practices

The amount of smart phones has increased exponentially. Due to the periodic release of high-performance smart phones and upgraded operating system, new smart phones become out-dated over 1 or 2 years. In order to solve environmental constraints of these smart phones, virtualization technology using Thin-Client terminal has been developed. However, in the case of Virtual Machine(VM), the applications associated with sensors and a GPS device can not run because they are not included. In this paper, by implementing the device driver for Android running in a virtual machine in the x86-based systems, it is to provide Android virtualization capabilities such as using the latest smart phones in the virtual machine environment. It would like to propose a method that the virtual device driver receives sensors and GPS information from the old Android smart phones(Thin-Client) that actually work and run as if the real device exists.