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Classical remote desktop systems are considered for a single host, thus hindering their applicability on virtualization environment. Remote desktop virtualization is rising in the recent years as a new advanced extension to virtualization technology. However, existing remote desktop virtualization solutions introduce weak performance in relation to response time, video quality, and cost saving. This paper proposes a novel remote desktop virtualization system for multi-tenant, namely FastDesk, which is based on a server-push mode streaming mechanism and a heuristic virtual desktop placement algorithm. Through extensive experiments, the results show that FastDesk outperforms other popular platforms in terms of bandwidth with less than 2 Mbps and 94% video quality. Meanwhile, it creates the minimal resource wastage compared with other virtual machine placement algorithms. Furthermore, the proposed FastDesk achieves low CPU utilization, low bandwidth and good scalability. At last, to widen its applicability, FastDesk has been implemented on VirtualBox for running 3D programs.

With the advantages of low latency, high performance, and low power consumption, solid state drives (SSDs) have been widely deployed as the cache layer between memory and back-end low-speed storage devices to narrow the performance gap between CPU and storage system. However, in virtualization environment, the high integration of virtual machines can introduce a lot of duplicate data blocks in the cache device. Existing cache architectures and replacement algorithms rarely take this situation into consideration. This greatly limits the efficient use of the cache device. For this case, we proposed a duplication-aware SSD-based cache architecture. In this architecture, duplicate data blocks can be significantly reduced, and the utilization efficiency of the cache device will be notably improved. Furthermore, to reduce the cache replacement overhead, we also proposed an improved adaptive replacement cache (ARC)-based replacement strategy, which we named D-ARC. Experiment results show that, in some situations, our cache architecture can improve the cache hit ratio by five times, reduce the average I/O latency by 63%, and eliminate SDD writes by 81%. Compared with the ARC-based replacement strategy, D-ARC can provide a performance improvement by about 16% if the configuration is set appropriately.

Cloud and data center applications often make heavy use of virtualized servers, where flash-based solid-state drives (SSDs) have become popular alternatives over hard drives for data-intensive applications. Traditional data prefetching focuses on applications running on bare metal systems using hard drives. In contrast, virtualized systems using SSDs present different challenges for data prefetching. Most existing prefetching techniques, if applied unchanged in such environments, are likely to either fail to fully utilize SSDs, interfere with virtual machine I/O requests, or cause too much overhead if run in every virtualized instance.In this work, we demonstrate that data prefetching, when run in a virtualization-friendly manner can provide significant performance benefits for a wide range of data-intensive applications. We have designed and developed VIO-prefetching , consisting of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices and host systems. We have implemented a real system in Linux and evaluated it on different storage devices with the virtualization layer.Our comprehensive study provides insights of VIO-prefetching’s behavior at various virtualization system configurations, e.g., the number of VMs, in-guest processes, application types, etc. The proposed method improves virtual I/O performance up to 43 percent with the average of 14 percent for 1 to 12 VMs while running various applications on a Xen virtualization system.

Virtualization technologies introduce additional uncertainty and overhead for distributed applications, that may challenge their timeliness. The additional software abstraction layers of the virtualization software offer powerful parallel execution environments but, at the same time, reduce the effective performance as additional software layers are introduced. This requires some fine tuning of, among others, the communication middleware software. The different resource management mechanisms of the middleware may collide with the specific algorithms replicated by the underlying virtualization software. The present work characterises the set of steps of a publish-subscribe communication middleware in a distributed system, enhancing it to suit the communication scheme of virtualized remote nodes. The potential communication steps are identified, and the overhead introduced by the execution over the virtualization software is provided by means of a data communication rate metric; a set of benchmark tests are presented that empirically evaluate the overhead and stability of the most widely used publish-subscribe (P/S) middleware named DDS (Data Distribution System for real-time systems). A general purpose cloud computing virtual machine monitor is utilized to identify the side effects and the drawbacks of the general virtualization technology. Results are obtained on a setting with two different DDS implementations over a general purpose virtualization software, together with a discussion about the potential drawbacks of the main communication operations.

Lightweight virtualization technologies have revolutionized the world of software development by introducing flexibility and innovation to this domain. Although the benefits introduced by these emerging solutions have been widely acknowledged in cloud computing, recent advances have led to the spread of such technologies in different contexts. As an example, the Internet of Things (IoT) and mobile edge computing benefit from container virtualization by exploiting the possibility of using these technologies not only in data centers but also on devices, which are characterized by fewer computational resources, such as single-board computers. This has led to a growing trend to more efficiently redesign the critical components of IoT/edge scenarios (e.g., gateways) to enable the concept of device virtualization. The possibility for efficiently deploying virtualized instances on single-board computers has already been addressed in recent studies; however, these studies considered only a limited number of devices and omitted important performance metrics from their empirical assessments. This paper seeks to fill this gap and to provide insights for future deployments through a comprehensive performance evaluation that aims to show the strengths and weaknesses of several low-power devices when handling container-virtualized instances.

