Hypervisor Research Articles

Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer

Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.

Advanced Study: Improving the Quality of Cooling Water Towers’ Conductivity Using a Fuzzy PID Control Model

Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In such environments, the quality of electrical conductivity in the cooling water towers can be significantly negatively affected. Once minerals (e.g., calcium and magnesium) form in the water, conductivity becomes too high, and cooling water towers can become easily clogged in a short time; this leads to a situation in which the cooling water host cannot be cooled, causing it to crash. This is a serious situation because manufacturing processes are then completely shut down, and production yield is thus severely reduced. To solve these problems, in this study, we develop a practical designation for a photovoltaic industry company called Company-L. Three control methods are proposed: the motor control method, the PID control method, and the fuzzy PID control method. These approaches are proposed as solutions for successfully controlling the forced replenishment and drainage of cooling water towers and controlling the opening of proportional control valves for water release; this will further dilute the electrical conductivity and control it, bringing it to 300 µS/cm. In the experimental processes, we first used practical data from Company-L for our case study. Second, from the experimental results of the proposed model for the motor control method, we can see that if electrical conductivity is out of control and the conductivity value exceeds 1000 µS/cm, the communication software LINE v8.5.0 (accessible via smartphone) displays a notification that the water quality of the cooling water towers requires attention. Third, although the PID control method is shown to have errors within an acceptable range, the proportional (P) controller must be precisely controlled; this control method has not yet reached this precise control in the present study. Finally, the fuzzy PID control method was found to have the greatest effect, with the lowest level of errors and the most accurate control. In conclusion, the present study proposes solutions to reduce the risk of ice-water host machines crashing; the solutions use fuzzy logic and can be used to ensure the smooth operation of manufacturing processes in industries. Practically, this study contributes an applicable technical innovation: the use of the fuzzy PID control model to control cooling water towers in industrial applications. Concurrently, we present a three-tier monitoring checkpoint that contributes to the PID control method.

Mach: Firefighting Time-Critical Issues in Complex Systems Using High-Frequency Telemetry

To understand the complex interactions in modern software, engineers often rely on high-frequency telemetry (HFT) data generated via tools like eBPF. However, today's database systems are too slow for HFT's rate and volume and cannot process HFT within the limited resources available on individual host machines. Mach is a new storage engine for collecting and querying HFT. Key to Mach is the Temporal Skip Log (TSL)---a lightweight, write-optimized, log-based data structure specialized for HFT. Mach supports high ingest rates and makes data immediately queryable while operating within a limited on-host resource envelope. Our demo shows how Mach helps engineers collect and query HFT in near real-time when diagnosing performance problems. In contrast, current systems and data reduction techniques fail to keep up. While a widely used time series database (InfluxDB) drops much of the HFT, the audience will see how Mach loses no data and allows them to interactively explore HFT from application and kernel events as they arrive.

Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines

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.

Improving live migration efficiency in QEMU: An eBPF-based paravirtualized approach

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.

Parallel differential evolution paradigm for multilayer electromechanical device optimization

Design optimization of multilayer piezoelectric transducers is intended for efficient and practical usage of wideband transducers for fault diagnosis, biomedical, and underwater applications through adjusting layer thicknesses and volume fraction of piezoelectric material in each layer. In this context, we propose a parallel differential evolution (PDE) algorithm to mitigate the complexities of multivariate optimization as well as the computation time to achieve an optimized wideband transducer for the particular application. For lead magnesium niobate-lead titanate (PMN PT)- and PZT5h-based piezoelectric materials, the fitness function is formulated based on uniformity of mechanical pressure at the first three harmonics to achieve wide bandwidth in the required functional frequency range. It is carried out using a one-dimensional model (ODM), while input layer thicknesses and volume fractions of active material are evaluated using PDE. The simulation is performed on a parallel computing platform utilizing three different host machines to reduce computational time. Results of the proposed methodology for PDE are statistically represented in the form of minimum, maximum, mean, and standard deviation of fitness value, while graphically represented in terms of speedup and time. It can be observed that the execution time for parallel DE decreases with the increasing number of cores.

