Virtual Cloud Computing Research Articles

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network

ABSTRACT The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

Optimal component activation in multi-attempt missions with common shock process

Abundant research efforts were devoted to modeling and optimizing mission systems operating in random shock environments. The existing models assumed that different components are exposed to independent shock processes. However, in many real-world applications (e.g., virtual machines in cloud computing, drones deployed in the same mission), multiple operating components may be simultaneously impacted by common shock processes during the mission, causing them to deteriorate and even crash. This paper contributes by modeling a multi-attempt mission system with heterogeneous components characterized by different performance, shock resistance and cost. Each component may start performing the mission at different times and a common shock process can negatively affect all operating components. Different activation schedules of system components may lead to dramatically different mission success probabilities and expected component losses, contributing to the expected mission losses (EML). We formulate and solve a new optimal component activation schedule (CAS) problem to minimize the EML. The proposed model is demonstrated through a case study of an aerial vehicles delivery mission system. Influences of several key model parameters (shock rate, mission failure penalty, allowed mission time, and component performance) on the mission performance metrics and the optimal CAS solutions are also investigated using the case study.

ТЕНДЕНЦИИ ЦИФРОВИЗАЦИИ ХИМИЧЕСКОЙ ОТРАСЛИ В РОССИИ И ЗА РУБЕЖОМ

Modern challenges of economic development have led to the digitalization of the Russian chemical industry within the framework of the international concept "Industry 4.0". Among the key emerging trends in the digitalization of the chemical industry, the following are highlighted and discussed: big data and advanced analytics, the Internet of Things, simulation and digital twins, blockchain technology, specialized software, augmented and virtual reality, cloud computing, artificial intelligence and machine learning, cybersecurity and data privacy, mobile applications. Despite the fact that the problem of assessing and measuring the level of digitalization still exists, especially in the chemical industry, a number of indices have been named to assess digitalization. Among them is the I-DESI index, which is proposed to be used internationally in the future. Modern chemical enterprises both in Russia and in other countries use digital technologies because they increase efficiency, accelerate innovation, increase safety and create new business models in an increasingly competitive environment. Various leading chemical companies pay special attention to investments in the digitalization of their production, as this contributes to the sustainable development of their business.

Exploration of Construction Paths for Virtual Simulation Training Bases under the Background of Digital Intelligence Economy

With the application of new-generation information technologies such as virtual reality, artificial intelligence, big data, cloud computing, and 5G, the construction of business training bases has transformed from physical to virtual. This paper analyzes the current construction status of business training bases in higher vocational colleges, identifies major issues such as lack of top-level design, unclear positioning, incomplete operational mechanisms, insufficient teaching content, and weak teaching staff. It proposes construction paths including scientific and reasonable planning, overcoming teaching difficulties with hybrid training, combining education and training, and strengthening the teaching staff, providing valuable references for the construction of new business virtual simulation training bases in higher vocational colleges.

Deterministic Lightweight VM placement for HANDLING resource constraint issues in the cloud

The virtual machines (VMs) placement is the subject of current cloud computing research. This study proposes the energy-constrained VM placement technique to address the issue of placing location limits on VMs to satisfy their requirements throughout the VM placement process. Each virtual machine (VM) can only be installed on one of the designated candidate physical machines (PMs) that have sufficient processing power, additionally, in order to satisfy the communication requirements of the associated VMs, there must be sufficient bandwidth between the selected PMs. Choosing where to put a virtual machine (VM) is a crucial task. It involves finding the best physical server or computer to host the VM. Picking the right host is essential for making sure the cloud system uses power wisely, uses resources effectively, and supports good quality of service. This work explore the problem of imposing constraints on the placements of VMs in cloud computing (CC) and offer an alternative perspective on VM placement. This work provides a novel algorithm based on this unique viewpoint to produce the required outcome. To show how successful the proposed Deterministic Lightweight VM placement (DLVMP) is, we run and examine several simulations. The outcomes demonstrate that our technique achieves reduced computing time and improved performance. Simulation results show that the proposed model functions and performs better in terms of blocking probability and computing time than other benchmark algorithms.