This document describes open research challenges for network virtualization. Network virtualization is following a similar path as previously taken by cloud computing. Specifically, cloud computing popularized migration of computing functions (e.g., applications) and storage from local, dedicated, physical resources to remote virtual functions accessible through the Internet. In a similar manner, network virtualization is encouraging migration of networking functions from dedicated physical hardware nodes to a virtualized pool of resources. However, network virtualization can be considered to be a more complex problem than cloud computing as it not only involves virtualization of computing and storage functions but also involves abstraction of the network itself. This document describes current research and engineering challenges in network virtualization including guaranteeing quality-of-service, performance improvement, supporting multiple domains, network slicing, service composition, device virtualization, privacy and security, separation of control concerns, network function placement and testing. In addition, some proposals are made for new activities in IETF/IRTF that could address some of these challenges. This document is a product of the Network Function Virtualization Research Group (NFVRG).

Hardware errors are no longer exceptions in modern cloud data centers. Although virtualization provides software failure isolation among different virtual machines (VM), the virtualization infrastructure including the hypervisor and privileged VMs remains vulnerable to hardware errors. What makes matters worse is that such errors are unlikely bounded by the virtualization boundary and may lead to loss of work in multiple guest VMs due to unexpected and/or mishandled failures. To understand reliability implication of hardware errors in virtualized systems, in this paper we develop a simulation-based framework that enables a comprehensive fault injection study on the hypervisor with a wide range of configurations. Our analysis shows that, in current systems, many hardware errors can propagate through various paths for an extended time before an observed failure (e.g., whole system crash). We further discuss the challenges of designing error tolerance techniques for the hypervisor.

oud computing is a new trend which increase application ability in terms of functionality, resource management and collaborative aspect in term of distributed as well as centralized computing environment. Cloud computing system produce new disputes because of system clusters and high volume data generated by these systems. Storage virtualization makes stockpiling a ware. This makes for some intriguing courses for organizations to lessen their expenses. The central part of cloud computing is virtualization which enables industry or academic IT resources through on-demand allocation dynamically. The main importance in cloud computing is virtualization, which perform user requests on different logical machines in term of their execution in a predefined manner. Cloud computing is unquestionably conceivable without virtualization, however requires more work and time to really pull it off; by utilizing virtualization, it would be fundamentally getting a great deal of the work accomplished for nothing. The resources can be multiple networks in a client server environment having storage as well as applications. The main purpose of this research is how to increase resource management with virtualization which will improve the performance of cloud computing resource allocation its challenges and proposed new approach which will helpful to optimize the performance in sharing network infrastructure.

Virtualization is a new method regarding using computing resources efficiently, by maximizing energy efficiency, extend the life of the hardware and also recycles. Virtualization technology is a system of work done by the software can merge some real physical systems into a single virtual form commonly known as virtualization without prejudice advantages over the single system. Of course, this system can reduce the amount of hardware, electrical energy consumption and time used thereby increasing the level of efficiency and effectiveness. Also, virtualization certainly reduces heat energy arising from the number of installed hardware, thereby reducing the increase in temperature geothermal (Global Warming). Some virtualization includes Server Virtualization, Network Virtualization, Memory Virtualization, Grid Computing, Application Virtualization, Storage Virtualization, Virtualization and Thin Client Platform.

Recent growth in the processing and memory resources of mobile devices has fueled research within the field of mobile virtualization. Mobile virtualization enables multiple persona on a single mobile device by hosting heterogeneous operating systems (OSs) concurrently. However, adding a virtualization layer to resource-constrained mobile devices with real-time requirements can lead to intolerable performance overheads. Hardware virtualization extensions that support efficient virtualization have been incorporated in recent mobile processors. Prior to hardware virtualization extensions, virtualization techniques that are enabled by performance prohibitive and resource consuming software were adopted for mobile devices. Moreover, mobile virtualization solutions lack standard procedures for device component sharing and interfacing between multiple OSSs. The objective of this article is to survey software- and hardware-based mobile virtualization techniques in light of the recent advancements fueled by the hardware support for mobile virtualization. Challenges and issues faced in virtualization of CPU, memory, I/O, interrupt, and network interfaces are highlighted. Moreover, various performance parameters are presented in a detailed comparative analysis to quantify the efficiency of mobile virtualization techniques and solutions.

The Virtual machine monitor and container is an example of virtualization engine technologies that are currently in wide use. Both types of virtualization engines have vulnerabilities that could hamper a virtual machine. In our previous work, we created a resilient server using virtualization technologies with a single virtualization engine. To improve the resilience of the server, we use two different types of virtualization engines that monitor and respond by the SRN manager. The experiments results show that using multiple virtualization engines could cover their vulnerabilities against limited scenarios of failure.

