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Summary: In the ever‐evolving realm of cloud computing, the challenge of intrusion detection has grown increasingly intricate and vital. With the proliferation of cyber‐attacks and the widespread use of virtualized environments, there is a pressing need for network security solutions that are not only innovative and robust but also easily comprehensible. These solutions must possess the ability to effectively detect malicious activities, provide visibility into network operations, adapt to changing requirements, and promptly alert stakeholders to any suspicious behavior. In this study, we introduce a groundbreaking approach known as fuzzy logic‐driven optimized long short‐term memory (LSTM)—FLOLSTM, specifically designed for hypervisor‐based environments. By integrating fuzzy logic with an optimized LSTM neural network, FLOLSTM aims to significantly enhance the detection of attack traffic within hypervisor networks by combining the interpretability and uncertainty management capabilities of fuzzy logic with the temporal pattern recognition prowess of LSTM. The research methodology involves meticulous data collection from hypervisor monitors, followed by rigorous cleaning and preprocessing to enhance data reliability. Subsequently, the preprocessed data is input into the FLOLSTM classifier to identify malicious activities. Furthermore, the performance of the LSTM is fine‐tuned using the waterwheel plant optimization (WPO) algorithm. Experimental evaluations compare the efficiency of the proposed FLOLSTM with existing techniques across various metrics including accuracy, recall, precision, F‐measure, specificity, false‐positive rate (FPR), and false‐negative rate (FNR). Overall, the FLOLSTM model represents a significant advancement in intrusion detection for cloud environments, offering a potent blend of interpretability, accuracy, and efficiency. Its superior performance underscores its capacity to enhance network security and effectively mitigate cyber threats in dynamic and virtualized settings, thereby making a substantial impact on the field of cyber security. [ABSTRACT FROM AUTHOR]

We present VirtSecIO, a hypervisor-based platform for executing secure modules. VirtSecIO provides the modules with secure paths to peripheral devices, which can be shared between the modules and the operating system. Moreover, VirtSecIO is a thin hypervisor with a negligible performance overhead and a minimal attack surface. We demonstrate VirtSecIO's abilities by developing HyperWallet, a secure module that acts as a hardware crypto-wallet, without requiring any dedicated hardware. [ABSTRACT FROM AUTHOR]

In recent years, there has been a trend towards integrating functions using a small number of microcontrollers instead of employing multiple microcontrollers across various environments. This shift underscores the need for a hypervisor capable of efficiently utilizing resources while imposing minimal overhead. Addressing this demand, this paper introduces a hypervisor employing dynamic round-robin scheduling, which flexibly adjusts time quantum allocation based on the urgency of each OS. Furthermore, a monitor mode is devised to oversee resource allocation among multiple OSs. To enhance responsiveness while managing these OSs, ultra-light context-switching is implemented within the monitor mode. The proposed system demonstrates a notable reduction in execution time, approximately 19% compared to traditional round-robin scheduling. Additionally, in terms of energy efficiency, the proposed system yields a 34% reduction in energy consumption compared to existing methods. Notably, the ultra-light context-switching mechanism consumes only about 5% of the processing cycle when compared to FreeRTOS. [ABSTRACT FROM AUTHOR]

This study propose the use of AI enabled machine learning algorithms with the Bag‐of‐Word (BoW) methods for the detection of intrusions by analysing the system call patterns. Host based Intrusion Detection System can make use of system call patterns to differentiate between normal and anomalous program behaviours. First, the system call patterns are pre‐processed with different approaches like BoW, BoW with Boolean value, BoW with Probability value and BoW with TF‐IDF. Next machine learning algorithms are used to evaluate the performance of classifier models. We used J48 (C4.5), Random Forrest, RIPPER, KNN, SVM, and NaiveBayes ML algorithms. This process was carried out on ADFA‐LD and on our proposed virtual machine monitor (VMM) malware attack data set for analysis. The proposed work is evaluated based on detection accuracy and false alarm rate metrics. Random Forrest algorithm performs better compared with other ML algorithms in terms of intrusion detection accuracy and false alarm rate on ADFA and VMM malware data set. The proposed data set provide better results compared with ADFA‐LD analysed using ML algorithms. The classifier model trained with ADFA and VMM malware system call data sets may do predictive analytics in detecting security issues for Industry 4.0 systems. [ABSTRACT FROM AUTHOR]

