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The fast access to data and high parallel processing in high-performance computing instigates an urgent demand on the improvement of the NVMe storage within modern data centers. However, the former NVMe virtualization's unsatisfactory performance demonstrates that NVMe devices are often underutilized within cloud platforms. An NVMe virtualization mechanism with high performance and device sharing has captured researchers and developers' attention. This paper introduces MDev-NVMe, a new virtualization solution for NVMe storage device with (1) full NVMe storage virtualization for VMs running native NVMe driver, (2) a mediated pass-through mechanism for NVMe management, and (3) adaptive configuration of active polling optimization to simultaneously achieve high throughput, low latency performance, and substantial device scalability. We practically implement the MDev-NVMe as a Linux kernel module. This paper subsequently evaluates MDev-NVMe with Intel OPTANE and P3600 SSD by comparing several mainstream NVMe virtualization mechanisms using application-level I/O benchmarks. MDev-NVMe with active polling can demonstrate a 142% improvement over native (interrupt-driven) throughput and over 2.5 × the Virtio throughput with only 70% native average latency and 31% Virtio average latency. Finally, the advantages of MDev-NVMe and the importance of adaptive polling are discussed, offering evidence that MDev-NVMe is a superior virtualization choice for cloud storage.

Nowadays the cloud computing grows in high rate with the help of recent technological advancement. It provides the on demand and low cost services to the customer. Cloud computing distributes the services to various location based on the demand. The services can cross various layers and location which leads the security problem. The existing security system provides reliable solutions which are not enough to secure the resource and services from unauthorized access. Three types of security is available in the cloud namely Infrastructure security, Platform security and application security. The proposed analysis gives the complete information about these security and its issues. Data security management process has various steps for securing the data around the cloud network with reliable access. Virtualization level security is analyzed with different parameter based on the number issues present in the cloud. Authentication types are also identified and analyzed over security level. Various types of cloud framework and vulnerabilities are assessed in order to achieve better security.

Green IT thrives in industries that focus on environmental sustainability. However, currently green IT is starting to be implemented in the industry in general, so it is necessary to explain the concepts and indicators that explain green IT. Based on the phenomenon of green IT, this study aims to examine the antecedents of green IT and their impact on competitive advantage. The research was conducted through a survey of companies that focus on environmental sustainability in Indonesia. Data from companies is taken quantitatively through a questionnaire with a focus on examining green IT and competitive advantage. The results of the study found that there are several factors that can be used as measurements for green IT including procurement policy, storage consolidation, storage virtualization, server virtualization, managing electronic waste policy, and measuring the environmental impact. The implementation of green IT has a good relationship in supporting the achievement of the company's competitive advantage. Findings from the research can be used as information for companies in implementing green IT.

As a file system ages, it can experience multiple forms of fragmentation. Fragmentation of the free space in the file system can lower write performance and subsequent read performance. Client operations as well as internal operations, such as deduplication, can fragment the layout of an individual file, which also impacts file read performance. File systems that allow sub-block granular addressing can gather intra-block fragmentation, which leads to wasted free space. Similarly, wasted space can also occur when a file system writes a collection of blocks out to object storage as a single large object, because the constituent blocks can become free at different times. The impact of fragmentation also depends on the underlying storage media. This article studies each form of fragmentation in the NetApp ® WAFL ® file system, and explains how the file system leverages a storage virtualization layer for defragmentation techniques that physically relocate blocks efficiently, including those in read-only snapshots. The article analyzes the effectiveness of these techniques at reducing fragmentation and improving overall performance across various storage media.

