Application Of Cloud Platform Research Articles

Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation Using Reinforcement Learning Approach

Abstract:: Virtual machines are used to reduce cloud platform application performance, management costs, and access irregularities. Virtual machines are frequently vulnerable to delays, overburdening workloads, and other obstacles while consolidating and migrating servers. To significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented to control energy dissipation, monitor overloading, and address underloading problems. Background:: The process of consolidation involves more calculations and resources in order to transfer services between virtual machines, provided that Service Level Agreements are observed. Methods:: The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. The main design considerations for the suggested Dynamic Weightage algorithm, which includes the clustering approach in relation to reinforcement learning approaches, are overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines is created, and resources are distributed according to performance and energy requirements. Virtual machine resource requests are converted into a matching relationship factor, which represents the individual hosts while taking PPR into account. The overall workload associated with virtual machine consolidation is also provided by these estimations. It is noted that there is little energy trade-off and that performance is maintained at a nominal level across the cluster. The architecture is put into practice throughout offline platforms, which are dispersed ecosystems that allow for increased system performance and scaling. Results:: The CloudSim simulator is used to validate the system using datasets that are obtained from PlanetLab. According to the data, energy saving has produced yields of up to 47% and promising quality of service attributes. Conclusion:: The validation of the system is performed using the CloudSim simulator with datasets from PlanetLab. The results indicate significant energy conservation, up to 47%, along with promising quality of service parameters. The proposed architecture is compared with other state-of-the-art algorithms for distributed architectures and heterogeneous environments, showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation and energy efficiency in the proposed architecture, which has been tested on a Proliant G7- based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming OpenStack-based techniques in simulation results.

The Real Time Monitoring System and Early Detection of Unbalanced Load for the Three Phase Distribution Systems in Urban Areas

This paper introduces a three-phase unbalanced load condition monitoring system in a power distribution network that can monitor and provide early warning. The distribution system is a component of the power system that must always be observed in maintaining the service of delivering electricity to customers. Currently, many technologies are applied to the distribution system that can ensure the operation of the distribution network, especially distribution transformers to determine real-time transformer conditions so that damage and power losses can be minimized, but do not provide early detection. Therefore, the role of information technology is currently one of the investments that must be made for all electric power supply operators in maintaining the performance of distribution transformers through early detection. In this study, the system developed based on Internet of Things (IoT) technology that was built consists of: embedded system devices that function to carry out measurements and transmit data, Web Service software that functions to receive data from sensor systems, and Arduino-based software using the Blynk Cloud application platform in presenting data and managing data through Android display devices (HP/Laptop). The Blynk is a compatible platform for applications on Mobile OS (iOS and Android) in controlling Arduino, Raspberry Pi, or ESP8266-based modules, as well as similar modules via the Internet (IoT). This system was built to provide an early warning function against network parameter anomalies such as overload, overvoltage, and unbalanced load. After getting the data, the system will perform calculations to obtain recommendations for system optimization in the form of balancing, maintenance, or load maneuver. Based on testing, the time needed to send and receive notifications on smartphone users via the blynk application ranges from 3-4 seconds. By setting a tolerance limit of 20%, when the load is severely unbalanced where the neutral current is almost equal to or greater than the phase current, the local indicator light will light up and the Android system will provide an unbalanced load indicator.

An effective mechanism for ontology-based cloud service discovery and selection using Aneka cloud application platform

An effective mechanism for ontology-based cloud service discovery and selection using Aneka cloud application platform

An effective mechanism for ontology-based cloud service discovery and selection using Aneka cloud application platform

An effective mechanism for ontology-based cloud service discovery and selection using Aneka cloud application platform

A probabilistic public key encryption switching scheme for secure cloud storage

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates {mathcal {O}}(m+2 log N) bits of ciphertext where min {mathbb {N}} and m<n, nin {mathbb {N}} is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper.