Virtualization and Cloud computing are two popular research directions in recent times. Today, Virtualization is being used by a growing number of organizations to reduce power consumption, Server Consolidation, Testing and Development, Dynamic Load Balancing and Disaster Recovery, Virtual Desktops and Improved System Reliability and Security. Virtualization also provides high availability for critical applications, and streamlines application deployment and migrations. Through cloud computing, Information Technology resources can be delivered as services over the Internet to the end user. Virtualization is one of such important core technologies of cloud computing. In this paper, we present a detailed review on virtualization. Furthermore, three technologies for x86 CPU virtualization and the architecture of Xen are introduced. Specifically, we propose an architecture of the cloud computing platform based on virtualization. Finally, we discuss the performance evaluation of server virtualization in saving cost, time and energy consumption.

Virtualization is a technique in which the user required services run remotely in a ubiquitous environment which gives scalable resources. Virtualization is being used in cloud computing for load balancing and aggregation of cloud resources. Virtualization provides higher hardware utilization. It is also being used for partitioning of computational resources and hence supports sharing of resources. Virtualization has different types such as Native virtualization, Full virtualization, Operating system level virtualization and Para virtualization. Other than these there is Resources virtualization, Desktop virtualization, Server virtualization, Data centres virtualization and application virtualization. The resources virtualization is implemented in different forms such as the Full virtualization, Native virtualization, Para virtualization, Operating system (OS) layer virtualization or Hosted virtualization. Virtual machines and Virtual machine monitors (VMMs) have been developed to offer better energy efficient solutions to the virtualization problems. Virtualization tools like OpenVz, Xen, VmWare etc. are widely used in the computing industry.DOI: http://dx.doi.org/njst.v15i2.12131Nepal Journal of Science and Technology Vol. 15, No.2 (2014) 141-150

Virtualization is growing in different areas: from powerful servers in data centers to students' laptops and even cell phones. It can provide a more efficient use of hardware resources. Virtualization enables multiple virtual machines to run side-by-side in an isolated environment on a physical hardware. Modern processors are enhanced with technologies like Intel-VT and AMD-V that speed up virtual machines. However, there is still room for improving support of hardware for virtualization workloads. Gem5 is an open-source full system simulator capable of simulating a Chip-Multiprocessor with its caches, interconnection network, memory controllers among others. In its current state, gem5 does not support virtualized workloads. In this paper, we present a modified version of gem5, named gem5v, that simulates the behavior of a virtualization layer and can simulate virtual machines. We test this simulator in different scenarios using Parsec, Splash, MapReduce (Phoenix), SPEC and EEMBC benchmarks and compare its measured runtime with real systems. Results show 1---9 % difference between the simulated system and two virtualization softwares on a real hardware, namely KVM and VMware ESX. The comparison of vCPU overhead in VMware ESX and gem5v shows between 0.1 and 9 % difference.

The concept of virtualization machines is not new, but it is increasing vastly and gaining popularity in the IT world. Hypervisors are also popular for security as a means of isolation. The virtualization of information technology infrastructure creates the enablement of IT resources to be shared and used on several other devices and applications; this increases the growth of business needs. The environment created by virtualization is not restricted to any configuration physically or execution. The resources of a computer are shared logically. Hypervisors help in virtualization of hardware that is a software interact with the physical system, enabling or providing virtualized hardware environment to support multiple running operating system simultaneously utilizing one physical server. This paper explores the benefits, types and security issues of Virtualization Hypervisor in virtualized hardware environment.

The article of record as published may be found at http://dx.doi.org/10.1109/INFCOMW.2017.8116499 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Network functions like load balancers and stateful firewalls which traditionally have been packaged in a single proprietary device are now being virtualized in software across multiple physical devices networked together to achieve greater flexibility and scale. A virtualization can become very complex. Separating its definition from the software that implements it allows this complexity to be managed more easily. This paper describes some elementary behaviors that can be rigorously combined to produce modular definitions of new virtualizations. Behaviors are expressed using a new type of symbolic finite automaton called a λ-SFA. These automata can be formally analyzed and serve as a guide for synthesizing efficient code. As behaviors are combined, proofs of invariants for the result can leverage proofs of invariants for the elementary behaviors.