With an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single‐operating‐system (OS) non‐uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single‐node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale‐up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single‐OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74 × on average in terms of execution time compared with the existing state‐of‐the‐art open‐source technology. [ABSTRACT FROM AUTHOR]

Modern multiprocessing embedded applications require, in many cases, two different environments on the same platform: one that meets real-time requirements and another one with a general purpose operating system. Although several technologies can be used, two of the most popular are virtualization based on hypervisors and asymmetric multiprocessing software. However, using these tools introduces latency, which must be measured to verify compliance with real-time requirements. With the aim of facilitating these measurements, this work provides a hardware tool that is more precise and easier to use than other existing software solutions. The paper also studies the interrupt latency generated by different hypervisors and asymmetric multiprocessing frameworks in a Zynq UltraScale+ platform. This research work facilitates the accurate study of the temporal response of multiprocessor embedded systems, which allows for evaluating their suitability for applications with real-time requirements. [ABSTRACT FROM AUTHOR]

As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model's final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system's security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance. [ABSTRACT FROM AUTHOR]

The isolation technique of containers introduces uncertain security risks to malware detection in the current container environment. In this paper, we propose a framework called Malware Detection for Container Runtime based on Virtual Machine Introspection (MDCRV) to detect in-container malware. MDCRV can automatically export the memory snapshots by using virtual machine introspection in container-in-virtual-machine architecture and reconstruct container semantics from memory snapshots. Although in-container malware might escape from the isolating measures of the container, our detecting program which benefits from the isolation of the hypervisor still can work well. Additionally, we propose a container process visualization approach to improve the efficiency of analyzing the binary execution information of container runtime. We convert the live processes of in-container malware and benign application to grayscale images and employ the convolutional neural network to extract malware features from the self-constructed dataset. The experimental results show that MDCRV achieves high accuracy while improving security. [ABSTRACT FROM AUTHOR]

As the extensive use of cloud computing raises questions about the security of any personal data stored there, cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment. A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware. The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment. An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance; Each hypervisor should be examined to meet specific needs. The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine (KVM) while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs. This study evaluated the efficiency of two hypervisors, Hyper-V and KVM, in implementing six cryptographic algorithms: Rivest, Shamir, Adleman (RSA), Advanced Encryption Standard (AES), Triple Data Encryption Standard (TripleDES), Carlisle Adams and Stafford Tavares (CAST-128), BLOWFISH, and TwoFish. The study's findings show that KVM outperforms Hyper-V, with 12.2% less Central Processing Unit (CPU) use and 12.95% less time overall for encryption and decryption operations with various file sizes. The study's findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment, which could assist both cloud service providers and end users. Future research may focus more on how various hypervisors perform while handling cryptographic workloads. [ABSTRACT FROM AUTHOR]

This research analyzes and identifies the evolution and trends in the telecommunications sector to successfully implement a hyperconverged solution. The approach was qualitative through a document review, exploring the best practices of consulting firms, the technology community, and manufacturers in the sector. The Gartner Magic Quadrant for Hyperconverged Infrastructure in 2022 was used as a basis to achieve this objective. The leading vendors successfully implementing their solutions in the telecommunications sector were identified. Subsequently, the information gathered by consulting firms, the technology community, and experts in the field was contrasted to corroborate the evolution, advantages, and market trends. Finally, it was concluded that organizations' decision-making should consider economic and commercial criteria and evaluate aspects of greater depth and relevance, such as field evidence, technological innovation, and customer experience described by third parties. [ABSTRACT FROM AUTHOR]

Featured Application: We present a Firefox extension acting as a trust indicator and a clipboard encryptor. The framework can also be used for other means such as storing passwords, secure access to crypto wallet, etc. Malware often attempts to steal input and output through human interface devices to obtain confidential information. We propose to use a thin hypervisor, called "HyperIO", to realize a secure path between input and output devices using a partial implementation of device drivers. We apply our approach using two security systems built on HyperIO: FireSafe and ClipCrypt. FireSafe is a web browser extension which allows a remote web server to display and receive sensitive user information securely. ClipCrypt enables the user to securely enter and view their confidential information in commodity Windows applications. [ABSTRACT FROM AUTHOR]