Construction and Application of a Cloud Platform for English Teaching Supported by Computer Network Technology

With the rapid development of network technology, network users have higher and higher requirements for understanding and using the network. At present, the wide application of cloud technology provides the best solution for people. Network is the main carrier of information exchange and interpersonal communication and a new way for human beings to obtain information. The education community, like other industries, is increasingly recognizing the importance of cloud computing and constantly using these new capabilities in education and instruction. Cloud service group and WeChat group are the new mobile applications. This article introduces cloud computing technology into college English classroom, aiming to explore new methods of college English by using the new Internet technology. At the same time, it also discusses some problems existing in the current college English classroom, providing new ideas for the reform of college English classroom. This paper adopts J2EE technology in college English classroom teaching cloud platform application. Based on B/S model, a cloud university English teaching platform is established. The cloud platform English classroom teaching system covers three modules of English listening, English reading, and English writing, covering various fields of education and management. It has laid a solid foundation for the development of education informatization and network.

Piano Information Teaching Mode Based on Deep Learning Algorithm

In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching objectives, curriculum objectives, and education and training objectives. A piano information classroom integrating cloud application platform, teaching platform, resource platform, learning space, and interactive classroom is built. The previous teaching mode is optimized to build an innovative teaching mode of piano information classroom. The evaluation index system of piano informatization classroom teaching quality is constructed, and the hierarchical structure model of each evaluation index is established by using the analytic hierarchy process. The hierarchical analysis method is used to establish a hierarchical structure model of each evaluation index. The judgment matrix is determined by the nine-digit scale method. After the consistency verification of the judgment matrix, the weight of the quality evaluation of piano information classroom teaching is calculated. The new mode optimizes the weight and threshold of BP neural network in deep learning algorithm by genetic algorithm (GA). The weight of each classroom teaching quality evaluation index is input into the GA-BP neural network, and the network output result is the piano information classroom teaching quality evaluation score. The test results show that the optimal number of hidden layer nodes for the BP neural network is 7, when the GA-BP neural network iterations are 95. This method can evaluate the quality of piano information classroom teaching, with high evaluation accuracy and strong practical application.

The Construction of English Smart Classroom and the Innovation of Teaching Mode under the Background of Internet of Things Multimedia Communication

There are some problems in English smart classroom, such as slow response speed of related platforms and room for improvement of teaching effect. Therefore, it is necessary to construct English smart classroom and innovate teaching mode under the background of Internet of Things (IoT) multimedia communication. The teaching objectives are divided into classroom teaching objectives, curriculum objectives, and education and training objectives, so as to design the technical support for English smart classroom, that is, to build a smart classroom integrating cloud application platform (including students’ learning cloud and teachers’ teaching cloud), teaching platform, resource platform, learning space, and interactive classroom by using Internet of Things multimedia communication technology, and build an innovative teaching mode of English wisdom classroom. The fuzzy hierarchical evaluation model is used to design the fuzzy hierarchical evaluation algorithm. Through the fuzzy hierarchical evaluation algorithm, the teaching effect of smart classroom is evaluated, and the final evaluation result is obtained. The results show that the average platform login time of the proposed method is always below 0.3 s, the average score of the summative evaluation is high, the evaluation time is always below 0.4 s, the average communication delay is 0.54 s, and the user satisfaction is always above 9.2. The practical application effect is better.

Optimal Load Balancing with Locality Constraints

Applications in cloud platforms motivate the study of efficient load balancing under job-server constraints and server heterogeneity. In this paper, we study load balancing on a bipartite graph where left nodes correspond to job types and right nodes correspond to servers, with each edge indicating that a job type can be served by a server. Thus edges represent locality constraints, i.e., an arbitrary job can only be served at servers which contain certain data and/or machine learning (ML) models. Servers in this system can have heterogeneous service rates. In this setting, we investigate the performance of two policies named Join-the-Fastest-of-the-Shortest-Queue (JFSQ) and Join-the-Fastest-of-the-Idle-Queue (JFIQ), which are simple variants of Join-the-Shortest-Queue and Join-the-Idle-Queue, where ties are broken in favor of the fastest servers. Under a "well-connected'' graph condition, we show that JFSQ and JFIQ are asymptotically optimal in the mean response time when the number of servers goes to infinity. In addition to asymptotic optimality, we also obtain upper bounds on the mean response time for finite-size systems. We further show that the well-connectedness condition can be satisfied by a random bipartite graph construction with relatively sparse connectivity.