Nested virtualization, the ability to run a virtual machine inside another virtual machine, is increasingly important because of the need to deploy virtual machines running software stacks on top of virtualized cloud infrastructure. As ARM servers make inroads in cloud infrastructure deployments, supporting nested virtualization on ARM is a key requirement, which has been met recently with the introduction of nested virtualization support to the ARM architecture. We build the first hypervisor to use ARM nested virtualization support and show that despite similarities between ARM and x86 nested virtualization support, performance on ARM is much worse than on x86. This is due to excessive traps to the hypervisor caused by differences in non-nested virtualization support. To address this problem, we introduce a novel paravirtualization technique to rapidly prototype architectural changes for virtualization and evaluate their performance impact using existing hardware. Using this technique, we propose Nested Virtualization Extensions for ARM (NEVE), a set of simple architectural changes to ARM that can be used by software to coalesce and defer traps by logging the results of hypervisor instructions until the results are actually needed by the hypervisor or virtual machines. We show that NEVE allows hypervisors running real application workloads to provide an order of magnitude better performance than current ARM nested virtualization support and up to three times less overhead than x86 nested virtualization. NEVE will be included in ARMv8.4, the next version of the ARM architecture.

The Moodle portal of our faculty is running in a virtualized environment together with other about 50 application servers and 60 virtualized desktops. Increasing traffic on the site (reaching over 400 000 views/posts monthly) forced us to assess its performance impact on the virtualization environment. The performance analysis identified processor cycles and disk operations as the bottlenecks of the system. We are planning to address these issues with increasing of the number of processor cores in our virtualization hosts and with a solid state disk upgrade of the disk array used in our virtualization environment in our next hardware upgrade cycle.

On the road towards 5G, virtualization is viewed as one of the critical technologies to maximize the network flexibility, scalability and efficiency in order to accommodate diverse applications and scenarios. Despite the simplicity of the idea of adopting virtualization to telecom industry, the implementation is more difficult in practice. Wireless communication is distinct from IT data centres in that wireless communication has extremely strict requirements on real-time processing. In this chapter, the virtualization on radio access networks, in particular C-RAN, is carefully studied with the analysis on the challenges and potential solutions. Given that RAN functions, such as baseband processing, require intensive computation and real-time processing, the RAN virtualization then requires joint optimization on various system modules, including the operating systems, the hypervisor, etc. It also requires enhancement and optimization on existing traditional IT technologies including the network acceleration, live migration, VM communication, support of OpenStack and so on. A prototype of optimized virtualization platform with enhanced real-time performance was developed. The tests have shown that the optimized round-trip L2 processing latency of the platform could be reduced to around 30us on average, while it is usually around 300-500us in traditional Linux system. In the meantime,the VM communication latency could be optimized to around 10us, compared to several hundred microseconds for traditional Linux system. Enhancement on the platform real-time performance is just the first step. In future, much more work should be involved such as real-time management, virtualization granularity, hardware acceleration, design of high-speed low-latency switch networks, etc. to realize completely virtualized C-RAN systems.

© 1979-2012 IEEE. Service providers and vendors are moving toward a network virtualized core, whereby multiple applications would be treated on their own merit in programmable hardware. Such a network would have the advantage of being customized for user requirements and allow provisioning of next generation services that are built specifically to meet user needs. In this article, we articulate the impact of network virtualization on networks that provide customized services and how a provider's business can grow with network virtualization. We outline a decision map that allows mapping of applications with technology that is supported in network-virtualization - oriented equipment. Analogies to the world of virtual machines and generic virtualization show that hardware supporting network virtualization will facilitate new customer needs while optimizing the provider network from the cost and performance perspectives. A key conclusion of the article is that growth would yield sizable revenue when providers plan ahead in terms of supporting network-virtualization-oriented technology in their networks. To be precise, providers have to incorporate into their growth plans network elements capable of new service deployments while protecting network neutrality. A simulation study validates our NV-induced model.

Systems-on-a-chip (SoC) represents a rupture on the traditional HPC infrastructure and started to be adopted together OS-level virtualization to provide services in Fog and Edge computing scenarios. In this work, we analyzed the performance of OS-level virtualization solutions - Linux Containers (LXC) and Docker - for HPC activities running in SoC systems in order to discover how OS-level virtualization and resource sharing affects the performance of virtualized environments. We evaluated CPU and (network and inter-process) communication performance taking in account two different scenarios: without communication between benchmark processes in execution and, with processes communication in a cooperative way. Results shows that both tools are suitable for HPC applications in SoC systems, despite the notice of some performance reduction due resource sharing.

This chapter addresses the evolution of virtualization, an innovative concept designed to enable organizations to gain better control over their IT resources, reduce network equipment costs, and reduce power and space requirements. Two of the most common ways to characterize virtualization is to view it as a logical abstraction of a physical system that allows multiple physical systems to appear as a single logical system, or a single physical system that is partitioned to appear as multiple independent logical systems. The multiple facets of virtualization include virtualized desktops, virtualized applications, virtual appliances, network virtualization, storage virtualization, and service virtualization. The benefits and challenges encountered when managing virtual machines are also an important focus of this chapter.

Virtualization is the creation of components, such as hardware platform, operating system, storage, processing power, memory, or network resource, in virtual environments. The applications of virtualization are spreading rapidly and the critical systems are working on virtualization infrastructures. However; the effects of security vulnerabilities which may occur can also be high. In this study, security risks that may arise in virtualization systems, ways to avoid risks, and security configuration of a sample virtualization system have been examined. It is aimed to create security awareness to users and administrators of virtualization systems, to show how the steps of risk avoidance which can be implemented by examining a sample system.