Parallel hierarchical scheduling of multicore processors in avionics hypervisor is being studied. Parallel hierarchical scheduling utilizes modular reasoning about the temporal behavior of the upper Virtual Machine (VM) by partitioning CPU time. Directed Acyclic Graphs (DAGs) are used for modeling functional dependencies. However, the existing DAG scheduling algorithm wastes resources and is inaccurate. Decreasing the completion time (CT) of DAG and offering a tight and secure boundary makes use of joint-level parallelism and inter-joint dependency, which are two key factors of DAG topology. Firstly, Concurrent Parent and Child Model (CPCM) is researched, which accurately captures the above two factors and can be applied recursively when parsing DAG. Based on CPCM, the paper puts forward a hierarchical scheduling algorithm, which focuses on decreasing the maximum CT of joints. Secondly, the new Response Time Analysis (RTA) algorithm is proposed, which offers a general limit for other execution sequences of Noncritical joints (NC-joints) and a specific limit for a fixed execution sequence. Finally, research results show that the parallel hierarchical scheduling algorithm has higher performance than other algorithms. [ABSTRACT FROM AUTHOR]

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Data centers (DC) play a very important role in higher educational institutions (HEIs) all over the world, particularly in Pakistan for the provision of expedited and reliable services, especially during the Covid-19 pandemic. Due to that educational and financial businesses have been shifted online, which increasing the mandate of virtual data center framework with the support of Learning Management Systems (LMS), and Enterprise Resource Planning (ERP) for HEIs. The main reasons preventing this adoption are the cost, complexity, and security. This paper proposed a cost-effective approach for traditional DC. This method-imposed VMware ESXI Hypervisor, which utilizes a single server machine to host multiple servers, vSwitches for network isolation and two DMZs (De-Militarized Zones) are set up along with Mikrotik router firewall policies implemented for the security of DC. The outcome of this study indicates that all the servers worked in the production environment, and the firewall coupled with DMZ implemented in the network prevented DDOS attacks, resulting in the continuity of DC services and security. [ABSTRACT FROM AUTHOR]

Real-time virtualization is currently a hot topic, and there is much ongoing research on real-time Virtual Machines and hypervisors. However, most of the previous research focused either on reducing the latencies introduced by the virtualization stack (hypervisor, host Operating System, Virtual Machine scheduling, etc...) or analyzing the virtual CPU scheduling algorithms. Only a few works investigated the impact of the guest Operating System architecture on real-time performance or considered multiple performance metrics (latency, schedulability, startup times, resource consumption) at the same time. This paper compares various features of different virtualization technologies and guest Operating Systems, evaluating their suitability for serving real-time applications. The results indicate that solutions based on KVM (and an appropriate microvm) and the OSv unikernel can be considered viable alternatives to more traditional VMs or containers. [ABSTRACT FROM AUTHOR]

For cloud data centers, a performant yet energy-efficient operation is critical for service quality and experience. The growing demand for cloud-based services has led to the development of large-scale hierarchical data center structures, characterized by horizontal expansion and vertical hierarchy, leading to challenges in managing Virtual Machines (VM) at a granular level. The hierarchical arrangement can increase the risk of deployment failures, often stemming from inadequate computational resources on physical hosts, even when the cluster-level resources seem sufficient. While substantial work has gone into managing VMs at the physical host level, there remains a dearth of research under hierarchical data center configurations. To fill the research gap, we address the hierarchy in cloud data centers with a novel two-stage approach named VMM-HHGT, aiming at suppressing VM deployment failures, while balancing the energy consumption and computation resource contention. VMM-HHGT comprises a Hyper-Heuristic-assisted broker (VMM-HH), which can learn the workload patterns and hardware configurations to generate cluster-selection heuristics. An offline training process is incorporated for continuous heuristic evolution with zero overhead on decision-making. Besides, a Game-Theory-assisted hypervisor (GT) is designed for inter-host live VM migration for fine-grained balancing of energy consumption and resource contention. Extensive experiments with traces from real-world VMware data centers show that VMM-HHGT achieves a higher deployment success rate compared to the state-of-the-art approaches, with a well-situated performance in energy consumption and resource contention. • Handling hierarchy in cloud data centers to suppress VM deployment failures. • A two-stage solution for initial VM placement and live migration in the long term. • A Hyper-Heuristic-assisted broker for initial cluster-level VM placement. • A Game-Theory-assisted hypervisor for host-level VM placement and long-term migration. • Effectively suppressed deployment failures with satisfying performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Whilst virtualization was once restricted to large-scale computing platforms, it is now widely deployed on modern embedded computing systems. This has been driven by the availability of hardware support which alleviates the performance penalties incurred by traditional software virtualization technologies. In the domain of hard real-time systems, specialist virtualization technology which respects restricted timing requirements and constraints can be deployed to allow sharing of processors. However, other aspects of the embedded system (I/O, memory, and communication) are harder to analyze. In this paper, we argue that in order to support real-time virtualization on modern embedded systems, additional system-wide hardware support is required. We propose BlueVisor, an analyzable and scalable hardware hypervisor for many-core embedded systems, which enables time-predictable CPU, memory, and I/O virtualization, as well as supporting a fast interrupt handler, and inter-VM communication. We describe the design and implementation of the real-time hypervisor, and demonstrate how a BlueVisor-based virtualization system can be leveraged to meet real-time requirements with significant improvement in system performance, and with a low-performance cost when executing different types of software. [ABSTRACT FROM AUTHOR]