Hypervisor managed SSD caching is an often used technique for improving IO performance in virtualization based hosting solutions. Such caches are either explicitly managed by the hypervisor which approximate the access semantics of the applications for improving cache utilization, or operate as statically partitioned devices (which are utilized as caches) by virtual machines. We reason that both these broad directions do not exploit the potential of SSD based IO caches to the fullest, in terms of generalized management policies and performance. We propose SymFlex, a novel method to perform symbiotic management of IO caches by enabling elastic SSD devices. Each virtual machine is configured with an elastic virtual SSD whose contents can be managed according to guest OS and application semantics and requirements. Furthermore, the SSD sizing is managed by the hypervisor with a ballooning-like mechanism to dynamically adjust SSD provisioning to VMs based on performance and usage fairness policies. Our primary contribution of this work is to design and engineer the mechanism for elastic SSD disks to be virtualized, and demonstrate usage models and effectiveness of the symbiotic management of SSD caches across virtual machines. Through our empirical evaluation, we show that the overhead of implementing a virtio-based elastic SSD device is minimal (within 5% of virtio based device virtualization techniques). Further, we demonstrate using dm-cache and Fatcache, the applicability and benefits of SymFlex for enhancing IO throughput and enforcing VM-level SSD allocation policies.

Cloud computing technology nowadays is a leading IT technology and introduces the next generation of distributed systems with a great acceptance and impact in the businesses. It influences the businesses on structural and operational processes with potential to reduce total costs and increase revenues. This technology is mainly service oriented and focuses on cost reduction, like hardware reduction, reduction on maintenance and total cost of ownership, and with different pricing models such as pay-per-use. Virtualization is the foundation process and technology in Cloud computing, as it hides the complexity of underlying hardware and software resources. It is the objective of virtualization to achieve an improvement in data security aspects, to provide flexible operations and data transfer, elimination (masking) of system failures and to ensure economic benefits. The aim of this research is through a systematic literature review of the existing work to provide a comprehensive understanding of the role and significance of virtualization within the domain of Cloud Computing.

Virtualization is a framework or methodology that is used to divide the resources of a computer into multiple execution environments by applying concepts or technologies storage virtualization, client virtualization, and server virtualization are different ways to achieve virtualization. Our goal in this paper is to implement server virtualization with attacks and threats with possible solutions. Server virtualization is best suitable for dividing a server into a multiple virtual servers via software such as VMware and Hyper V. This method is used to increase the resource utilization by performing the same. In this each virtual server act and look like a physical server by increasing the capacity of every single physical machine (Kuche et al. in Int J Comput Netw Wireless Mobile Commun (IJCNWMC) 4:5–10 (2014), [1] with problems like efficient resource management, more downtime and privacy and security of data are associated with server virtualization. The problem is unauthorized users are accessing the access of machine/server in VMware platform. This can be done by proposing model named object level permission model to provide security by assigning permissions and roles as per level.

SANMAN – Storage Area Network Management Software is an in-house developed Linux based software to manage, monitor and configure SAN based high capacity storage systems which are built over off the shelf commodity hardware. SANMAN is designed and developed using open source software components providing features of storage virtualization, volume management, access control and snapshot offered by all commercial proprietary SAN solutions. To ensure business continuity and disaster recovery in storage systems deployed on non-commercial hardware along with SANMAN, a remote replication module is developed and integrated with the software. SANMAN facilitates software defined orchestration and monitoring of storage resources. This paper discusses the implementation of remote replication module in detail which is developed using DRBD-9 (Distributed Replicated Block Device) software facilitating remote replication and automatic failover.

This paper proposes a data storage method that cost-efficiently manages a massive amount of short lifetime data continuously generated typically by IoT devices (called Live Data, e.g., surveillance camera images) considering real-time data retrieval. Along with the rapid growth of the IoT, many users expect to shift to an Open IoT in which they can share enormous amounts of Live Data. To efficiently use Live Data, it is important to use both hot and cold data storage. Data should be stored in cold storage, which is less expensive but has slow access speeds than hot storage, only if the data are unlikely to be used. The proposed method virtualizes distributed hot storage to provide two data placement mechanisms; (1) an original-data-only optimized allocation according to demand by migrating original data to the appropriate distributed hot storage, and (2) integrated data replacement in the case of capacity overflow by migrating data to the distributed hot storage storing less useful data. As a result of a performance evaluation, compared with a cache algorithm often used for traditional data, the proposed method can reduce operational costs (transmission costs for data migration and retrieval and miss–hit penalty costs) when the ratio of single miss–hit penalty costs to 1-hop transmission costs is more than 6.