Recently, network virtualization technology (NFV) is being studied to reduce network operation cost, mainly in telco companies. Network virtualization technology is a technology that enables software control by abstracting computing, server, and network functions that were formerly hardware-oriented. Network virtualization technology provides the opportunity for small and medium enterprises with technology to compete in the network market, which is now dominated by major network equipment manufacturers, and software companies participate in the hardware-oriented network equipment market, It provides an instrument that can be easily produced. In this paper, we investigated a takeover scheme based on virtualization technology to provide high availability service in NFV environment and measured takeover performance by virtualizing BGP based on virtualization technology.

This paper proposes the data security technology for the desktop virtualization environment using the separated software execution method. In the virtual environment where allocates separate VMs to the users, there is a benefit that the programs in one guest machine are isolated from the programs running in another guest machine, whereas in the separated execution environment that supports application virtualization, the isolation is not offered and it causes the data security problem because the applications are executed by the one root privilege in the server. To solve this problem, we provides the data security method using the server storage filter, the viewer filter, and the file mapping table in this paper.

Virtualization is a building block technology of cloud computing. Software virtualization solutions, such as paravirtualized Memory Management Unit (MMU) and paravirtualized I/O, provide flexible manageability, such as virtual machine (VM) migration and IP-based network packet filtering. However, it suffers from performance and scalability issues. Hardware virtualization and its advanced accelerations, such as Extended Page Table (EPT), and Single Root I/O Virtualization (SR-IOV), were introduced later to simplify virtualization implementation and improve performance. However, this solution may suffer from a manageability issue. In this paper, we describe our work on the implementation of hardware virtualization and optimizations of the advanced hardware acceleration support in Xen to improve the virtualization performance and scalability. We further propose HYVI, a hybrid virtualization solution combining the advantages of software virtualization (manageability) and hardware virtualization (performance and scalability), to address performance and manageability issues. Finally, we present performance evaluations and characterizations of these hardware accelerations and hybrid virtualization, using both OS benchmarks and a server consolidation benchmark (vConsolidate). The results show that optimized hardware acceleration can achieve an up to 77% and 33% performance improvement over software full virtualization and paravirtualization in the server consolidation benchmark, respectively. Hybrid virtualization achieves an up to 1.56x performance, with 0.69 lower CPU core usage of paravirtualization, and maintains flexible manageability.

Cloud computing is the delivery of computer resources over a network through web services, while the actual physical location and organization of the equipment hosting these resources are unknown to the users. Cloud computing strategy is subjected to many enablers. Interpretive structural modeling is a well proven tool for finding the interrelationship among the enablers. Total Interpretive structural model is an essential innovative version of interpretive structural modeling technique(ISM). In Total Interpretive Structural Modeling (TISM), influence/enhancement of enablers and their interrelationship is considered. Total interpretive structural model steps are identification of elements, pair-wise comparison, level partition, interaction formation, diagraph representation and formation of total interpretive structural model. The methodology of TISM is used to delineate the hierarchical relationship of enablers of cloud computing. Cloud computing is based on the concept of virtual machines. Hardware cost can be reduced by using virtualization. Different types of virtualization are operating system –level virtualization, application virtualization, service virtualization, memory virtualization, storage virtualization, virtual file system, data virtualization etc .Lack of trust between cloud users and providers has hindered the universal acceptance of cloud as outsourced computing services. To promote Multitenency we must design the cloud ecosystem to be secure, trustworthy and dependable

In this paper, we proposed new virtualization technology that is more convenient and stable in local computing environment, then found technique elements need to desktop virtualization which is based on clients in various virtualization technologies. After running excution of process explorer utility in user level virtualization and VMWare, we found memory capacity that is used 30.1MB in VMWare and 16.6MB in user level virtualization respectively to compare private bytes each of process. We found no significant difference of CPU utilization which is executed application program in local computing environment and user domain with user level virtualization. In this result, proposed vitualization technology is able to minimize performance degradation of local computing envrionment.

Prospects for applying virtualization technology in high-performance computations on the x64 systems are studied. Principal reasons for performance degradation when parallel programs are running in virtual environments are considered. The KVM/QEMU and Palacios virtualization systems are considered in detail, with the HPC Challenge and NAS Parallel Benchmarks used as benchmarks. A modern computing cluster built on the Infiniband high-speed interconnect is used in testing. The results of the study show that, in general, virtualization is reasonable for a wide class of high-performance applications. Fine tuning of the virtualization systems involved made it possible to reduce overheads from 10–60% to 1–5% on the majority of tests from the HPC Challenge and NAS Parallel Benchmarks suites. The main bottlenecks of virtualization systems are reduced performance of the memory system (which is critical only for a narrow class of problems), costs associated with hardware virtualization, and the increased noise caused by the host operating system and hypervisor. Noise can have a negative effect on performance and scalability of fine-grained applications (applications with frequent small-scale communications). The influence of noise significantly increases as the number of nodes in the system grows.