Cloud Education Chain and Education Quality Evaluation Based on Hybrid Quantum Neural Network Algorithm

This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA‐BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1‐X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA‐BP neural network algorithm has a good effect on the evaluation of school education quality.

Optimal Load Balancing with Locality Constraints

Applications in cloud platforms motivate the study of efficient load balancing under job-server constraints and server heterogeneity. In this paper, we study load balancing on a bipartite graph where left nodes correspond to job types and right nodes correspond to servers, with each edge indicating that a job type can be served by a server. Thus edges represent locality constraints, i.e., an arbitrary job can only be served at servers which contain certain data and/or machine learning (ML) models. Servers in this system can have heterogeneous service rates. In this setting, we investigate the performance of two policies named Join-the-Fastest-of-the-Shortest-Queue (JFSQ) and Join-the-Fastest-of-the-Idle-Queue (JFIQ), which are simple variants of Join-the-Shortest-Queue and Join-the-Idle-Queue, where ties are broken in favor of the fastest servers. Under a "well-connected'' graph condition, we show that JFSQ and JFIQ are asymptotically optimal in the mean response time when the number of servers goes to infinity. In addition to asymptotic optimality, we also obtain upper bounds on the mean response time for finite-size systems. We further show that the well-connectedness condition can be satisfied by a random bipartite graph construction with relatively sparse connectivity.

Construction and Application of Cloud Platform for Urban Rail Transit

&lt;p&gt;Rail transit is a new form of transportation in the process of modern urban development. It has a far-reaching impact on improving urban traffic environment, relieving urban traffic pressure and improving urban traffic service quality. The rail transit cloud platform is an important part of modern rail transit, and it is also a key factor related to the construction quality of urban rail transit. In this paper, the current situation of construction and application of urban rail transit cloud platform are introduced in detail, and the related standard compilation of urban rail transit cloud platform, achievements in the development and construction process, construction mode, index establishment, acceptance process optimization, cloud operation and maintenance system establishment and other issues are systematically expounded. According to the technical drive and standard compilation of urban rail transit cloud platform, the development of urban rail transit cloud platform is prospected.&lt;/p&gt;

Design of an Unmanned Boat System for Floating Garbage Salvage and Water Quality Monitoring Based on OneNET

In view of the serious floating garbage on water surface and the drawbacks of relevant cleaning equipment, an unmanned boat system for floating garbage salvage and water quality monitoring based on OneNET IOT platform is designed. The design of hardware, software and cloud platform application of the system are introduced in detail. The system is mounted on a small remote-control boat, which monitors water quality data through various sensors and uploads the data to the cloud platform while collecting garbage, and sends warning emails when water quality data exceeds the threshold value. The experimental results show that the system has realized the transmission of monitoring video in real time, remote control of floating garbage salvage, water quality information collection and release, and warning of monitoring data over threshold value. The system has a certain application prospect in water quality monitoring and protection of small-scale water area.

Accelerating Parallel Applications in Cloud Platforms via Adaptive Time-Slice Control

Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation. Existing methods optimize parallel applications by promoting the priorities of involved VMs. They cannot fully explore the performance of parallel applications, because they ignore the time-slice requirements of different phases of parallel applications. Furthermore, non-parallel applications experience unsatisfied performance because of low scheduling priorities. Given empirical analysis on time-slices of virtual machines (VMs), we find that shortening time-slices can mitigate synchronization overhead which incurs during communication phases, while over-short time-slices cause frequent cache misses in computation phases. Accordingly, we propose an Adaptive Time-slice Control (ATC) mechanism. ATC first detects the phases of parallel applications based on lock latency or cache misses. Then, ATC shortens time-slices during communication phases and prolongs time-slices during computation phases for parallel applications, and sets a uniform time-slice for non-parallel applications. We evaluate ATC using seven well-known benchmarks with 25+ applications. Experiments show that ATC obtains 1.5-75× performance gain for running parallel applications than state-of-the-art solutions, with nearly unaffected impact on non-parallel applications.