In order to implement huge data storage of high performance in embedded environment, more and more SSDs with Non-Volatile Memory Express (NVMe) standard are deployed and virtualized on heterogeneous multi-core platforms. Existing solutions of emulating NVMe SSD between guest operating system (OS) and remote OS rely on virtual machines (VMs), which bring in long I/O latency due to their expensive context switch and extra data transfer. In this paper, we propose NVMe over remote processor messaging (NVMe-over-RPMsg), a software framework that emulates remote storage system as local NVMe device, without requiring VMs. We implemented a prototype using ZCU102 FPGA board. The evaluation results show that NVMe-over-RPMsg has 45.4% reduction in latency and 1.74X improvement in throughput than native QEMU solution.

Storage management is a challenging issue due to exponential growth of data and computing workloads transacted per day. Although storage virtualization plays a prominent role in addressing this problem, there exist complexities in the adoption process of virtualization technologies because the transition from a non-virtualized to a virtualized environment is not always a smooth process. Using a survey of 24 public and private institutions in Tanzania, the organizational readiness for managing data storage in virtualized servers is studied. While there is a satisfactory awareness level for most adopters of the existing virtual-based data storage technologies and disk partition techniques, the attention given to securing adopter’s practices for virtual resource allocation is inefficient for countering virtual machine attacks. Adopters are prone to starve their virtual machines as soon as their computing workloads expand. The study reveals a serious ad hoc allocation of virtual HDDs with high discrepancy between adopters from the same sector and with similar IT infrastructure, and computing level and demands. Organizations are prompted about the limitations of their current practices which would hardly bring maximum benefits of storage virtualization when expanding from small- to large-scale data workloads.

This paper is tied in with reproducing framework as an assistance with the assistance of capacity virtualization. Virtualization as far as distributed computing is characterized as a manner by which all workers and applications that are required for the other cloud suppliers are taken care of and administered by an outsider and this administration can be utilized on different machines simultaneously, making it conceivable to see the virtual picturization of the capacity gadgets. As a major aspect of this is reenactment two locales will be utilized, one site for On-premise organize and can be actualized with the assistance of VMware professional. We have the Amazon Web Services as the second webpage which offers types of assistance, for example, programming, a stage and a foundation. It tends to be executed in organizations that is anticipating limit their expense on framework so that in least cost most extreme execution productivity can be gotten.

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.

Modern data centers usually use virtual machine technology to host various big data applications in a single physical machine, not only enhancing the server utilization, but also providing them with the hardware-level isolation. However, in a typical virtualized environment an extra software layer called virtual machine monitor (VMM) is often interposed between the hardware resource and guest operating system (virtual machine, VM), shielding the specific user-process semantic inside a running VM. As a result, it obstructs the disk I/O scheduler of VMM to acquire the accurate information of a user-process (often a big data application), and thus proposes a challenge on the I/O request scheduling as well as the disk resource management at the granularity of VM user-process. Eventually, the disk I/O performance of a virtualized system is sub-optimal. This paper introduces an improved disk I/O scheduling framework for the virtualized system. It aims at bridging the semantic gap between physical disk I/O scheduler and VM user-process, providing a fair sharing of disk I/O resource among concurrent VMs. At the same time, it improves the overall disk I/O performance through a novel method for creating the image file of VM. Besides, an extra scheduling algorithm is proposed to further refine the storage performance. Finally, we implement these improvements on Xen hypervisor and conduct extensive experiments to verify our framework. The experimental result shows that our work improve the performance of read-intensive, write-intensive and mixed workloads up to 9, 10.7 and 20% respectively.

Cloud computing has become a compelling paradigm built on compute and storage virtualization technologies. The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Given the recent booming of the container ecosystem, the container-based virtualization starts receiving more attention for being a promising alternative. Although the container technologies are generally considered to be lightweight, no virtualization solution is ideally resource-free, and the corresponding performance overheads will lead to negative impacts on the quality of Cloud services. To facilitate understanding container technologies from the performance engineering's perspective, we conducted two-stage performance investigations into Docker containers as a concrete example. At the first stage, we used a physical machine with “just-enough” resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Moreover, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed. At the ongoing second stage, we employed a physical machine with “fair-enough” resource to implement a container-based MapReduce application and try to optimize its performance. In fact, this machine failed in affording VM-based MapReduce clusters in the same scale. The performance tuning results show that the effects of different optimization strategies could largely be related to the data characteristics. For example, LZO compression can bring the most significant performance improvement when dealing with text data in our case. (Less)

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.