Virtualization is a new technology that creates virtual environments based on the existing physical resources. This article evaluates effect of virtualization techniques on control servers and access method in storage systems [1,2]. In control server virtualization, we have presented a tile based evaluation based on heterogeneous workloads to compare several key parameters and demonstrate effectiveness of virtualization techniques. Moreover, we have evaluated the virtualized model using VMotion techniques and maximum consolidation. In access method, we have prepared three different scenarios using direct, semi-virtual, and virtual attachment models. We have evaluated the proposed models with several workloads including OLTP database, data streaming, file server, web server, etc. Results of evaluation for different criteria confirm that server virtualization technique has high throughput and CPU usage as well as good performance with noticeable agility. Also virtual technique is a successful alternative for accessing to the storage systems especially in large capacity systems. This technique can therefore be an effective solution for expansion of storage area and reduction of access time. Results of different evaluation and measurements demonstrate that the virtualization in control server and full virtual access provide better performance and more agility as well as more utilization in the systems and improve business continuity plan. K e y w o r d s: virtual server, virtual access, storage system, workload

Virtualization enables to switch different operating systems without reboot. It enables live migration from one Operating System (OS) to another and results in proportional sharing of storage resources. Virtualization is gaining importance day by day in the fields of academics, industry and business. Performance is the major requirement to fulfill today’s need. As far as, computer’s workload is concerned, there is a need of high performance computing system. As the use of virtualization has increased tremendously there is much focus on optimizing the virtual machine performance. Disk scheduling within the virtual environment plays a key role in optimizing the overall system performance. Prior works on disk scheduling in virtual environment found it difficult to achieve system performance because of the high disk seek time. This paper presents an approach towards the performance improvement of disk scheduling in virtualized environment by future request arrival prediction. The basic idea is to examine whether the traditional High Throughput Token Bucket Disk Scheduling algorithm (HTBS) is still efficient for the performance improvement in virtualized environment.

Virtualization is a key technology in cloud computing; it can accommodate numerous guest VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing. Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for networking, suffers from significant performance degradation in the presence of high-speed networking connections. In this paper, we first analyze performance challenges in network I/O virtualization and identify two problems—conventional network I/O virtualization suffers from excessive virtual interrupts to guest VMs, and the back-end driver does not efficiently use the computing resources of underlying multicore processors. To address these challenges, we propose optimization methods for enhancing the networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual receive-side scaling to effectively leverage multicore processors. These methods are implemented and evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results confirm that the proposed optimizations can significantly improve network I/O virtualization performance and effectively solve the performance challenges.

Hardware virtualization solutions provide users with benefits ranging from application isolation through server consolidation to improved disaster recovery and faster server provisioning. While hardware assistance for virtualization is supported by all major processor architectures, including Intel, ARM, PowerPC, and MIPS, these extensions are targeted at virtualization of the same architecture, for example, an x86 guest on an x86 host system. Existing techniques for cross-architecture virtualization, for example, an ARM guest on an x86 host, still incur a substantial overhead for CPU, memory, and I/O virtualization due to the necessity for software emulation of these mismatched system components. In this article, we present a new hardware-accelerated hypervisor called C aptive , employing a range of novel techniques that exploit existing hardware virtualization extensions for improving the performance of full-system cross-platform virtualization. We illustrate how (1) guest memory management unit (MMU) events and operations can be mapped onto host memory virtualization extensions, eliminating the need for costly software MMU emulation, (2) a block-based dynamic binary translation engine inside the virtual machine can improve CPU virtualization performance, (3) memory-mapped guest I/O can be efficiently translated to fast I/O specific calls to emulated devices, and (4) the cost for asynchronous guest interrupts can be reduced. For an ARM-based Linux guest system running on an x86 host with Intel VT support, we demonstrate application performance levels, based on SPEC CPU2006 benchmarks, of up to 5.88× over state-of-the-art Q emu and 2.5× on average, achieving a guest dynamic instruction throughput of up to 1280 MIPS (million instructions per second) and 915.52 MIPS, on average.

In this paper, we propose AggFlow, a new address virtualization scheme for high scalability and resource efficiency. AggFlow is intended to provide complete address virtualization while incurring low overhead for physical switches and the control channel. To reduce overhead of address virtualization, we propose mapping-less address virtualization. We additionally introduce hop-by-hop-based forwarding, which aggregates flow rules installed in physical switches. This leads to efficient use of ternary content addressable memory and bandwidth of the control channel. Our evaluation using Mininet with simple linear and tree topologies, the flow table size of the core switch is reduced up to 1/16 for both topologies, and the control channel traffic decreases by 51% and 29% respectively. Furthermore, we measure the overhead incurred from AggFlow. Compared to OpenVirteX, AggFlow increases control plane delay by 0.1 ms, only 0.4% increase of CPU usage, and no change of throughput for UDP traffic.

