Virtualized Cloud Environment Research Articles

Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.
 OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.
 METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.
 RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment 
 CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Physics laboratory work using cloud technologies

The article describes the structure and content of the physics textbook «Laboratory work using cloud technologies» for students of vocational higher education institutions. The importance of modernizing the educational process in physics in general and approaches to conducting laboratory work in particular using the methods of modern information and communication and cloud technologies are analyzed. The importance and advantages of digitization of the educational process in physics through the use of cloud services, virtual laboratory works, simulators, mobile applications, flash animations, etc. are substantiated. The theoretical and practical blocks of the manual are described in detail. The theoretical unit is represented by three sections and is filled with didactic and methodical materials that contain recommendations, advice and rules for organizing, preparing and conducting a physical experiment. The practical unit of the manual is represented by one section «Laboratory works», which presents instructions for seven laboratory works. A feature of such works is the use of virtual laboratories, simulators, and cloud environments for testing physical laws, researching physical phenomena, and determining physical quantities. The article provides an example of instructions for laboratory work «Determining the index of refraction of light at the boundary of separation of two media». The research presented in this experiment is carried out using the «Refraction of Light» physical simulator of the website Phet.colorado.edu - the website of the University of Colorado. The process of measuring the refractive index of light at the boundary between two media is described in detail and step by step. The order of performing some tasks is illustrated by the corresponding screen images of the «Refraction of Light» physics simulator. The materials for surveying and monitoring the educational achievements of students are given, among which three directions of questions and tasks are singled out for the didactic purpose: to check the assimilation of knowledge, to check the formation of skills and abilities, to check the formation of beliefs. This manual has been tested in groups of students of the humanitarian profile of institutions of vocational pre-higher education. At the All-Ukrainian competition «Pedagogical OSCAR – 2023», the manual won the 2nd place in the nomination «Innovative approaches to the organization of practical training of students of vocational pre-university education».

Fortifying the Cloud: Unveiling the Next-Generation Security Model of AWS

Amazon Web Services (AWS) is a renowned provider of cloud computing services and infrastructure for businesses of all kinds. As organizations shift crucial data and apps to the cloud, security becomes paramount. This abstract describes AWS's security model and efforts to secure client data and prevent unauthorized access. The AWS security model protects client assets with many layers. AWS data centers include tight access controls, surveillance systems, and numerous levels of protection. AWS also meets worldwide security requirements and best practices. The shared responsibility approach governs AWS and customer security. AWS secures the infrastructure, while clients secure applications, data, and user access. AWS offers identity and access management, network security, encryption, and threat detection to help businesses meet security requirements. AWS security relies on identity and access management (IAM) to govern user access to AWS resources. IAM lets organizations set fine-grained access controls, implement multi-factor authentication, and link with identity systems, improving security. AWS protects data in transit and at rest with strong network security. Network access control lists (ACLs) and security groups provide clients granular control over inbound and outgoing traffic in Virtual Private Cloud (VPC) environments. AWS data is protected by several encryption solutions, including key management services for secure key storage and access. AWS monitors and logs to detect and mitigate security risks. AWS CloudTrail, Config, and Guard Duty let clients monitor and audit their AWS environments, detect configuration changes, and detect security breaches and criminal behavior. In conclusion, AWS security protects client data and applications. AWS secures cloud installations through physical, network, data, and identity and access management. Organizations must understand their shared obligations and use security services and features to secure their AWS resources.

FMMEHO Based Workflow Scheduling in Virtualized Cloud Environment for Smart Irrigation System