More and more infrastructure is becoming virtualized. Recently this trend has begun to include network functions - such as firewalls, WAN optimizers, and intrusion prevention systems - that have traditionally been implemented as middleboxes using dedicated hardware. This trend towards network function virtualization (NFV) offers a variety of potential benefits that resemble those of cloud computing, including consolidation, easier management, higher efficiency, and better scalability. However, current cloud technology is not a perfect match for NFV workloads: since the infrastructure is shared, the time it takes for a packet to pass through a particular function is no longer predictable, and can in fact vary considerably. This is causing headaches for operators, who can no longer treat network functions as "bumps in the wire" and must now consider a complex web of possible interactions and cross-talk when operating or diagnosing their systems. In this position paper, we propose a compositional approach towards building a scalable NFV platform that can provide latency and throughput guarantees using timing interfaces. We discuss our preliminary results that leverage and extend recent advances on timing interfaces and compositional theory from the real-time systems domain to the NFV setting, and we highlight open challenges and potential directions towards real-time NFV.

Hardware consolidation in the datacenter often leads to scalability bottlenecks from heavy utilization of critical resources, such as the storage and network bandwidth. Client-side caching on durable media is already applied at block level to reduce the storage backend load but has received criticism for added overhead, restricted sharing, and possible data loss at client crash. We introduce a journal to the kernel-level client of an object-based distributed filesystem to improve durability at high I/O performance and reduced shared resource utilization. Storage virtualization at the file interface achieves clear consistency semantics across data and metadata, supports native file sharing among clients, and provides flexible configuration of durable data staging at the host. Over a prototype that we have implemented, we experimentally quantify the performance and efficiency of the proposed Arion system in comparison to a production system. We run microbenchmarks and application-level workloads over a local cluster and a public cloud. We demonstrate reduced latency by 60% and improved performance up to 150% at reduced server network and disk bandwidth by 41% and 77%, respectively. The performance improvement reaches 92% for 16 relational databases as clients and gets as high as 11.3x with two key-value stores as clients.

Virtualization technology starts becoming more and more widespread in the embedded space. The penalties incurred by standard software-based virtualization is pushing research towards hardware-assisted solutions. Among the existing commercial off-the-shelf technologies for secure virtualization, ARM TrustZone is attracting particular attention. However, it is often seen with some scepticism due to the dual-OS limitation of existing state-of-the-art solutions. This letter presents the implementation of a TrustZone-based hypervisor for real-time embedded systems, which allows multiple RTOS partitions on the same hardware platform. The results demonstrate that virtualization overhead is less than 2 percent for a 10 milliseconds guest-switching rate, and the system remains deterministic. This work goes beyond related work by implementing a TrustZone-assisted solution that allows the execution of an arbitrary number of guest OSes while providing the foundation to drive next generation of secure virtualization solutions for resource-constrained embedded devices., This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia – (grant SFRH/BD/91530/2012 and UID/CEC/00319/2013).

The recent decade has witnessed an evolution toward virtualization of everything for the IT industry. Resources, from utility resources to networking components and functions, are abstracted as logical or virtual services. Virtualization results in elastic, agile, and automated resource provision, and facilitates the resource pricing in a pay-as-you-go model. Among this wave, the optical community has made parallel efforts in virtualizing resources in optical networks including both optical node and link resources. A virtualized optical network can not only seamlessly support automated resource provision, but also supply high-bandwidth any-to-any connectivity for network virtualization. In this paper, we overview the motivations and architecture for optical-based network virtualization. Among candidate optical networking paradigms, we argue that spectrum-sliced elastic optical path (SLICE) networks can be considered as a promising substrate choice, and study the key enabling problem, namely optical virtual network embedding over SLICE networks (OVNE-SLICE). We prove the NP-completeness of the OVNE-SLICE problem, and propose two mathematical models for this problem based on a concept named path-channel . The models are evaluated in our simulation, and compared with alternative models proposed in the literature.

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.