The Virtualization topic has been researched for years, but it gained popularity in the last century. Virtualization is technology, which unites and devices calculating resources to provide one or more work environments. It is achieved with inserting abstract software between default hardware and OS. In the scientific research their essence, advantages and ways of application are considered. The main types of virtualization are classified and it is emphasized to virtualization, which is based on hypervisor. An analysis is made of different types of hypervisors. Based on the authors' research, a platform for virtualization is suggested - Proxmox VE, which is easy to implement and to use. The characteristics of the suggested platform are examined in detail and it's practical usage in University of economics - Varna is analyzed. The result of the analysis shows a criteria for choosing a virtualization platform. [ABSTRACT FROM AUTHOR]

We present H-KPP, hypervisor-based protection for kernel code and data structures. H-KPP prevents the execution of unauthorized code in kernel mode. In addition, H-KPP protects certain object fields from malicious modifications. H-KPP can protect modern kernels equipped with BPF facilities and loadable kernel modules. H-KPP does not require modifying or recompiling the kernel. Unlike many other systems, H-KPP is based on a thin hypervisor and includes a novel SLAT switching mechanism, which allows H-KPP to achieve very low ( ≈ 6 % ) performance overhead compared to baseline Linux. [ABSTRACT FROM AUTHOR]

Summary: Hardware‐accelerated cloud computing systems based on FPGA or ASIC chips have proved useful in providing power‐efficient acceleration for a variety of software applications. However, these computing systems rely on operating systems and hypervisors, which not only are implemented with inefficient software, but which also are incapable of handling massively parallel systems, due to the lack of parallelism and scalability in their algorithmic designs. As a result, power, performance, and scalability problems will emerge in an exascale cloud computing environment. As a solution to these problems, the present study proposes a parallel hardware hypervisor system implemented entirely in special‐purpose hardware without resorting to energy‐inefficient general‐purpose processors. Furthermore, the proposed hypervisor system virtualizes application‐specific multi‐chip supercomputers, in order to enable the virtual supercomputers to share available FPGA and semi‐configurable ASIC resources in a cloud system. Single‐chip verification studies based on Verilog simulation have been done to verify the functional correctness of the proposed hardware hypervisor system, which consumes only a fraction of hardware resources. This article will in particular focus on the virtualization of multi‐chip message‐passing based supercomputers using limited reconfigurable hardware resources. [ABSTRACT FROM AUTHOR]

Virtual Machines are the key technology in cloud computing. In order to upgrade, repair or service the physical machine where a Virtual Machine is hosted, a common practice is to live-migrate the Virtual Machine to a different server. This involves copying all the guest memory over the network, which may take a non-negligible amount of time. In this work, we propose a technique to speed up the migration time by reducing the amount of guest memory to be transferred with the help of the guest OS. In particular, during live-migration, an eBPF program is injected in the guest kernel to obtain, and send to the Virtual Machine Monitor, the list of guest page frames that are currently unused. The VMM can then safely skip these pages during the copy. We have integrated this technique in the live-migration implementation of QEMU (Bellard, 2005), and we show the effects of our work in some experiments comparing the results against the QEMU default implementation. [ABSTRACT FROM AUTHOR]