Container-based virtualization is a virtualization technique at operating system level. It realizes a lightweight virtualization whose overhead is much less than hypervisor based virtualization. In a container, the programs often consume much more memory than the developer or the administrator expected. There are several methods to prevent such memory overuse. They, however, have each shortcoming such as its operation cost, the false-positive problem and so on. In this paper, we propose a new resource management method for container-based virtualization environment based on the resource consumption behavior. The method detects a wrong container that has a sign of memory overuse and makes a limitation to the detected container. A preliminary experiment for a proof-of-concept shows that it is possible to implement the proposed method in Linux kernel and it is effective to attack the problem situation.

Virtualization is central to cloud computing systems. It abstracts computing resources to be shared among multiple virtual machines (VMs) that can be easily managed to run multiple applications and services. To benefit from the advantages of cloud computing, and to cope with increasing traffic demands, telecom operators have adopted cloud computing. Telecom services and applications are, however, characterized by real- time responsiveness, strict end-to-end latency, and high reliability. Due to the inherent overhead of virtualization, the network performance of applications and services can be degraded. To improve the performance of emerging applications and services that demand stringent end-to-end latency, and to understand the network performance bottleneck of virtualization, a comprehensive performance measurement and analysis is required. To this end, we conducted controlled and detailed experiments to understand the impact of virtualization on end-to-end latency and the performance of transport protocols in a virtualized environment. We also provide a packet delay breakdown in the virtualization layer which helps in the optimization of hypervisor components. Our experimental results indicate that the end-to-end latency and packet delay in the virtualization layer are increased with co-located VMs.

Virtualization is used in data center and cloud environments to decouple applications from the hardware they run on. Hardware virtualization and operating system level virtualization are two prominent technologies that enable this. Containers, which use OS virtualization, have recently surged in interest and deployment. In this paper, we study the differences between the two virtualization technologies. We compare containers and virtual machines in large data center environments along the dimensions of performance, manageability and software development.We evaluate the performance differences caused by the different virtualization technologies in data center environments where multiple applications are running on the same servers (multi-tenancy). Our results show that co-located applications can cause performance interference, and the degree of interference is higher in the case of containers for certain types of workloads. We also evaluate differences in the management frameworks which control deployment and orchestration of containers and VMs. We show how the different capabilities exposed by the two virtualization technologies can affect the management and development of applications. Lastly, we evaluate novel approaches which combine hardware and OS virtualization.

The paper provides a way to provide virtualization administration as a service and also ways to customize, optimize and improvise the performance of latest available virtualization techniques. Till now virtualization provides utilization from 60-70% for hypervisor but there is no technique available to optimize the server and client associated with it. The paper delivers a solution which is oriented for VM administrators and VM providers. The solution once implemented will provide a secure, economical, interoperable, storage platform independent solution for virtualization setup for administrators. The implementation can be done in a few minutes but it will provide a boost to management of virtual machines from the base level. The security techniques discussed in paper can be improvised i.e. VPN and encryption can be implemented in place of port forwarding although it might affect performance.

In computing era, a virtual version of a stratagem or resource, such as computer network resources, server, storage device, or a combination of these usually refers as virtualization where the framework segregates the resources into one or more execution instances. A Virtual Machine (VM) created on the host hardware as a software is called a hypervisor or a VM manager. Nowadays, hypervisor based virtualization is the most prevailing technology for virtual environment. It is supple and performs for equivalently any guest operating system (OS) but it leads to overload on memory and CPU. Nevertheless, container based virtualization (or OS virtualization) technique which allows various different VMs employed on top of running kernel. Unalike the existing virtualization approach, a containerization does not execute a VM as a complete OS instance but runs as partial instances of the OS which reduces the CPU and memory overload and also provides the portability due to small size of containers. Docker is a tool to make Linux Containers (LXC) easier to use. Docker containers could be specifically deployed for the applications and systems deployment using lightweight containerization technique which results reducing economical overload instead of VMs over Cloud.

To achieve high-performance and platform-independent network virtualization among multiple service infrastructures in service providers' data centers, we propose hardware gateway-based data center network virtualization architecture where we place a gateway on each service infrastructure and inter-connect them using overlay network virtualization.