With the increasing popularity of virtual machine monitoring (VMM) technologies, performance variability among collocated virtual machines (VMs) can easily become a severe scalability issue. Particularly, it becomes a necessary for administrative team to control the performance degradation level in a shared environment when multiple I/O-intensive applications simultaneously request their I/O operations [1]. Nevertheless, adding several logical layers between the running applications and the physical storage system, as seen in contemporary virtualized storage devices, makes it considerably difficult to build a low overhead controlling mechanism for such systems (while each VM may running a separate operating system instance) [2]. In this paper, we propose a strategy based on control theory for managing the performance of several I/O requests, such as mean response times and read/write throughput in a consolidated environment where multiple virtual services can share access to a storage system. This scheme uses an approach for measuring the characterization of read/write performance attributes of each virtual services and also takes into account the run-time quality of service enforcement levels requested by them. This is formulated as an optimization problem where a reward function is defined to reduce the overall QoS violation incidents among all consolidated virtual services. Performance evaluation is carried out by comparing the proposed solution with the default embedded Linux controller across a range of emulated application workloads in scenarios with multiple consolidated virtual containers. The results confirm that the proposed solution can reduce the overall QoS violation incident rates in scenarios in which the platform operates at a significant traffic load comparing to the default policy in LXC engine.

Persistent memory's (PM) byte-addressability and high capacity will also make it emerging for virtualized environment. Modern virtual machine monitors virtualize PM using either I/O virtualization or memory virtualization. However, I/O virtualization will sacrifice PM's byte-addressability, and memory virtualization does not get the chance of PM image management. In this paper, we enhance QEMU's memory virtualization mechanism. The enhanced system can achieve both PM's byte-addressability inside virtual machines and PM image management outside the virtual machines. We also design pcow, a virtual machine image format for PM, which is compatible with our enhanced memory virtualization and supports storage virtualization features including thin-provision, base image and snapshot. Address translation is performed with the help of Extended Page Table (EPT), thus much faster than image formats implemented in I/O virtualization. We also optimize pcow considering PM's characteristics. The evaluation demonstrates that our scheme boosts the overall performance by up to 50x compared with qcow2, an image format implemented in I/O virtualization, and brings almost no performance overhead compared with the native memory virtualization.

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.

Following the progressive development of IT technology, on-premise IT resources have been shifted to cloud computing environments. The principle reason for this change in IT resource-composing environments is that cloud computing services allow IT resources to be used as and when necessary, which means without buying hardware equipment. For this reason, studies on diverse aspects are being conducted for better security, rapidity, availability, reliability, and elasticity of cloud computing. Among the virtualization technologies that are basic for cloud computing, desktop storage virtualization (DSV) is composed of distributed legacy desktop personal computers. In DSV environments, clustering by unavailable state time and auto-scaling for storage provision as requested by users are considered very important. In addition, deferred processing for analysis of desktop PC performance states in DSV environments to select an appropriate desktop PC is directly connected to the quality of service (QoS). Although diverse algorithms and schemes for clustering and auto-scaling have been developed to this end, they have limited performance or have been made without considering DSV environments. Consequently, large amounts of deferred processing time are required. In the present paper, an efficient auto-scaling scheme (EAS) is proposed that minimizes deferred processing time in Internet of Things (IoT) environments by using many-cores of the GPU for clustering and auto-scaling in DSV environments. The EAS provides higher QoS to storage users compared to the CPU by mapping the information of numerous distributed desktop PCs on individual threads of the GPU and processing the information in parallel.

The expanding interest for storage and computation has driven the development of vast information centers–the gigantic server cultivates that run huge numbers of today's Internet and business applications. This work proposes another upgraded design for storage virtualization. In a local customer server environment stores information, as well as oversees information appropriately. The principle center of this work is to accomplish four noteworthy goals. In the first place, security parameter where another chronicled data based approach is utilized which stores the MAC address so that there is one time security check along these lines lessening the time and expanding the property. Second metadata creation, in this duplication is uprooted by evacuating the copy words and utilizing the new indexing plan to diminish the span of metadata and hunt time where is enhanced the execution of the framework. At long last, the records are compacted with a misfortune less pressure technique and it is being scrambled and put away in the database therefore diminishing the storage room and securing the document by encoding the record.