Extensive and exhaustive water utilization for agriculture, industries and ground water consumption for domestic purposes has heavily deterioted the water bodies. Cloud and sensor technology is widely deployed in a several real-time applications, especially in agriculture. The transformation of data obtained from large sensor networks into a valuable knowledge and assests for applications can effectively leverage the techniques like Cloud Computing (CC). In CC, scheduling the workflow is the major concern that focuses on comprehensive execution of workflows without compromising the Quality of Service (QoS). But workflow scheduling augmented with resource allocation is extremely challenging task because of its inherent computational intensity, task dependencies, and heterogeneous cloud resources. In this article, a novel Optimum Energy and Resource Aware Workflow Scheduling (OERES) scheme that is motivated by popular Fuzzy Membership Mutation Elephant Herding Optimization (FMMEHO) algorithm is proposed, that aims to schedule the task workflow to Virtual Machines (VMs) that are involved in computation. This also concentrates on dynamically deploying and un-deploying the VMs pertaining to the task requirements. The FMMEHO algorithm is a popular nature inspired technique, which is rooted on herding patterns of the giant mammals, the elephants. This algorithm employs a clan operator that updates the location and distance of elephants depending resource and energy usage of each clan in the context of matriarch elephant. The proposed OERES schema elevates the resource utilization and simultaneously mitigates the energy usage without compromising the dependency and deadline constraints. This work uses the famous Cloud Sim simulator to simulate the underlying cloud environment to investigate the effectiveness of proposed model. The efficacy of the scheduling methods is examined based on important parameters like mean Resource Utilization (RU), Energy utilization or Consumption/ Task (ECT), Total Energy Consumption (TEC), Makespan and Execution Time per Task (ETT). The results very well portray the effectiveness of proposed OERES algorithm against already existing methods.

Virtual Cloud Environment Protection Method Based on Attack Graph Structure

The article proposes the creation of an effective system for detecting and responding to external influences in order to minimize the consequences of breaching the protection of cloud virtual resources in a timely manner. Mathematical models have been improved to ensure the protection of virtual cloud resources for software-configured networks, namely: a mathematical model of the impact of an attack on virtual cloud resources, a mathematical model for assessing the state of virtual cloud resources, a mathematical model for choosing a countermeasure based on a complex indicator for software-configured networks. Based on the obtained mathematical models, a graph of attacks on the virtual cloud environment was developed. This graph allows you to get information about all known system vulnerabilities.

Where Is Research in the Era of Electronic Health Records?

IntroductionData extraction from electronic health records (EHRs) for use in clinical research continues to be labor-intensive and to offer little benefit over traditional paper chart reviews. This is largely due to poor integration of EHR systems with hospital process flow, which still relies heavily on traditional paperwork as a means of documentation. DiscussionNew methods in data collection through mobile applications have streamlined data entry through better data standardization and improved overall data quality. However, mobile applications address only a portion of the problem. Data entry errors and legacy integration will continue to be an issue when there are changes between practitioners with different EHR systems. The combination of a mobile application with the cloud platform has been applied in multiple specialties to monitor recovery and patient-reported outcomes. ConclusionMobile applications along with a virtual cloud environment to host data provide a reasonable solution for consolidating patient data and can accelerate population research.

Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment

Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches “Replication with equal failure rate” and “Efficient reliability replication method” are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches.

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

An Early Intervention Technique for At-Risk Prediction of Higher Education Students in Cloud-based Virtual Learning Environment using Classification Algorithms during COVID-19

Higher Education is considered vital for societal development. It leads to many benefits including a prosperous career and financial security. Virtual learning through cloud platforms has become fashionable as it is expediency and flexible to students. New student learning models and prediction outcomes can be developed by using these platforms. The appliance of machine learning techniques in identifying students at-risk is a challenging and concerning factor in virtual learning environment. When there are few students, it is easy for identification, but it is impractical on larger number of students. This study included 530 higher education students from various regions in India and the outcomes generated from online survey data were analyzed. The main objective of this research is to predict early identification of students at-risk in cloud virtual learning environment by analyzing their demographic characteristics, previous academic achievement, learning behavior, device type, mode of access, connectivity, self-efficacy, cloud platform usage, readiness and effectiveness in participating online sessions using four machine learning algorithms namely K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF). Predictive system helps to provide solutions to low performance students. It has been implemented on real data of students from higher education who perform various courses in virtual learning environment. Deep analysis is performed to estimate the at-risk students. The experimental results exhibited that random forest achieved higher accuracy of 88.61% compared to other algorithms.

Implementation of Data Synchronization Mechanism in Virtual Desktop Infrastructure

There are more and more applications and services of Cloud technology. Whether the government, enterprises or organizations, even everyone may have requirements of constructing cloud systems. To build the virtual cloud infrastructure VMware vSphere which is the leader of the information industry on virtual platforms can let users use Virtual Desktop Infrastructure by their mobile device. This study integrated some management programs such as Activity Directory, VMware vCenter, View connection server, VMware vSphere and used the shared storage system of FreeNAS to provide the cloud virtual environment to users. With the simple interface to manage, it successfully provides administrators to build Virtual Desktop Infrastructure faster. Users can use their own device to control the virtual desktop in a mobile classroom by VMware Horizon View or browser. Users can also use a shared storage system to be the information exchange platform. If you can connect to the Internet, you can use your own virtual desktop anywhere. Finally, the complete construction implements the combination of several operating systems and improves their performance to save lots of hardware cost. It also meets the social focus of carbon reduction and environment protection to achieve sustainable use.