Advances in the application of cloud platform in diagnosis and treatment of OSA

Obstructive sleep apnea(OSA) has been a major public health problem due to a surge in the number of patients with OSA, which can create a heavy socio-economic burden. How to effectively carry out mass screening and prevention is an important link in China's health management and chronic disease prevention.The development of cloud computing and Internet of things technology has brought new directions and new solutions.For the sake of diagnosis and treatment of disease involving multiple disciplines, cross cooperation problems, we can use the Internet of things, a cloud platform and the database as the core architecture to establish the network which contains family-primary hospitals-tertiary hospitals-specialized subject and sleep medicine laboratory.With the help of handsets, we are able to realize the effectiveness and convenience of remote cooperation and eventually form the cloud platform diagnosis and treatment system of sleep-related breathing disease based on the large sample data.

Research on Application of SME Manufacturing Cloud Platform Based on Micro Service Architecture

For SMEs, intelligent manufacturing execution is an important part of perfecting PSS. the business requirements of small and medium-sized manufacturing enterprises vary greatly, and system development and maintenance are facing enormous challenges. However, the existing manufacturing execution system can not quickly respond to the requirements of business reconfiguration. Aiming at the current requirements of manufacturing execution system of SME, and with the development of enterprise in cloud manufacturing, this paper proposes a manufacturing cloud platform application research for small and medium-sized manufacturing enterprises based on micro-service architecture. In this research we have introduced the design of platform’s micro-service architecture and the implementation of platform-based business system. It can solve the problems of business expansion and user requirement cannot be quickly respond caused by the huge application of traditional monolithic architecture. It provides an effective support for the development of business systems.

Application of Cloud Platform in Health Promotion-An Example of Improvement on Sugar-sweetened for Adolescents

APP-Cloud-based mobile healthcare has rapidly risen in recent years, which brings the disabled people, bad walker and dementia patients the chance to receive medical treatment opportunities without having to go to the hospital. The healthy people can also enjoy the benefit it brings to make ubiquitous monitoring for self-healthcare. In this paper, a pre-test to investigate the factors including the sources and drinking contexts which affect the intake of sugary drinks are made for the target group of adolescents. Next, the APP tool is designed to provide reminding and guidelines in real time. The early warning core implemented in the cloud can be used to monitor users' past drinking records and predict possible impacts as the time passes. The experimental results show this kind of health management mechanism indeed have considerable effect and which can achieve the indirect goal of reducing medical costs.

Research on Hybrid Learning Based on Cloud Platform

As cloud computing makes online learning more personalized, convenient and low-cost, the cloud platform application research in education and teaching has also become a hot topic. From the perspective of theoretical analysis, this paper discusses the concepts of cloud platform and hybrid learning, as well as the characteristics of developing hybrid learning based on cloud platform in adult colleges and universities. The purpose of this paper is to provide relevant theoretical guidance for adult education teaching in the intelligent environment supported by Internet and cloud technology.

Real-time data capture with electronic cigarettes for smoking cessation programme: a cloud platform for behavioural research

Tobacco smoking is a major risk factor for many chronic diseases, which led to the popularity of electronic cigarette (e-cigarette) and showed to be an effective tool for tobacco smoking...

Arkas: Rapid reproducible RNAseq analysis.

The recently introduced Kallisto pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. We offer cloud-scale RNAseq pipelines Arkas-Quantification, and Arkas-Analysis available within Illumina's BaseSpace cloud application platform which expedites Kallisto preparatory routines, reliably calculates differential expression, and performs gene-set enrichment of REACTOME pathways . Due to inherit inefficiencies of scale, Illumina's BaseSpace computing platform offers a massively parallel distributive environment improving data management services and data importing. Arkas-Quantification deploys Kallisto for parallel cloud computations and is conveniently integrated downstream from the BaseSpace Sequence Read Archive (SRA) import/conversion application titled SRA Import. Arkas-Analysis annotates the Kallisto results by extracting structured information directly from source FASTA files with per-contig metadata, calculates the differential expression and gene-set enrichment analysis on both coding genes and transcripts. The Arkas cloud pipeline supports ENSEMBL transcriptomes and can be used downstream from the SRA Import facilitating raw sequencing importing, SRA FASTQ conversion, RNA quantification and analysis steps.