Online Network Protection Firmware for Malware Identification Utilizing Transformer Learning

Abstract: Malware ID expects a critical part in network security with the expansion in malware improvement. What more, kinds of progress in cutting edge assaults. Noxious programming applications, or malware, are the principal wellspring of different security issues. For different reasons, including the taking of state of the art developments and insightful properties, regulative exhibitions of retaliation, and the modification of sensitive information, to give some examples, these pernicious applications plan to perform unapproved exercises on the host machines to assist their makers. More valuable assistance systems are required because of the quick expansion of noxious programming on the web and their self changing skills, as in polymorphic and remarkable malware. This task proposes to support the MalFree Sandbox with stacked bidirectional long transient memory (Stacked BiLSTM) and generative prepared transformer based (GPT2) critical learning language models for recognizing pernicious code isolated. The proposed computations, specifically the bidirectional long transient memory (BiLSTM) model and the generative prepared transformer 2 (GPT-2) method, employ gathering rules derived from Minimal Executable (PE) Records static examination results to identify harmful code pieces. To comprehend malwares through MalFree Sandbox, care should be taken to sandbox the malwares in a climate that considers an encapsulation and exhaustive evaluation while in addition keeping on propelling spread from being gifted.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This studyaimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Challenges of the FCC-ee civil engineering studies

The European Organisation for Nuclear Research (CERN) is planning a Future Circular Collider (FCC), to be the successor of the current Large Hadron Collider (LHC). Significant civil engineering is required to accommodate the physics experiments and associated infrastructure. The 90.6 km, 5.5 m diameter tunnel will be situated in the Geneva region, straddling the Swiss-French border. Civil engineering studies are to incorporate the needs of both the FCC lepton collider (FCC-ee) and the FCC hadron collider (FCC-hh), as the tunnel will host both machines successively.

Discussion on the Current Situation and Future Development of Unmanned Smart Farmland

At present, unmanned smart farmland in China is in the early stage of demonstration and promotion, and technological research mainly focuses on single machine intelligent control technologies such as autonomous driving and precision operation. However, related technologies such as multi machine collaboration, host machine and machine operation collaboration, as well as collaboration between agricultural production scenarios and autonomous agricultural machine operation, are insufficient. Building unmanned smart farmland is an important way to alleviate the shortage of rural labor and promote the modernization of agriculture and rural areas. It provides precise planting, visual management, and intelligent decision-making for agricultural production, and also allows agriculture and rural areas to share the achievements of technological development in the era.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\\%), decreased node shutdown time (45 %), and less infrastructure cost (48\\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

Valve Setting Using Fuzzy Logic Method To Control Main Engine Coolant Flow

The main objective of this research is to improve the operating efficiency and performance of ship main engines by optimizing coolant flow. Improper coolant flow can cause excessive temperature rise in the engine, which in turn can reduce engine life and efficiency. In this research, the coolant flow control system is implemented using the fuzzy logic method. Fuzzy logic allows modeling that is more adaptive to parameter variations and uncertainties in the operational environment. Fuzzy rules are developed based on practical knowledge of experts and operational data related to the host machine. The test was carried out through a simulation using a water flow control circuit that supplies the coolant to the ship's main engine with the main components being an ESP32 microcontroller chip and three DS18B20 temperature sensors. The results of this research show that the use of fuzzy logic in regulating coolant flow is able to provide a faster and more accurate response to changes in engine operational conditions. This has the potential to increase cooling efficiency and prevent engine over-temperatures, which can ultimately increase the overall service life and performance of the host engine. The practical implications of the results of this research can be applied in the development of more intelligent and adaptive control systems for various types of host machines, with the potential to increase operating efficiency and reduce the risk of damage due to excessive temperatures

Perancangan Sistem Informasi Presensi Online Karyawan Berbasis Website dengan Face Record dan Geo Location

This research discusses the development of an online employee attendance system in the context of the company. Attendance data includes employee attendance, arrival and return information, as well as absence status such as sickness or clearance. Traditionally, some organizations still use physical methods to collect attendance data, which is considered less efficient and effective. The research uses classical waterfall development method to facilitate the process of system development up to release. With face recognition and geo location methods, an online attendance system is proposed with IT infrastructure using private hosting and virtual machines with Red Hat Enterprise Linux 8.7 Operating System as a web server. With the implementation of this system, it is expected that benefits will be realized in the ease for employees to conduct attendance and management capabilities in managing employee attendance data in a structured manner.