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

Leadership in a Virtual Working World: A Review

Understanding how the function of leading in a virtual cloud environment is becoming critical as organisations are increasingly forced to use dispersed teams to continue business operations in the “un-usual” current economy. This paper shares some findings of a study titled “A select bouquet of leadership practices advancing good governance and business ethics: A conceptual framework” undertaken in the pursuit of a doctoral degree submission. These findings can inform leaders by providing academic knowledge on the subject. Furthermore, it can support the enterprise leadership environment in transitioning from a physical to a cloud-based proximity which, because of the COVID-19 pandemic, must and is happening relatively fast. Keywords: COVID-19; communication; empathy vision; trust; Leadership; humility; people; profit.

Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan

Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan

A Multi-Tier Architecture for the Management of Supply Chain of Cloud Resources in a Virtualized Cloud Environment

As cloud resource demand grows, supply chain management (SCM), which is the core function of cloud computing, faces serious challenges. Quite a number of techniques have been proposed by many researchers for such a challenge. As such, numerous proposed strategies are still under reckoning and modification so as to enhance its potential. An optimized dynamic scheme that combined several algorithms' characteristics was proposed to map out such a challenge. The hybridized proposed scheme involved the meta-heuristic swarm mechanism of ant colony optimization (ACO) and deterministic spanning tree (SPT) algorithm as it obtained faster convergence chain, ensured resource utilization in least time and cost. Extensive experiments conducted in cloudsim simulator provided an efficient result in terms of minimized makespan time and throughput as compared to SPT, round robin (RR), and pre-emptive fair scheduling algorithm (PFSA) as it significantly improves performance.

Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

Cloud computing enables the execution of various applications submitted by the users in the virtualized Cloud environment. However, the Cloud infrastructure consumes a significant amount of electrical energy to provide services to its users that have a detrimental effect on the environment. Many of these applications (tasks), like those belonging to the healthcare system, scientific research, the Internet of Things (IoT), and others, are deadline-sensitive. Hence efficient scheduling of tasks is essential to prevent deadline violation, decrease makespan, and at the same time reduce energy consumption. To address this issue, we have considered the bi-objective optimization problem of minimization of energy and makespan and have proposed two scheduling approaches for independent, deadline-sensitive tasks in a heterogeneous Cloud environment. Our first approach is a greedy heuristic based on the Linear Weighted Sum technique. The second one is based on Ant Colony Optimization and uses a combination of heuristic search and positive feedback of information to improve the solution. Both approaches use a three-tier model where tasks are scheduled by taking into account the properties of three entities- tasks, VMs, and hosts. Moreover, we have proposed a suitable strategy for scaling of Cloud resources to improve energy-efficiency and task schedulability. Extensive simulations using Google Cloud trace-logs and comparison with some state-of-art approaches validate the effectiveness of our proposed scheduling techniques in achieving a proper trade-off between the energy consumption of the virtualized Cloud infrastructure and the average makespan of the tasks.

A Service Sustainable Live Migration Strategy for Multiple Virtual Machines in Cloud Data Centers

Virtual machine (VM) migration is an indispensable aspect of a virtualized cloud environment. It assists in resource management by dynamically relocating VMs from one physical machine to another. This is an essential aspect especially for big data applications that are prone to variable workloads and often demand relocation of resources in terms of VMs. However, such applications not only experience stochastic workloads but also have stringent requirements on the maximum tolerable latency. To address such issues, VMs are often relocated using live VM migration. VM migration is associated with overheads, hence, in this paper, we propose a modified serial migration strategy to migrate multiple VMs based on the pre-copy live migration technique. We propose to interleave the pre-copy stages in such a way that a balance is achieved between the migration time and downtime overheads. The proposed technique is compared with the state-of-the-art serial, parallel, and improved serial migration strategies. Concerning downtime, the proposed approach performs exceptionally well compared to both serial and improved serial methods. The downtime of the proposed scheme and parallel are comparative for read-intensive applications (low dirtying rates). However, for write-intensive applications (high dirtying rates) the former significantly outperforms the latter. The migration time performance of the proposed scheme is observed to be much better than that of the parallel technique and is slightly higher than those of serial and improved serial techniques.