Background: The mobile virtualization security system can provide enhanced security with separated environment of simultaneously and independently operating multiple OS on the Hypervisor Abstraction Layer. Methods Analysis: This paper introduces the research and implementation of Type-1-Based Virtualization Security System in Smart Device Environment. Its detailed functions are mobile virtualization management, security authentication, security policy and access control, encryption/decryption, and safe storage that provide secure communication among the guest Operating Systems. This paper also introduces an experimental product operating a mobile office application. Findings: In the comparative test with the existing TEEMO Type-2-based Virtualization Security System, it measured processing times of compulsory loading situation and no-loading situation for 12 major security APIs. As a result, our system has showed reduced API processing time from 48% to 85%, faster processing speed in both compulsory loading situation and noloading situation. Its average processing time has been 0.189 in the situation of consecutive API callings. Therefore, our system has proved acceptable processing time for real users as well as enhanced security with authentication and safe storage in the mobile hypervisor virtualization system. Application/Improvements: This research can offer assistance in providing secure communication and speed improvement among guest OS in mobile virtualization environment.

In this paper, we propose virtualization based cyber-functions for mobile cyber-physical systems (CPSs). If a physical object of a CPS has a high level of mobility, and as the network path length from the cyber-function box gets larger, it becomes very difficult to satisfy the data exchange delay requirement. The proposed scheme can perform the migration of the cyber-functions to an appropriate location on the cyber-infrastructure using virtualization techniques.

Over the past years the use of digital devices has increased and home networks continue to grow in size and complexity. By the use of virtualized residential gateways advanced functionality can be moved away from the home by extending the customers edge network to the Internet Service Provider (ISP) and thereby decrease the administrative burden for the home user. By employing edge computing and cloud applications at the operator by virtualizing residential gateways instead of using physical devices creates new challenges. This paper is looking at how the choice of virtualization technology impacts performance by investigating operating system level virtualization in contrast to full virtualization for use in virtualized residential gateways. Results show that container based virtualization uses fewer resources in terms of disk, memory, and processor in virtualized residential gateways.

Embedded virtualization has emerged as a valuable way to increase security, reduce costs, improve software quality and decrease design time. The late adoption of hardware-assisted virtualization in embedded processors induced the development of hypervisors primarily based on para-virtualization. Recently, embedded processor designers developed virtualization extensions for their processor architectures similar to those adopted in cloud computing years ago. Now, the hypervisors are migrating to a mixed approach, where basic operating system functionalities take advantage of full-virtualization and advanced functionalities such as inter-domain communication remain para-virtualized. In this paper, we discuss the key features for embedded virtualization. We show how our embedded hypervisor was designed to support these features, taking advantage of the hardware-assisted virtualization available to the MIPS family of processors. Different aspects of our hypervisor are evaluated and compared to other similar approaches. A hardware platform was used to run benchmarks on virtualized instances of both Linux and a RTOS for performance analysis. Finally, the results obtained show that our hypervisor can be applied as a sound solution for the IoT.

Malicious stealth software can detect being executed in a virtual machine and thus behave differently. If the system virtualization however is moved to the hardware level, the malware is fooled and can be identified and monitored. This paper gives an overview of requirements for a hardware supported virtualization facility implemented on an FPGA. These requirements are examined along the lines of the basic parts of a typical computer architecture: the processor, memory, and devices. A proof-of-concept demonstrator was implemented on several Xilinx Evaluation boards.

Hadoop is an open-source software framework that commonly used for distributed processing and distributed storage in large datasets (big data). It perform processing and stored the data across all nodes in cluster. But, the deployment and operational costs of physical cluster are costly in some reasons. Physical cluster needs high energy and rigid in manageability. As the solution, virtualization technology used to get more flexibility and reduce costs. But, hypervisor-based virtualization gives more I/O performance overhead compared to bare-metal server. Nowadays, container-based virtualization are used to obtain lower performance overhead. Open Virtuozzo (OpenVZ) and Linux Container (LXC) are the examples of container-based virtualization. Implementation of container-based virtualization gives near native performances.

Today, consumers request virtual resources like CPU, RAM, disk (etc.) supplied by the service providers (like Amazon) and they pay on a "pay-as-you-go" basis. Generally, the supervisors adopt virtualization technologies, which optimize resources usage and limit the operating cost. The virtualization technologies are classified in two categories. The first one concerns the heavy virtualization. Each virtual machines (VM) emulates hardware and embeds its own operating system (OS) that is completely isolated from the host OS. The second one concerns the light virtualization, which is based on the management of containers. The containers share the host OS kernel [5] while ensuring isolation. In this paper, we benchmark the performance and the energy consumption of an infrastructure that is based on the software Hadoop regarding the two technologies of virtualization. At first, we will identify the points to be improved concerning Hadoop performances and then we will reduce the deployment cost on the cloud. Second, the Hadoop community finds an in-depth study of the resources consumption depending on the environment of deployment. Our experiments are based on the comparison of the Docker technology (light virtualization) and VMware technology® (heavy virtualization). We come to the point that in most experiments the light technology offers better performances in completion time of workloads and it is more adapted to be used with the Hadoop software.