Application of Virtual Reality-based Cloud Computing in Digital Creation of Dabie Mountain Culture

Abstract This paper looks at 3D model retrieval techniques for digital virtual workshops in cloud computing. The global D2 shape distribution features are first used to sort the models for initial retrieval. Then the local curvature features are used to further optimize the sorted results to obtain the final retrieval results. Through the historical and cultural theme scene design, stage performance scene design, and interactive performance design to enhance the immersive experience of the corresponding cultural creations, the creative inheritance and dissemination mode of Dabie Mountain culture in the new period is created. The results show that the difference in the percentage of attention points in the landscape node area is significant, P=0.032, indicating that the virtual reality experience group is better able to meet the needs of the experience. The weights of the three evaluation dimensions of the virtual experience of Dabie Mountain folk song culture are, in order, mood design (0.4073) > scene design (0.3814) > context design (0.2113). Designing multi-sensory (visual, auditory, olfactory, tactile) is of the highest importance. In terms of interactive experience, more attention is paid to the timeliness and accuracy of feedback, as well as the guidance of operations. In terms of emotional experience, more attention is paid to the sense of pleasure, surprise, and satisfaction. In the cultural experience, more attention is paid to traditional folk songs and historical architectural displays.

Trends of digitalization in the Chemical industry in Russia and abroad

The modern challenges of the economic development resulted in digitalization for the Chemical industry of Russia within the Industry 4.0 international concept. Among the key developing digitalization trends of the Chemical industry, the following are distinguished and discussed: big data and advanced analytics, Internet of things, simulation modeling and digital twins, blockchain technology, specialized software, augmented and virtual reality, cloud computing, artificial intelligence and machine learning, cybersecurity and data privacy, mobile applications. Though, the problem of evaluation and measuring the level of digitalization still exists, especially in the Chemical industry a number of indices for the digitalization evaluation is named. Among which the I-DESI index is suggested for the future reference at the international scale. The modern Chemical enterprises in Russia as well as in other countries use digital technologies because they increase efficiency, accelerate innovation, improve safety and create new business models in an increasingly competitive environment. Various leading chemical companies pay special attention to investments in digitalization of their production because it facilitates the sustainable development of their businesses.

Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy

<p>In the domain of cloud computing and network resource virtualization, existing fusion techniques for containers and virtual machines suffer from high energy consumption, inflexible scheduling requirements, and suboptimal resource utilization. This study critically examined the current methods, accounted for the contemporary requirements, and developed a novel strategy aimed at maximizing resource utilization while minimizing energy consumption. Comprehensive experiments illustrate the superiority of our approach over state-of-the-art fusion strategies such as Kubernetes+Kubevirt and OpenStack+Kubernetes, demonstrating significant reductions in energy consumption, improved resource utilization, and enhanced system performance.</p> <p> </p>

Virtualization in Cloud Computing

Virtualization in Cloud Computing

A Review of Cloud Computing's Virtualization Techniques and Types

A Review of Cloud Computing's Virtualization Techniques and Types

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

AbstractEnergy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).

Use of Technology in Enhancing Learning Among Nurses in Saudi Arabia; a Systematic Review.

The landscape of teaching and learning, particularly in the realm of technology-supported education, is being transformed by the ongoing presence of portable digital assistant devices and other technological tools. Such technologies have become an integral aspect of learning these days. The use of Virtual Reality, Augmented Reality, cloud computing, and social media through platforms such as Twitter, Dropbox, Google Apps, and YouTube has become the norm in modern education and has greatly enhanced the quality of higher nursing education. Therefore, this study aims to synthesize evidence on the effectiveness of technology in nursing education in Saudi Arabia. The study used a systematic review methodology to extract relevant studies from databases and reference lists of related literature reviews. Two independent reviewers screened the title, abstract, and full texts based on predefined eligibility criteria. The review identified four themes from the data retrieved from 15 published articles. The themes include attitude towards e-learning, challenges and quality related to e-learning, social media and smart phones usage, virtual reality and simulation experience. Mixed attitudes have been identified among the participants of the selected studies. Various challenges linked with e-learning, social media usage, smart phones, and simulation have been identified inclusive of technical issues, lack of awareness, lack of training, etc. The findings have also stated that awareness level should be increased related to e-learning for better outcomes in Saudi Arabia. The findings suggest that technology has the potential to improve learning outcomes for nurses, including those involved in research. Therefore, it is crucial to ensure that both educators and students receive adequate training on how to effectively use the upcoming technology in Saudi Arabia.

Pre-copy live virtual machine migration techniques in cloud computing using HDWHM algorithm

Live Virtual Machine (VM) migration is a legacy of server virtualization in cloud computing. One of the difficulties in a Virtual Machine (VM) Environment is load balancing, and the pre-copy migration strategy is both time-consuming and expensive. In this paper, we propose an efficient VM migration strategy to compute the parameters for Linear Adam Algorithm overload detection and Interquartile Range (IQR) under-loaded identification, the Central Processing Unit (CPU) utilization is sufficient. VMs require constant network traffic, storage, CPU, and memory demands. This study recommends using the novel Hamming Distance-Based Weighted Harmonic Mean (HDWHM) strategy for pre-copy live VM migration. We tested the suggested strategy using datasets from CloudSim-3.0.3 on a real PlanetLab. Simulations show that the suggested approach decreases host downtimes, energy consumption, VM migrations, and SLA degradation.