OPSA: an optimized prediction based scheduling approach for scientific applications in cloud environment

Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and Platform-as-a-Service (PaaS). The research community is able to get access to resources on-demand within a short period of time. But, as the demand for cloud resources is dynamic in nature, this affects resource availability during scheduling. Hence, there is a need for efficient management of resources so that tasks can be scheduled based on their execution requirements. To provide a solution, a resource prediction based scheduling approach has been introduced in this paper which automates the resource allocation for scientific applications in a virtualized cloud environment. This research work focuses on the design of an optimized prediction based scheduling approach which maps the tasks of scientific application with the optimal VM by combining the features of swarm intelligence and TOPSIS. The proposed approach minimizes the execution time, cost, and SLA violation rate in comparison to existing scheduling heuristics.

Enhancing Performance and Energy Efficiency for Hybrid Workloads in Virtualized Cloud Environment

Virtualization has attained mainstream status in enterprise IT industry. Despite its widespread adoption, it is known that virtualization also introduces non-trivial overhead when tasks are executed on a virtual machine (VM). In particular, a combined effect from device virtualization overhead and CPU scheduling latency can cause performance degradation when computation intensive tasks and I/O intensive tasks are co-located on a VM. Such an interference also causes extra energy consumption. In this paper, we present Hylics, a novel solution that enables efficient data traverse paths for both I/O and computation intensive workloads. This is achieved with the provision of in-memory file system and network service at the hypervisor level. Several important design issues are pinpointed and addressed during our prototype implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment on KVM, we show that Hylics can significantly improve computation and I/O performance for hybrid workloads. Moreover, this design also alleviates the existing virtualization overhead and naturally optimizes the overall energy efficiency.

Handling Non-Local Executions to Improve MapReduce Performance Using Ant Colony Optimization

Improving the performance of MapReduce scheduler is a primary objective, especially in a heterogeneous virtualized cloud environment. A map task is typically assigned with an input split, which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling schemes, data blocks are copied over the network to a node where the map task is running. This increases job latency and consumes considerable network bandwidth within and between racks in the cloud data centre. Considering this situation, we propose a methodology, “improving data locality using ant colony optimization (IDLACO),” to minimize the number of non-local executions and virtual network bandwidth consumption when input split is assigned to more than one data block. First, IDLACO determines a set of data blocks for each map task of a MapReduce job to perform non-local executions to minimize the job latency and virtual network consumption. Then, the target virtual machine to execute map task is determined based on its heterogeneous performance. Finally, if a set of data blocks is transferred to the same node for repeated job execution, it is decided to temporarily cache them in the target virtual machine. The performance of IDLACO is analysed and compared with fair scheduler and Holistic scheduler based on the parameters, such as the number of non-local executions, average map task latency, job latency, and amount of bandwidth consumed for a MapReduce job. Results show that IDLACO significantly outperformed the classical fair scheduler and Holistic scheduler.

A Subset Feature Selection Based DDoS Detection Using Cascade Correlation Optimal Neural Network for Improving Network Resources in Virtualized Cloud Environment

Cloud computing offers a technological revolution to the end-users need less infrastructure costs with virtualizes resources, and storage remains the insecure to delivers the scalability. The most common type of Distributed Denial of Service DDoS attack, (denial of service), is a serious damage measure that affects virtual cloud users and Internet Service Providers (ISPs) are predominantly affects ongoing service attacks. I'm the recipient. These legacy of machine learning approach used to detect vulnerabilities to the attacker's leading network traffic intervention opening the door. By concentrating feature selection and classification approach with optimized neural network model to detect the DDoS type monitoring. This presents a deep neural network based DDoS detection system using Subset Feature Selection based Cascade Correlation Optimal Neural Network (SFS-C2ONN). The proposed approach is based on assumptions based on flow rate which is collected as dataset previously extracted from a model for network traffic. The test results shows that the sensitivity and specify based calcification approach which is suitable for the detection of neural network architecture and hyper parameters, and the optimizer DDoS attack. The results are obtained by calculating the accuracy of the attack detection.