基于Python的OpenStack运维分析与实践

使用传统的人工方式对OpenStack云平台进行运维不仅耗时耗力,而且效率低下。作为OpenStack的主要开发语言,Python为运维人员提供了强大的工具集和脚本编写能力,极大地促进了运维自动化的实现。文章展示了一个利用Python在OpenStack云平台上创建、上传、查看镜像的案例。实践表明,利用Python对OpenStack私有云平台进行自动化运维可以提高运维效率、减少人为错误、降低运维成本。

Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.

Corrigendum Regarding Missing Ethics Approval Statements in Previously Published Articles

The publisher would like to draw the reader's attention to the following errors.Ethics approval statements were not included in the published version of the following articles that appeared in previous issues of Grain&Oil Science and Technology.The authors were contacted after publication to request ethical approval statements for the following articles.

Assessment of the Drafting Quality of Request Forms Submitted to the Malaria and Parasitology Units at the Institut Pasteur de Cote d’Ivoire

Biological tests provide information on the medical analysis requested by both the patient and the prescriber. It is a communication link between the prescriber and the laboratory staff. The lack of some information on request forms not only affects the drafting quality of the test and patient care, but could also make thousands of data produced by healthcare centers unusable. The aim of this study was to assess the drafting quality of request forms submitted to the Malaria and Parasitology Units at the Institut Pasteur de Côte d’Ivoire. Methods: It was a descriptive cross-sectional study to assess the drafting quality of request forms of various prescribers received at the Institut Pasteur de Côte d’Ivoire. This study was conducted at the Malaria and Parasitology Units, department of Parasitology and Mycology (Institut Pasteur de Côte d’Ivoire), from 6th December 2020 to 6th December 2021. The information on each request forms was recorded on a data collection form designed for this purpose. Each data collection form corresponds to a request forms and each test to a patient. Results: Out of a total of 1990 request forms received, the patient’s age and sex were missing on 18% and 26.8% of the tests respectively. More than half (51.80%) of request forms did not indicate the patient’s place of residence. Clinical information was not provided on 45.90% of the tests. Prescribers omitting their signatures were 51%, stamps were 50.3% and contacts were 71.2%. Only 5.4% of request forms were of good drafting quality. Providing all the required information on the forms could facilitate the use and analysis of data and samples.

基于python+requests的数据采集技术

本文指出在数据采集、数据分析等方面,python语言是人们的不二选择。针对许多人刚开始学习采用python语言进行数据采集时思路混乱的问题,本文首先分析了基于python语言的数据采集基本流程,然后介绍了第三方请求模块requests库向目标地址发送请求的功能,最后通过采集某网站中的单条数据、单页数据、多页数据乃至整个网站的数据案例,逐步为初学者解释了python语言数据采集的基本步骤,并为初学者理清了python语言数据采集的思路,以此为python语言数据采集的初学者提供了一定的参考价值和学习思路。

Design & Test of an Advanced Web Security Analysis Tool (AWSAT)

Considering the escalating frequency and sophistication of cyber threats targeting web applications, this paper proposes the development of an automated web security analysis tool to address the accessibility gap for non-security professionals. This paper presents the design and implementation of an automated web security analysis tool, AWSAT, aimed at enabling individuals with limited security expertise to effectively assess and mitigate vulnerabilities in web applications. Leveraging advanced scanning techniques, the tool identifies common threats such as Cross-Site Scripting (XSS), SQL Injection, and Cross-Site Request Forgery (CSRF), providing detailed reports with actionable insights. By integrating sample payloads and reference study links, the tool facilitates informed decision-making in enhancing the security posture of web applications. Through its user-friendly interface and robust functionality, the tool aims to democratize web security practices, empowering a wider audience to proactively safeguard against cyber threats.

Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

在AOP切面类中获取Session对象的实现方法

在Spring AOP的实现时,切面类访问Request、Session等对象的方法可以有多种。文章通过一个简单案例,探讨了几种不同方法的实现,希望对进行Spring AOP开发中有实际应用需求的读者提供一些参考。

基于Python和Requests快速获取网页数据的方法研究

随着“互联网+”和大数据时代的到来,网络上充斥着各种各样的数据,过滤并获取有用的数据在当今环境下至关重要。文章提出一种基于Python和Requests模块的快速获取网页数据的方法,使用该方法可以获取解析前的网页源代码文本和图片数据,并保存为本地文件,为之后的数据分析和深入学习大数据技术奠定基础。实验结果表明,该方法步骤和代码编写简单易学,运行结果较好,具有一定的实用性。

基于Python的三种网络爬虫技术研究

针对网络爬虫技术选型较多,影响抓取效率和准确性的问题,对基于Python语言的Requests、Scrapy和Selenium三种主流爬虫技术进行分析。首先,安装配置开发环境,完成单线程和多线程爬虫软件开发;其次,爬取“站长之家”网站10页、100页、500页和1,000页简历数据,计算爬取时间;最后,通过爬取“中国裁判文书网”中的数据验证突破反爬虫机制的能力。实验结果表明,Requests爬虫使用一行代码就能实现数据爬取,开发定制灵活;Scrapy爬虫技术平均每页的抓取时间为0.02 s,并发性能突出;Selenium爬虫技术破解网站反爬虫机制能力强。因此,开发网络爬虫技术要综合考虑业务需求和技术特点,只有这样,才能达到最佳的数据抓取效果。

基于Python的豆瓣图书数据的爬取与分析

运用基于Python语言的第三方库Lxml,对豆瓣Top250的图书信息进行爬取、存储、分析,在爬虫设计、分析网页、代码编写等方面形成了完整的过程构架,以期为相关技术的应用提供参考。

基于Python使用爬虫从豆瓣网获取最新上映的电影信息

互联网技术的迅猛发展丰富了人们的学习和娱乐生活,快速地从庞大的互联网资源中获取自己所需的信息变得越来越重要。网络爬虫技术为人们快速从互联网上获取信息提供了技术支持。掌握爬虫相关的技术能够更好地利用互联网上的资源。基于Python语言使用requests模块和Beautiful Soup模块,可以实现自动从豆瓣网上获取最新上映的电影信息的功能。

基于Python的静态网页数据分析与正则提取

学院在修订学前教育人才培养方案时需收集大量幼儿园的联系方式,在互联网上大海捞针无疑是一种效率较低下的方式。基于此,提出了利用Python的第三方requests库实现某省幼儿园的联系方式自动化采集的脚本设计(注:数据来自公共开放平台)。该脚本可分区采集某省各地区在网的幼儿园联系方式并写入Excel表格,极大地提高了工作效率。

基于Python与Requests模块的网络图片爬虫程序设计

在互联网的时代背景下,从大量的网络信息中快速获取批量的图片资源,是一个值得研究的论题。在此基于Python爬虫框架中的5个部分,按照Requests模块的6个步骤,实现快速且批量获取网络图片的爬虫程序设计。实验结果表明,该爬虫程序代码量少、思路清晰、简单易学、结果正确,具有一定的实用性。

Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar PRs.Wepropose a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity calculation.The experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

RFC最近二十年来的动向(2003-2023)

互联网标准文档RFC之于互联网用户,如同空气之于普通人,它透明、隐形,让人意识不到其存在,但同时它又无比关键、必要,赋予了互联网真正的生命与活力,使其在几十年的发展中愈发蓬勃。RFC的概念与意义RFC(Request for Comments),即征求意见稿,是由互联网工程任务组(简称IETF,是世界上唯一的国际性、非政府、开放性的互联网技术开发和标准制定组织)发布的一系列备忘录,包含了关于互联网的重要文字资料。可以说,如果一项技术想要成为全世界软件开发商、硬件制造商以及网络运营商自愿实施的标准和遵守的规范,那么成为R F C是它的必经之路。

基于矩阵卡尔曼滤波的捷联惯导初始对准算法

针对捷联惯导系统大失准角晃动基座条件下的初始对准问题,提出了一种基于矩阵卡尔曼滤波的抗干扰自对准算法。该方法将传统大失准角非线性对准问题,简化为确定初始时刻姿态的线性矩阵卡尔曼滤波估计问题。借鉴惯性系REQUEST算法,将重力矢量在惯性系下的投影作为量测,利用K矩阵在对准过程中为常值特性,以其作为待估计的状态可避免系统模型误差和初始误差的影响,同时避免了传统方法对失准角大、小的假设,也不再区分粗、精对准过程,适用于任意姿态、无初值条件下的对准。在发动机振动及外界扰动条件下进行了四个方位的对准试验,试验表明,对于导航级惯导系统,算法可在5 min内完成初始对准且统计方位均方差小于3'(1σ),略优于传统算法。