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.

OS-level virtualization is often used for server consolidation in data centers because of its high efficiency. However, the sharing of storage stack services among the colocated containers incurs contention on shared kernel data structures and locks within I/O stack, leading to severe performance degradation on manycore platforms incorporating fast storage technologies (e.g., SSDs based on nonvolatile memories). This article presents MultiLanes, a virtualized storage system for OS-level virtualization on manycores. MultiLanes builds an isolated I/O stack on top of a virtualized storage device for each container to eliminate contention on kernel data structures and locks between them, thus scaling them to manycores. Meanwhile, we propose a set of techniques to tune the overhead induced by storage-device virtualization to be negligible, and to scale the virtualized devices to manycores on the host, which itself scales poorly. To reduce the contention within each single container, we further propose SFS, which runs multiple file-system instances through the proposed virtualized storage devices, distributes all files under each directory among the underlying file-system instances, then stacks a unified namespace on top of them. The evaluation of our prototype system built for Linux container (LXC) on a 32-core machine with both a RAM disk and a modern flash-based SSD demonstrates that MultiLanes scales much better than Linux in micro- and macro-benchmarks, bringing significant performance improvements, and that MultiLanes with SFS can further reduce the contention within each single container.

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.

We propose the combination of optical network virtualization and network function virtualization (NFV) for deployment of on-demand OpenFlow-controlled virtual optical networks (VON). Each tenant SDN controller is run on the cloud, so the tenant can control the deployed VON. This paper demonstrates the feasibility of the proposed use case and provides implementation details in the ADRENALINE testbed of an NFV orchestrator, which is able to provide multitenancy on top of an heterogeneous transport network by means of network orchestration and virtualization.

Programmability and virtualization have both become the trends of next generation networks. ForCES (Forwarding and Control Element Seperation) is a new programmable framework for network element. We introduce virtualization into ForCES, propose a method of ForCES virtualization, design its framework, implement a prototype system named vForTER, and test its performance in different scenarios. Test results show that we can flexibly build isolated virtual routers in one physical ForCES network element. And the influences to UDP forwarding rate, TCP out of order packets rate, and RTT are very limited. ForCES network elements supporting with both programmability and virtualization can be used as core infrastructure in future networks.

The increasing requirements of intensive interoperaterbility among the distributed nodes desiderate the high performance network connections, owing to the substantial growth of cloud computing and datacenters. Network I/O virtualization aggregates the network resource and separates it into manageable parts for particular servers or devices, which provides effective consolidation and elastic management with high agility, flexibility and scalability as well as reduced cost and cabling. However, both network I/O virtualization aggregation and the increasing network speed incur higher traffic density, which generates a heavy system stress for I/O data moving and I/O event processing. Consequently, many researchers have dedicated to enhancing the system performance and alleviating the system overhead for high performance networking virtualization. This paper first elaborates the mainstreaming I/O virtualization methodologies, including device emulation, split-driver model and hardware assisted model. Then, the paper discusses and compares their specific advantages in addition to performance bottlenecks in practical utilities. This paper mainly focuses on the comprehensive survey of stateof-the-art approaches for performance optimizations and improvements as well as the portability management for network I/O virtualization. The approaches include various novel data delivery schemes, overhead mitigations for interrupt processing and adequate resource allocations for dynamic network states. Finally, we highlight the diversity of I/O virtualization besides the performance improvements in network virtualization infrastructure.

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.

Storage virtualization is an important component of large-scale online services in multi-tenant clouds. It typically shares the physical storage among guest machines and performs transactional operations for high-performance data processing. However, even with the recent mediated pass-through virtualization optimization, the operations of multi-tenant storage I/O meet the bottleneck, and thus degrade the throughput performance of the cloud storage services. We observe that the root cause of the problem is the unawareness of varying and imbalanced workload inefficiency of resource management in the multi-tenant cloud storage setting. In this paper, we present FinNVMe, a new throughput-oriented NVMe storage virtualization management mechanism, that (1) passes-through I/O performance-critical resources and emulates privileged resources to provide high throughput in a workload-aware manner among multi-tenant VMs, (2) enables fine-grained scheduling for I/O resources to achieve promising flexibility and scalability with respective to virtualization, and (3) adopts the queue binding and the queue shuffling to reduce the virtualization and management overhead, and involves active polling for further I/O acceleration. This article subsequently evaluates FinNVMe with micro benchmarks on two typical scenarios (both balanced and imbalanced workload) and the real-world storage workloads to show its high throughput performance, along with the flexibility and scalability of virtualization and resource management. For example, FinNVMe achieves up to 20 percent throughput improvement with more stable latency in the varying and imbalanced workload.

