Virtual Machine Images Research Articles

Dual-energy computed tomography angiography-based quantification of lesion net water uptake to identify stroke onset time

ObjectivesTo explore whether dual-energy computed tomography (DECT) angiography can provide reliable quantitative information on net water uptake (NWU) of ischemic brain to identify stroke patients within 4.5 h. MethodsWe retrospectively reviewed 142 patients with stroke occurrence and who underwent DECT angiography between August 2016 and May 2022. DECT angiography manual drawn the ischemic area by referring to the normal area of the contralateral hemisphere and follow-up images. The NWU in the ischemic area was determined using virtual non-contrast and monoenergetic (VNC &VM) images acquired from DECT angiography. The NWU values in the ischemic area were compared between stroke patients within and beyond 4.5 h. The diagnostic performance of the NWU values derived from the VNC and VM images was assessed through receiver operating characteristic curve analysis. Additionally, Furthermore, we examined the correlation between the NWU values and the stroke onset time. ResultsSeventy-eight (54.93 %) stroke patients underwent DECT angiography and within 4.5 h. These patients with lower median National Institute of Health stroke scale (NIHSS) scores on admission than those beyond 4.5 h (p < 0.05). Furthermore, the group within 4.5 h had lower NWU values than did the group beyond 4.5 h on all VNC and VM images (p < 0.001). The analysis revealed that the NWU values determined using the VM (60 keV) images had the highest predictive efficiency (AUC, 0.95; sensitivity, 100 %; and specificity, 89.06 %) and showed the strongest positive correlation with stroke onset time (r-value = 0.58, p < 0.001). ConclusionsOur findings showed that DECT angiography-based quantification of NWU helps identify the stroke patients within 4.5 h with high predictive efficiency. Thus, NWU values determined using VM (60 keV) images could serve as a significant biomarker for stroke onset time.

A freely distributable professional computed tomography system using NICOS

Based on the control system of the ANTARES neutron imaging system at FRM II, we have designed a portable, but professional neutron computed tomography system using the same TANGO device servers and the instrument control system NICOS. We have created a minimalistic setup to control a computed tomography setup using a single control computer that directly connects to a camera detector and interfaces a Raspberry Pi computer with motor control board via a local network. The setup has the full capability of a large instrument controller and can control arbitrary hardware (like professional motor controllers) for which hundreds of free drivers are available. Full schematics for the controller as well as print files for 3D printers for the detector housing and disk or virtual machine images are available for free to allow for the quick implementation of a low-cost, but professional neutron CT system even at very low power research reactors.

GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction.

High-throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request.

Community-wide collaboration is a must to reinstall trust in bioinformatics solutions and biomedical interpretation.

Science has become a highly competitive undertaking concerning, for example, resources, positions, students, and publications. At the same time, the number of journals presenting scientific findings skyrockets while the knowledge increase per manuscript seems to be diminishing. Science has also become ever more dependent on computational analyses. For example, virtually all biomedical applications involve computational data analysis. The science community develops many computational tools, and there are numerous alternatives for many computational tasks. The same is true for workflow management systems, leading to a tremendous duplication of efforts. Software quality is often of low concern, and typically, a small dataset is used as a proof of principle to support rapid publication. Installation and usage of such tools are complicated, so virtual machine images, containers, and package managers are employed more frequently. These simplify installation and ease of use but do not solve the software quality issue and duplication of effort. We believe that a community-wide collaboration is needed to (a) ensure software quality, (b) increase reuse of code, (c) force proper software review, (c) increase testing, and (d) make interoperability more seamless. Such a science software ecosystem will overcome current issues and increase trust in current data analyses.

Hepatic lesion detectability in abdomen computed tomography: Investigation in low kVp single energy and low keV virtual monochromatic images generated from dual energy computed tomography using task-based image quality assessment in phantom