Active and passive virtual machine introspection mechanisms are pivotal for monitoring virtual machines on top of a hypervisor. They enable external tools to monitor and inspect the state from the outside. Active virtual machine introspection mechanisms intercept the execution at predetermined locations of interest synchronous to the execution of the system. Such mechanisms, in particular, require support from the processor vendor by facilitating interpositioning. This support is missing on AMD x86 processors, leading to inferior introspection solutions. We outline implicit assumptions about active introspection mechanisms in previous work, offer constructions for solution strategies on AMD systems, and discuss stealthiness and correctness. We show empirically that such retrofitted software solutions exhibit performance metrics in the same order of magnitude as native hardware solutions. Moreover, we highlight that the open problems for virtual machine introspection on ARM systems and those encountered on AMD x86 are related. Hence, we present an introspection architecture based on KVMi that addresses these open problems. Finally, we demonstrate comparable and, in many cases, superior performance to state-of-the-art solutions on Intel x86. [ABSTRACT FROM AUTHOR]

In this article, the fundamental suitability of the open-source hypervisor Xen for safety-critical real-time application is analyzed. In the field of avionics, satisfying criteria such as safety criticality and real-time capability are mandatory and pose one of the greatest challenges when developing systems and functions that are to be qualified for the installation in passenger aircraft. System architectures that additionally aspire adaptive behavior in terms of self-configuration and reconfiguration complicate these challenges even more. Spatial and temporal partitioning is a fundamental requirement for the implementation of safety-critical systems but is of static behavior in current avionic standards such as A653. Implementing a fully functional partitioning that is able to reconfigure during runtime would be a great leap forward in the field of adaptive avionics. The open-source hypervisor Xen offers features to dynamically create and manage virtual machines that natively come with architecture-related segregation. In order to determine the feasibility of Xen in the field of safety-critical real-time application, its spatial and temporal partitioning capabilities and occurring latencies are analyzed in this article. The analysis is based on specifically designed benchmarks that measure the accuracy of periodic task execution with different system load levels featuring various Xen schedulers and guest operating system kernels. The overall results for the spatial partitioning and real-time capabilities turn out to be promising but remain with minor interferences, varying based on the setup benchmarking configuration. The temporal partitioning enforced by Xen remains with clearly identified and fixable issues before a deterministic function execution in hard real time is reached. [ABSTRACT FROM AUTHOR]

The detection part is called the Hypervisor Scanner, which is programmed to detect malicious insiders. Using the feed forward neural network, the hypervisor scanner is generated and trained with a supervised learning algorithm referred to as the Levenberg-Marquardt algorithm. The three criteria are considered for service level agreement, such as bandwidth requirement, memory consumption and storage space. The Hypervisor Scanner can detect malicious insiders in cloud systems that breach SLA and suffer from an insecure cloud administrative domain that lacks control over the cloud service provider (CSP). Here, the Hypervisor Scanner is constructed using the biologically inspired classification approach referred to as artificial neural network modelling. ANN teaching uses the Levenberg-Marquardt learning algorithm. The LM algorithm works by minimising the average square error to boost the detection system. For detecting malicious insiders, the Hypervisor Scanner uses threshold values for SLA parameters. It is known from performance review and comparison that the Hypervisor Scanner is the appropriate one with high detection accuracy and low false alarm rate for the detection of malicious insiders. This can therefore be effective, robust and realistic in the detection of malicious insiders in and around the cloud world. [ABSTRACT FROM AUTHOR]

In today's modern times, hypervisor and virtualization in cloud computing is the most demanding researched topic. In IT industry cloud computing is an emerging field because all are embracing virtualization as a way to cut the expenses and to enhance security. Using hypervisor in virtualization is very significant to achieve system security, flexibility and reliability. In this paper, we discussed and reviewed ten different research paper to get the deeper insights on hypervisor, virtualization and cloud computing. [ABSTRACT FROM AUTHOR]

Containers emerged as a lightweight alternative to virtual machines that offer better microservice architecture support. They are widely used by organizations to deploy their increasingly diverse workloads derived from modern applications such as big data, IoT, and edge/fog computing in either proprietary clusters or private, public cloud data centers. With the growing interest in container-based virtualization technologies, the requirement to explore the deployment and orchestration of clusters of containers has become a central research problem. Although progress has been made to study containerization, systematic consolidation of the existing literature with a summative evaluation is still missing. To fill this gap, in this paper, we first taxonomically classify the existing research studies on the performance comparison between hypervisor and container technology and then analyze state-of-the-art for container cluster management orchestration systems, its performance monitoring tools, and finally future research trends. This results in a better understanding of container technology with attention to provide summative analysis in terms of (i) how much performance overhead is generated by a hypervisor compared to container-based virtualization, (ii) which container technology is suited for a cloud application deployment based on the type of benchmark executing, (iii) how to provide management of containers deployed in a cluster environment, (iv) container performance monitoring tools, and (v) finally emerging concerns for future research directions. [ABSTRACT FROM AUTHOR]