Network virtualization has been a quite active research area in the last years, aiming to tackle the increasing demand for high performance and secure communication in cloud infrastructures. In special, such research eforts have led to security solutions focused on improving isolation among multiple tenant of public clouds, an issue recognized as critical both by the academic community and by the technology Industry. More recently, the advent of Software-Defined Networks (SDN) and of Network Function Virtualization (NFV) introduced new concepts and techniques for addressing issues related to the isolation of network resources in multi-tenant clouds while improving network manageability and flexibility. Similarly, hardware technologies such as Single Root I/O Virtualization (SR-IOV) enable network isolation in the hardware level while improving performance in physical and virtual networks. Aiming to provide a cloud network environment that effciently tackles multi-tenant isolation, we present three complementary strategies for addressing the isolation of resources in cloud networks. These strategies are then applied in the evaluation of existing network virtualization architectures, exposing the security gaps associated to current technologies, and paving the path for novel solutions. We then propose a security architecture that builds upon the strategies presented, as well as upon SDN, NFV and SR-IOV technologies, to implement secure cloud network domains. The theoretical and experimental analyses of the resulting architecture show a considerable reduction of the attack surface in tenant networks, with a small impact over tenants\' intra-domain and inter-domain communication performance. Virtualização de redes é uma área de pesquisa que tem ganho bastante atenção nos últimos anos, motivada pela necessidade de se implementar sistemas de comunicação seguros e de alta performance em infraestruturas de computação em nuvem. Em particular, os esforços de pesquisa nesta área têm levado ao desenvolvimento de soluções de segurança que visam aprimorar o isolamento entre os múltiplos inquilinos de sistemas de computação em nuvem públicos, uma demanda reconhecidamente crítica tanto pela comunidade acadêmica quanto pela indústria de tecnologia. Mais recentemente, o advento das Redes Definidas por Software (do inglês Software-Defined Networks - SDN) e da Virtualização de Funções de Rede (do inglês Network Function Virtualization - NFV) introduziu novos conceitos e técnicas que podem ser utilizadas para abordar questões de isolamento de redes virtualizadas em sistemas de computação em nuvem com múltiplos inquilinos, enquanto aprimoram a capacidade de gerenciamento e a flexibilidade de suas redes. Similarmente, tecnologias de virtualização assistida por hardware como Single Root I/O Virtualization - SR-IOV permitem a implementação do isolamento de recursos de hardware, melhorando o desempenho de redes físicas e virtualizadas. Com o intuito de implementar uma solução de virtualização de redes que aborda de maneira eficiente o problema de isolamento entre múltiplos inquilinos, nós apresentamos três estratégias complementares para o isolamento de recursos de rede em sistemas computação em nuvem. As estratégias apresentadas são então aplicadas na avaliação de arquiteturas de virtualização de rede existentes, revelando lacunas de segurança associadas às tecnologias utilizadas atualmente, e abrindo caminho para o desenvolvimento de novas soluções. Nós então propomos uma arquitetura de segurança que utiliza as estratégias apresentadas, e tecnologias como SDN, NFV e SR-IOV, para implementar domínios de rede seguros. As análises teórica e experimental da arquitetura proposta mostram considerável redução das superfícies de ataque em redes virtualizadas, com um pequeno impacto sobre o desempenho da comunicação entre máquinas virtuais de inquilinos da nuvem.

The core elements of any data center are: compute, network, and storage. These elements operate in silos which had led to many operational inefficiencies in data centers. This has forced organizations to relook at ways in which the various infrastructure elements can be utilized in an optimal manner. Virtualization technology has proved very handy to ensure that all infrastructure components of the data center are used in an efficient manner. The various flavors of virtualization which are possible in the infrastructure components of the data center are the following: (1) compute virtualization, (2) network virtualization, (3) storage virtualization. In this chapter, we have focused exclusively on network virtualization technology and also the various network virtualization techniques. The various network virtualization techniques which are covered in this chapter are the following: (1)network functions virtualization, (2) Software-Defined Network

Grid systems and cloud servers are two distributed networks that deliver computing resources (e.g., file storages) to users' services via a large and often global network of computers. Virtualization technology can enhance the efficiency of these networks by dedicating the available resources to multiple execution environments. This chapter describes applications of virtualization technology in grid systems and cloud servers. It presents different aspects of virtualized networks in systematic and teaching issues. Virtual machine abstraction virtualizes high-performance computing environments to increase the service quality. Besides, grid virtualization engine and virtual clusters are used in grid systems to accomplish users' services in virtualized environments, efficiently. The chapter, also, explains various virtualization technologies in cloud severs. The evaluation results analyze performance rate of the high-performance computing and virtualized grid systems in terms of bandwidth, latency, number of nodes, and throughput.

There is an executive’s guide to the software-defined data center (SDDC) published by TechRepublic, USA. This document details and describes the nitty-gritty of next-generation Cloud centers. The motivations, the key advantages, and the enabling tools and engines along with other relevant details are being neatly illustrated there. An SDDC is an integrated abstraction layer that defines a complete data center by means of a layer of software that presents the resources of the data center as pools of virtual and physical resources and allows their composition into arbitrary user-defined services. A modern SDDC deployment is defined by virtualized, software-defined resources that can be scaled up or down as required and can be deployed as needed in a number of distinct ways. There are three key components to the SDDC: (1) software-defined computing, (2) Software-Defined Network, and (3) software-defined storage. This chapter is to describe how the tools-assisted and automated software-defined environments are going to be the game-changer for businesses and governments.

The proliferation of Machine-to-Machine (M2M) communication is exponentially increasing the amount of unstructured data that is generated in the digital universe. It is estimated that face book generates roughly about 1 TB of data every day and most of it is unstructured data. The storage infrastructures like Storage Storage Area Networks (SAN) and Network-Attached Storage (NAS) are not designed to store and process unstructured data. Hence, it is the need of the day to design storage devices and networks which are robust enough to scale and accommodate huge amount of unstructured data without causing any performance impact. However, any present-day storage infrastructure like software-defined storage uses the traditional storage technologies like NAS and SAN as their underlying foundation. Hence, it is impossible to understand software-defined storage platforms without proper understanding of underlying technologies. In this chapter, we are explaining the foundations of storage technologies in the beginning and, later on, we are doing a deep dive to understand how their design has been transformed in such a way that they can store and process huge volumes of data in the present-day data centers using the concept of software-defined storage. In most of the scenarios, multiple storage platforms are combined and then some kind of enhancement is added to make them compatible to handle large volumes of data. In the first half of the chapter, we are examining the storage technologies like Direct-Attached Storage (DAS), NAS, and SAN. Software-defined storage refers to the use of storage virtualization and policy-based provisioning techniques to ensure that data storage is done in a cost-optimized and performance-optimized manner. One of the main reasons for the evolution of software-defined storage is the necessity to store and process big data. In the second half of the chapter, we discuss and focus on all techniques related to software-defined storages like Cloud storage and storage tiering and we also cover latest storage technologies which have been designed and optimized for big data processing like PANASAS, GFS, and HDFS

Virtualization is the act of creating a virtual version of computing hardware, storage devices, and computer network resources. There are three classifications of virtualization technologies. The first classification is based on the internal process model. The second classification covers most of virtualization techniques at different levels of implementation within a computer. The third classification introduces two types of hypervisor. Besides virtualization through hypervisors, the container is a process-based lightweight virtualization, which has gained its momentum recently due to its efficiency and mobility. Besides computer virtualization, network virtualization (e.g., virtual networks, SDN, NFV, etc.), mobile virtualization (e.g., KVM over ARM), and storage virtualization (e.g., blob store, file store, etc.) are also important virtualization technologies. In this chapter, various virtualization techniques are surveyed, which form the foundation to build cloud computing service models.