Background: Low tube potential single-energy (SE) and virtual monochromatic image (VMI) dual energy (DE) abdomen CT images both improve the hepatic lesion detection by increasing the liver lesion contrast on images. Objectives: To study the performance of low kVp single energy and low keV virtual monochromatic images (VMI) generated from dual energy acquisition to detect the hepatic lesion on abdominal CT imaging in phantom. Materials and methods: The anthropomorphic liver with nodule inserted phantom with extension rings simulating the small, medium, and large patient was scanned under the SECT acquisition by varying the kVp from 70-120 kVp and for DECT acquisitions, three kVp combinations (80/-,90/-, and 100/Sn150-kVp). The series of 40-,50-,60-, and 70-keV VMI were generated from DECT data set. All images were used to assess the task-based image quality; task transfer function (TTF), noise power spectrum (NPS), and detectability index (d’) with the diagnostic task to detect 15 mm diameter hyperattenuating hepatic lesion. Results: The result showed that the TTF was higher in low kVp SECT while the lesion contrast was higher at low keV VMI -DECT. The noise magnitude remained constant for all kVp values in SECT, but it was dramatically increased as decreased the energy level from 70- to 40-keV in VMI-DECT. The fav of NPS shifted to higher frequency when increasing the kVp and when increasing the energy level of VMI. The obtained d’ was highest in low kVp SECT at 70-or 80-kVp. Conclusion: The low kVp SECT provided the highest d’ than that in low keV VM image from DECT in all phantom sizes where the highest d’ was found at 70 kVp-SECT for small and medium phantom and at 80 kVp for large phantom. For SECT, reduced the kVp to 70- or 80-kVp improved the detectability index. The kVp combination in DECT impacts to the d’ of VMI; at small phantom, the highest d’ for each keV VMI was found at 80/Sn150 kVp acquisition and at larger phantom size, higher kVp on tube “A” is more favored.

Secure Virtual Machine Image Storage Process into a Trusted Zone-based Cloud Storage

The emerging trend of Cloud Computing inducts virtualization as the quintessence concept to offer to end users a large scale Infrastructure-as-a-Service (IaaS) paradigm. Virtualization leverages virtual environment instantiated from user namely Virtual Machine Images (VMI) where user has the ability to provision and manage its own operating system. Therefore, user VMI dynamic data privacy depends upon the supplied security policy by Cloud Service Provider (CSP) and cloud storage architecture which remains the most prominent cloud delivered services. This paper covers thoroughly a secure storage process of user VMI in a cloud zone-based storage based on Proof-of-Retrievability (PoR). The proposed security architecture ensures the elementary security triad CIA (Confidentiality, Integritty and availability). VMI data confidentiality comprises generation of encryption key pair based on Goppa Code and McEliece cryptosystem, thus, integrity and stored VMI dynamic data correctness have been ensured using public auditing privacy-preserving in cloud zone-based storage. a know practical Error-Correcting-Codes (ECC) in heuristic researches

An Enhanced and Reliable Cloud Trust Protection Model for Privacy Data

The vulnerability of Cloud Computing Systems (CCSs) to Advanced Persistent Threats (APTs) is a significant concern to government and industry. We present a cloud architecture reference model that incorporates a wide range of security controls and best practices,and a cloud security assessment model – Cloud-Trust – that estimates high level security metrics to quantify the degree of confidentiality and integrity offered by a CCS or cloud service provider (CSP). Cloud-Trust is used to assess the security level of four multi- tenant IaaS cloud rchitectures equipped with alternative cloud security controls and to show the probability of CCS penetration (high value data compromise) is high if a minimal set of security controls are implemented. CCS penetration probability drops substantially if a cloud defense in depth security architecture is adopted that protects virtual machine (VM) images at rest, strengthens CSP an cloud tenantsystem administrator access controls, and which employs other network security controls to minimize cloud network surveillance and discovery of live VMs. Keywords: - Cloud Computing, Trust, Trust Management, Trust Models, Privacy, Trust metric, enables CSPs to start, stop, move, and restart

Securing Cloud Virtual Machine Image Using Ethereum Blockchain

Virtual Machine Image (VMI) is the building block of cloud infrastructure. It encapsulates the various applications and data deployed at the Cloud Service Provider (CSP) end. With the leading advances of cloud computing, comes the added concern of its security. Securing the Cloud infrastructure as a whole is based on the security of the underlying Virtual Machine Images (VMI). In this paper an attempt has been made to highlight the various risks faced by the CSP and Cloud Service Consumer (CSC) in the context of VMI related operations. Later, in this article a formal model of the cloud infrastructure has been proposed. Finally, the Ethereum blockchain has been incorporated to secure, track and manage all the vital operations of the VMIs. The immutable and decentralized nature of blockchain not only makes the proposed scheme more reliable but guarantees auditability of the system by maintaining the entire VMI history in the blockchain.

Virtual Consolidation of Resource Sharing in Green Cloud using an Efficient Meta Scheduling Algorithm

In modern researchers, cloud parallel data processing has emerging resource that to be one of the problematic application for Infrastructure-as-a-Service (IaaS)clouds. Major Cloud processing companies include starting incorporate frameworks using VM models for parallel data processing in their resource portfolio creation too easy for a client to access these services and to set out their programs. The growing computing requires from multiple requests on the main server has led to excessive power utilization. The waiting resource in the long-term sustainability of Cloud like infrastructures in provisions of energy cost but also from cloud environmental perspective. The trouble can be addressed to require with high energy consumption resource sharing infrastructures, but in the process of resources are dynamically switch to new infrastructure. Switching is not enough to cost efficient and also need time sharing green consuming. Cloud being consists of several virtual centers like VM's under the different administrative domain, make a problem more difficult. Thus, for the reduction in energy consumption, this propose address the challenge by effectively distributing compute-intensive parallel applications on the cloud. To propose a Meta-scheduling algorithm, this exploits the heterogeneous nature of Cloud to achieve the reduction in energy consumption as the green cloud. This intent addresses these challenges by proposing a virtual file system specifically optimized for virtual machine image storage. It is based on a lazy transfer scheme coupled with object versioning that handles snapshot transparently in a hypervisor independent fashion, ensuring high portability for different configurations.

ИСПОЛЬЗОВАНИЕ МИКРОЯДЕРНЫХ СРЕДСТВ ВИРТУАЛИЗАЦИИ ДЛЯ ОБЕСПЕЧЕНИЯ БЕЗОПАСНОСТИ СИСТЕМ С МИКРОСЕРВИСНОЙ АРХИТЕКТУРОЙ

Purpose of the article: to increase fault tolerance, security level and simplify the process of updating systems using a micro service architecture using virtualization tools. Method: Using microkernel virtualization tools called Unikernel images. Unikernel are virtual machine images containing a single application. Each micro service of the system runs in the Unikernel image, the processes and services used are not contained in the image. The result: a definition of Unikernel is given; a comparison is made with existing virtualization systems. The mechanisms that implement the Unikernel technology are listed. The definitions of the library operating system and microkernel are given. The definition of Unikernel is given as a technology that combines library operating systems and microkernels. The main advantages of using Unikernel are listed: ensuring the security of information systems and ease of updates. A developed software tool for testing Unikernel images for the possibility of penetrating an information system in the event of a vulnerability of the “remote execution of arbitrary code” type in the software tool. The image was tested. In conclusion, the assessment of the effectiveness of the use of Unikernel technology for building systems with a micro service architecture, in particular for building SIEM systems.

Toward Virtual Machine Image Management for Persistent Memory

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 article, 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-provisioning, base image, snapshot, and striping. Address translation is performed with the help of the Extended Page Table, thus much faster than image formats implemented in I/O virtualization. We also optimize pcow considering PM’s characteristics. We perform exhaustive performance evaluations on an x86 server equipping with Intel’s Optane DC persistent memory. The evaluation demonstrates that our scheme boosts the overall performance by up to 50× compared with qcow2, an image format implemented in I/O virtualization, and brings almost no performance overhead compared with the native memory virtualization. The striping feature can also scale-out the virtual PM’s bandwidth performance.

Optimal task replication considering reliability, performance, and energy consumption for parallel computing in cloud systems

In a cloud-based cyber–physical system, many jobs consist of multiple parallel tasks. The cloud system usually adopts active task replication to improve performance and guarantee the reliability of a job. This technology creates redundant replicas for each task and then executes the replicas concurrently. In the cloud system, each replica is a virtual machine (VM) image that can be easily assigned to different physical machines (PMs) to overcome resource heterogeneity. However, how to design a rational task replication strategy (including replica creation and VM assignment) is indeed a complex issue. It should comprehensively consider correlations and tradeoffs among reliability, performance, and energy consumption. This paper first proposes a reliability–performance correlation model for a job executed by using active task replication. We design a general method to avoid analyzing complex failure correlations and give a Bayesian approach to calculate the performability metric of the job. The paper also proposes a reliability–energy correlation model to evaluate random energy consumption of a PM hosting multiple VMs by using mixed random variables. Finally, an expected net profit optimization model and a genetic algorithm are developed to search for an optimal task replication strategy balancing tradeoffs among reliability, performance, and energy consumption. Illustrative examples are provided.

Practical aspects of implementing the IRIDA system as a solution for One Health bioinformatics analyses