As more and more applications migrate to clouds, the type and amount of malware attack against virtualized environments are increasing, which is a key factor that restricts the widespread deployment and application of cloud platforms. Traditional in-VM-based security software is not effective against malware attacks, as the security software itself becomes the target of malware attacks and can easily be tampered with or even subverted. In this paper, we propose a new malware detection method to improve virtual machine security performance and ensure the security of the entire cloud platform. This paper uses the virtual machine introspection(VMI) combined with the memory forensics analysis(MFA) technology to extract multiple types of dynamic features from the virtual machine memory, the hypervisor layer and the hardware layer. Furthermore, this paper proposes an adaptive feature selection method. By combining three different search strategies, three types of features are compared and analyzed from three aspects: effectiveness, system load and security. By adjusting the weight of each feature, it meets the detection requirements of different malware in the cloud environment as expected. Finally, the detection method improves the detection accuracy and generalization ability of the overall classifier using the AdaBoost ensemble learning method with Voting's combination strategy. The experiment used a large number of real malicious samples, and achieved an accuracy of 0.999 (AUC), with a maximum performance overhead of 5.6%. [ABSTRACT FROM AUTHOR]

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

In this paper using VMware vSphere hypervisor ESXi 6.5.0 for virtualization of the operating system Kali Linux (64-bit) for educational purposes in the Faculty of Technical Sciences is made. [ABSTRACT FROM AUTHOR]

In this paper virtualization of the operating system Microsoft Windows 10 (64-bit) via VMware vSphere Hypervisor ESXi 6.5.0 for educational purposes in wellequipped laboratories at the Faculty of Technical Sciences at the Konstantin Preslavsky University of Shumen is made. [ABSTRACT FROM AUTHOR]

Summary: Distributed Denial of Service (DDoS) attack is one of the major attacks that incur large financial loss in the cloud system. This motivated the research community to develop various detection techniques for controlling the effects of the DDoS attack. However, the existing techniques are not mature to satisfy the requirements of a cloud‐based attack detection system, as they manage the devious strategies that exploit the elastic and multi‐tenant properties of the cloud and ignore the resource constraints of the cloud system. This paper proposes a new solution that allows the hypervisor to establish trust‐based relationships towards the guest Virtual Machines (VMs). The Bayesian inference is applied to aggregate the objective and subjective trust sources. A trust‐based maximin game between DDoS attackers is designed. A hypervisor tries to maximize the attack minimization under a limited amount of resources. The game solution guides the hypervisor to determine the distribution of optimal detection load among VMs to improve the real‐time detection rate of DDoS attack. The Least Squares Support Vector Machine (LS‐SVM) classification is applied to classify the normal VMs and malicious VMs. The file is allocated to the VM based on the storage capacity of the VM. The experimental result shows that the proposed approach achieves high DDoS attack detection rate with minimum false positive and negative rate, when compared to the existing attack detection models. [ABSTRACT FROM AUTHOR]

Cloud computing is a facility that provides access to services. Management of massive amount of information in cloud computing is achieved by using VM consolidation. Nowadays, power or energy saving has emerge as a key factor in designing VM consolidation algorithms. Algorithms for deciding on next VM have never given consideration to both strength and data at the identical time in past. Cloud provides virtual access to resources. The users have a policy model to pay based on usage. The facility provides restrictions on access with increased time delays. A hypervisor with optimized algorithm approach will allow for allocation to resources in a optimal manner. This paper proposes a novel hypervisor named Cloud Resource Scheduling Optimal Hypervisor (CRSOH) for Dynamic Cloud Computing. Surroundings for deciding on VM with energy and facts constraints. Proposed algorithm within the direction of most resource utilization with the aid of VMs. Proper utilization of sources ends in conservation of energy. As a result, energy intake using CRSOH algorithm is much less and more efficient than other present algorithms. [ABSTRACT FROM AUTHOR]

System-level Dynamic Binary Translation (DBT) provides the capability to boot an Operating System (OS) and execute programs compiled for an Instruction Set Architecture (ISA) different from that of the host machine. Due to their performance-critical nature, system-level DBT frameworks are typically hand-coded and heavily optimized, both for their guest and host architectures. While this results in good performance of the DBT system, engineering costs for supporting a new architecture or extending an existing architecture are high. In this article, we develop a novel, retargetable DBT hypervisor, which includes guest-specific modules generated from high-level guest machine specifications. Our system simplifies retargeting of the DBT, but it also delivers performance levels in excess of existing manually created DBT solutions. We achieve this by combining offline and online optimizations and exploiting the freedom of a Just-in-time (JIT) compiler operating in a bare-metal environment provided by a Virtual Machine (VM) hypervisor. We evaluate our DBT using both targeted micro-benchmarks as well as standard application benchmarks, and we demonstrate its ability to outperform the de facto standard Qemu DBT system. Our system delivers an average speedup of 2.21× over Qemu across SPEC CPU2006 integer benchmarks running in a full-system Linux OS environment, compiled for the 64-bit ARMv8-A ISA and hosted on an x86-64 platform. For floating-point applications the speedup is even higher, reaching 6.49× on average. We demonstrate that our system-level DBT system significantly reduces the effort required to support a new ISA while delivering outstanding performance. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing and rapid integration of different architectures to form hybrid systems, the need for creation of virtual machines with the existing system is essential, to process voluminous data for a wide variety of application sets, and provide optimal solutions to their clients. This paper presents a framework for providing optimal utilization of resources through a hypervisor technique. This framework helps to reduce downtime of resources and improve machine utilization. The framework creates more than one virtual machine on a single physical machine. The performance of the partitioned machine is being examined using various signature cryptographic hash algorithms. Results show that the proposed framework is efficient and secure. The technique brings in reduced computation and supports better scalability of the virtual machines in bare metal. [ABSTRACT FROM AUTHOR]

In the last decade, research on CPU-FPGA hybrid architectures has become a hot topic. One of the main challenges in this domain is to efficiently and safely manage Dynamic Partial Reconfiguration (DPR) resources. This paper focuses on the management of reconfiguration by a custom hypervisor named Ker-ONE, on an ARM-FPGA platform. Using a virtualization approach, virtual machines (VM) may access resources independently, being unaware of the existence of other VMs. Our custom hypervisor guarantees the independence and isolation of VM domains. The purpose of our work is to provide an abstract and transparent interface for virtual machines to access reconfigurable resources, while meeting real-time constraints. This means that software engineers do not need to focus on implementation details. In this paper, we present a complete architecture in which hardware accelerators are seen as virtual devices which are universally mapped in each VM space as ordinary peripherals. The hypervisor automatically detects VMs' requests for DPR resources and handles them dynamically according to a preemptive allocation mechanism. We also evaluate the efficiency of our framework by measuring the critical overhead during DPR management and allocations. The results demonstrate that our mechanisms are implemented with low overhead compared to other approaches and that they are compatible with real-time scheduling. [ABSTRACT FROM AUTHOR]

Network Function Virtualization, NFV, aims to accelerate the implementation of new network services to support business strategies and revenue growth for the telecommunications sector in particular. In general, NFV aims to replace the function of the existing physical router device into a virtual router device and can be run on any virtualization server. In this study, a combination of Cisco virtual routers are tested on a virtualization platform for: throughput, packet loss, jitter and scalability as additional network hops. The virtualization platform used is a type 2 hypervisor, QEMU (Quick EMUlator), because it is open source and widely used in both educational and enterprise environments. Our results show: i) throughput increases with traffic volume, ii) link speed between host and router has a maximum rate of 1000 Mbps and a maximum recorded throughput of 778.6 Mbps, iii) the jitter parameter test ranged between 0.026 ms to 0.098 ms and met Cisco standards, iv) packet loss parameter was < 1%, v) the packets lost ranged from 0% to 2,471% caused by virtual links created by qemu and the virtual network interface cards, vi) for scalability, the more network hops the lower the performance. [ABSTRACT FROM AUTHOR]

A Deadlock is a state in which a process waits for the resource that is being held by another process. The processes will be blocked completely and it doesn't lead to any progress. Many of the present algorithms take polynomial time only to detect the deadlock. The proposed algorithm provides an approach to detect and recover from the deadlock in linear time complexity. The algorithm shows a one-to-one correspondence to the figures formed using the Penrose Geometry. The algorithm can detect the deadlock without ambiguity even if the resources with multiple instances are involved. [ABSTRACT FROM AUTHOR]