A review of QoE research progress in metaverse

Metaverse is a virtual world that maps and interacts with the real world. It is a digital living space with a new social system created through Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Artificial Intelligence (AI), Cloud Computing (CC) and other technologies. With the rapid development of block chain technology, interactive technology, network and computing technology, communication network technology, digital twin technology, artificial intelligence technology, Internet of Things technology and electronic game technology, the metaverse has also ushered in a stage of rapid development. However, enormous difficulties and challenges remain in how to create a perfect metaverse system that can truly break the barriers between reality and virtual. The quality of content, device and interaction in the metaverse all have an important impact on the Quality of Experience (QoE). The purpose of this paper is to provide the latest research progress of the current metaverse QoE, which will be helpful to the future research of the metaverse QoE and provide the direction for further research.

Energy efficient cloud computing using secure virtual machine migration: A taxonomy

Numerous cloud service enterprises have launched a broad variety of cloud services. Several businesses and people use these programs on a daily basis. The efficiency of cloud service depends on efficiency of the service providers. It is also important for cloud customers to analyses and review the available cloud resources thoroughly to pick the best cloud service. Secure virtual machine placement is a big issue during the migration of virtual machines in cloud computing. Secure virtual machine migration maximizes the throughput and reliability of the system and reduces Service Level Agreement (SLA) violations. This article focuses on secure virtual machine migration with energy-efficient techniques

Characterizing and optimizing Kernel resource isolation for containers

Container-based virtualization has become increasingly popular as a lightweight alternative to hypervisor-based virtualization in cloud computing. Isolation is a fundamental property for consistent and reliable performance for cloud environment. However, the isolation between containers is much weaker than virtual machines as containers on the same host share one underlying host kernel. Existing works have mainly focused on the isolation problems at physical resources (e.g. CPU) level and almost not discussed with kernel resources (e.g. lock). In this paper, we perform a study to quantify kernel resource isolation for containers with a new microbenchmark, KRIBench. Then we describe kernel resource isolation issues and identify several kernel resources competition behind the poor isolation. Furthermore, we design and implement Valve, a general and flexible system that reduces kernel resources competition through limiting usage of system calls. Valve adopts Pareto-based container identification to locate misbehaving containers and supply–demand model to manage usage of system calls. The evaluation results demonstrate that our system can effectively enhance the kernel resource isolation for containers with negligible performance overhead.

An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment.

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.

Effective and Secure vWSN Applications in a Virtualized Cloud Computing Environment

Security is one of the key concerns in cloud computing. We are proposing Virtual Wireless Sensor Network (vWSN) which is one of the key features of cloud computing in the area of agriculture. Our main focuses are effective and secure vWSN in virtualized cloud computing environment with enhanced security to detect environmental monitoring, humidity monitoring, soil moisture, air quality (pollution) monitoring, insect monitoring, pest and disease control, which is based on virtualization and cloud computing technology, centrally managed device having flexible and configurable parameter with less maintenance and operational cost. To the best of our knowledge, vWSN is the first approach to agriculture based on extensional WSN information. WSN contains both control plane (signaling) and data plane (forwarding) coupled together.WSN is a specific purpose computer containing hardware whose main components are memory, CPU, IO, registers, Data and address bus, timer, sensor, computing logic and decision making logic. WSN is a physical device which needs to be placed in the field in certain topology (Ring, mesh, tree, star) which may need to be protected for damage and erosion due to various parameter including but not limited to physical theft, damage due to weather, animal and various other reason. In this paper our approach is to propose virtualized model to replace physical wireless sensor network (WSN) to virtual machine (VM) based vWSN which can be deployed on the cloud. Since security is one of the major concern for VM in cloud computing therefore we also proposed the enhanced 3-tier security model for vWSN.

Managing Virtual Programmable Switches: Principles, Requirements, and Design Directions

Virtualization is a key enabler of various networking concepts, like virtual networks, cloud computing, and network function virtualization. With the advent of software-defined networks and programmable forwarding planes, researchers began investigating solutions to support the creation of virtual switches that one can program using emerging domain-specific languages like P4. This article covers the current state of affairs in programmable switch virtualization and highlights key principles, challenges, and opportunities related to operations and management of virtual switches whose behavior can be redefined on the fly and independently. We also introduce PvS, a proof-of-concept management layer that focuses on secure and independent management of programmable virtual switches.

New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing.

Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.