Managing sequence data, associated metadata, bioinformatics analyses and results can be challenging. In a One Health context, the challenge is even larger as there are many actors involved, many diverse types of results need to be produced, and the ensuing process data, such as software versions and options have to be tracked for auditing purposes. In addition, results must often be produced rapidly to be actionable, and non-bioinformaticians should be able to perform the the analyses. Therefore, a graphical user interface (preferably web system) with pipelines and visualization tools are needed to do these analyses. The Public Health Agency of Canada has together with other actors developed the web based system IRIDA (https://www.irida.ca) which uses Galaxy for analyses. IRIDA comes with a set of pipelines, visualization tools and a project based data management system that allows for fine grained data access control, which satisfies many of the requirements that a One Health bioinformatics platform dictates. However, as is often the case with a system meant to satisfy high demands, the platform is not trivial to set up and adapt for local use. In our setup, we are using two web servers, two database servers and one file server. The IRIDA web server provides the user interface. The Galaxy web server receives commands from IRIDA, executes the commands and returns results. Each web server has a database that keeps their respective metadata: user information, file locations and results. The actual files are stored on the fileserver. This spoke-and-wheel infrastructure was implemented to ensure minimum disruption of service if a component should go down. To get the necessary compute resources for this system, we are contracting with the Norwegian Research and Education Cloud (NREC), which offers Infrastructure as a Service (IaaS) services for Norwegian institutions and universities. NREC utilizes template VM images which can be instantiated according to need. The automated configuration and orchestration of images ensure that we can have dynamic access to resources according to need. This dynamic scaling is accomplished through collaboration with Elixir Norway. They have implemented the Pulse software which can check usage and instantiate and take down virtual machines as needed. At the Institute, we have spent close to two years on exploring and setting up this system. We have learned that it is important to not underestimate the amount compute resources needed to get a solid setup. However, having enough compute is irrelevant without knowledgeable staff. IRIDA comes with many features, which require considerable prior knowledge to adapt and set up in a local infrastructure. This includes knowledge on webservers, database systems, linux administration and Galaxy systems administration. The complexity dictates that these systems need to be set up and managed by in-house IT trained staff that will be able to tend the system along the way. It is also very important to maintain interactions with the users of the system, to ensure that the setup produces results that are useful to the users. To accomplish this, bioinformaticians are needed to develop pipelines and visualizations that give results that will on their own be easy for users to interpret in a biologically correct manner. Last but not least - such systems require a significant investment from the institution, thus it is important to showcase the benefits that the system will provide.

Develop security scripts to create vulnerable virtual machines and learn penetration testing techniques

The article demonstrates the concept of building a laboratory for penetration testing using a special pro-gram. The program is a set of scripts that configure the system in accordance with a user-defined script. Thanks to the elements of script randomization, this solution allows you to deploy several educational tasks at once to a group of students using only one virtual machine image. The basic idea is that the set-up and creation of a vulnerable target occurs just before the execution of the learning task itself. Those, the virtual machine is initially a basic Ubuntu Linux image that does not have any set of vulnerabilities. The main feature of the proposed solution is that the content of the scripts describes not one variant of the system configuration, but several at once, forming scripts with elements of randomization. In other words, having a basic Ubuntu Linux image and a set of the scripts, you can create different tasks for a dozen students.

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user’s own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6h, 12h, 24h, and 40h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration

In cloud computing, load balancing, energy utilization are the critical problems solved by virtual machine (VM) migration. Live migration is the live movement of VMs from an overloaded/underloaded physical machine to a suitable one. During this process, transferring large disk image files take more time, hence more migration and down time. In the proposed adaptive deduplication, based on the image file size, the file undergoes both fixed, variable length deduplication processes. The significance of this paper is resumption of VMs with reunited deduplicated disk image files. The performance measured by calculating the percentage reduction of VM image size after deduplication, the time taken to migrate the deduplicated file and the time taken for each VM to resume after the migration. The results show that 83%, 89.76% reduction overall image size and migration time respectively. For a deduplication ratio of 92%, it takes an overall time of 3.52 minutes, 7% reduction in resumption time, compared with the time taken for the total QCOW2 files with original size. For VMDK files the resumption time reduced by a maximum 17% (7.63 mins) compared with that of for original files.

Integrity control of virtual machine images on the OpenStack platform

Integrity control of virtual machine images on the OpenStack platform

Speeding Up VM Startup by Cooperative VM Image Caching

Virtual machine (VM) management is at the core of virtualized cloud data centers. Among others, how to reduce the startup delay of VMs is a key issue for improving user experience and resource utility. In this paper, we study this issue by jointly considering VM placement and VM image caching. We formulate the joint placement problem and design several joint algorithms, including both online and offline algorithms, to speed up VM startup. In our design, we adopt the cooperative caching approach, where image cache copies are shared among physical machines (PMs) so as to reduce image retrieval time. The key point of our algorithms lies in how to appropriately place VM image cache among PMs so as to speed up VM startup as much as possible. The proposed algorithms are evaluated by extensive simulations via SimGrid. The results show that our algorithms can achieve shorter startup delay in most cases, compared with existing ones.

Virtual Monoenergetic CT Imaging via Deep Learning

SummaryConventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.