Performance Analysis of Container Effect in Deep Learning Workloads and Implications

Container-based deep learning has emerged as a cutting-edge trend in modern AI applications. Containers have several merits compared to traditional virtual machine platforms in terms of resource utilization and mobility. Nevertheless, containers still pose challenges in executing deep learning workloads efficiently with respect to resource usage and performance. In particular, multi-tenant environments are vulnerable to the performance of container-based deep learning due to conflicts of resource usage. To quantify the container effect in deep learning, this article captures various event traces related to deep learning performance using containers and compares them with those captured on a host machine without containers. By analyzing the system calls invoked and various performance metrics, we quantify the effect of containers in terms of resource consumption and interference. We also explore the effects of executing multiple containers to highlight the issues that arise in multi-tenant environments. Our observations show that containerization can be a viable solution for deep learning workloads, but it is important to manage resources carefully to avoid excessive contention and interference, especially for storage write-back operations. We also suggest a preliminary solution to avoid the performance bottlenecks of page-faults and storage write-backs by introducing an intermediate non-volatile flushing layer, which improves I/O latency by 82% on average.

PyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are ‘RLmulti’ for processing multi-ligand protein complex, ‘RLmany’ for processing multiple ligands against a single protein target, ‘RLsingle’ for processing multiple pairs of proteins and ligands, and ‘PPmore’ for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M. Communicated by Ramaswamy H. Sarma

A machine learning-based optimization approach for pre-copy live virtual machine migration

Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.

Task Load and Work Property-Based Virtual Machine Dispatching Algorithm

This research proposes a virtual machine dispatching algorithm that takes into account the task load and work property of a virtual machine. The algorithm utilizes an interruption monitoring module to assess the interruption frequency of the virtual machine and determine whether it belongs to a CPU-dense or I/O-dense type. Additionally, a CPU monitoring module tracks the current CPU utilization rate to monitor the task load. By considering the historical information within a specified time range, the algorithm calculates a dispatching time segment for the virtual machine and notifies the dispatcher to update the time segment. The dispatcher module adjusts the credit value dispatcher based on inputs from the monitoring and CPU modules. It applies different dispatching time segments to virtual machines with varying work properties, aiming to reduce I/O request delays, provide sufficient time for I/O request handling, and minimize additional overhead. Notably, the modifications occur within the virtual machine monitor, ensuring practicality and adaptability.

Bad USB as HID and it’s Mitigations

Abstract: A bug known as "Bad USB" allows a hacker to manipulate a USB flash drive's firmware. Malicious code is automatically activated when the Bad USB device is connected into the USB port of the host system. It is challenging to identify the malicious code since the host system interprets the harmful activities as legitimate ones required to load the USB device. Additionally, antivirus software is unable to detect firmware that has been modified because it examines the storage region rather than the firmware area. The vulnerability has a detrimental knock-on impact since a large number of computer peripherals (similar to USB flash drives, keyboards) are connected to the host machine using the USB protocols. Universal Serial Bus (USB) has become the primary connecting port for cutting-edge computers as a result of its universality. Programmability makes it easier for operating system and tackle manufacturers to develop their goods and associated firmware, but as of right now, a fix for the issue is unknown. In this article, we analyze the tampered area of the firmware that occurs when a good USB device is switched out with a bad one and provide a fix to ensure the integrity of the area when the USB thrills

Detecting Intruders in Network using Machine Learning

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Malicious software applications, or malware, are the primary source of many security problems. These intentionally manipulative malicious applications intend to perform unauthorized activities on behalf of their originators on the host machines for various reasons such as stealing advanced technologies and intellectual properties, governmental acts of revenge, and tampering sensitive information, to name a few. Malware detection methods rely on signature databases, including malicious instruction patterns in today's practice. The signature databases are used for matching against a signature generated from a newly encountered executable. Nevertheless, more efficient mitigation methods are needed due to the fast expansion of malicious software on the Internet and their self- modifying abilities, as in polymorphic and metamorphic malware. In this work, it detects Network Intrusion anomalies by using NSL-KDD dataset. The user enters the hacking parameters in the front end. The model predicts the type of attack and gives information about the type of attack to the user. The project is fully responsive and completely based on session and cookies (Client-server protocol). Then we activated our malware security device which forms production for the set of attack. This will help many cyber threads

VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively.