File Assignment Policy in Network Storage System

Network storage increase capacity and scalability of storage system, data availability and enables the sharing of data among clients. When the developing network technology reduce performance gap between disk and network, however, mismatched policies and access pattern can significantly reduce network storage performance. So the strategy of data placement in system is an important factor that impacts the performance of overall system. In this paper, the two algorithms of file assignment are presented. One is Greed partition that aims at the load balance across all NADs (Network Attached Disk). The other is Sort partition that tries to minimize variance of service time in each NAD. Moreover, we also compare the performance of our two algorithms in practical environment. Our experimental results show that when the size distribution (load characters) of all assigning files is closer and larger, Sort partition provides consistently better response times than Greedy algorithm. However, when the range of all assigning files is wider, there are more small files and access rate is higher, the Greedy algorithm has superior performance in compared with the Sort partition in off-line.

Computer Multimedia Network File Transfer Security Protection System

With the rapid development of Internet technology and the rapid increase of multimedia information, people put forward higher requirements for the security, reliability, stability and efficiency of multimedia file transmission. As far as the current network situation is concerned, there are various problems in network security, so how to ensure the safe transmission of files is a very important research topic. In order to improve the security effect of database information, we should also do a good job of classification and archiving storage. Multimedia files must be heavily compressed so that they can be efficiently transmitted over traditional data communication networks. The rapid development of computer networks has made people's lives more and more convenient. In the process of using computer networks, it is necessary to actively strengthen the security control of file transmission on computer networks. Take appropriate measures to avoid network file transmission security risks.

Computer Multimedia Network File Transfer Security Protection System

With the rapid development of Internet technology and the rapid increase of multimedia information, people put forward higher requirements for the security, reliability, stability and efficiency of multimedia file transmission. As far as the current network situation is concerned, there are various problems in network security, so how to ensure the safe transmission of files is a very important research topic. In order to improve the security effect of database information, we should also do a good job of classification and archiving storage. Multimedia files must be heavily compressed so that they can be efficiently transmitted over traditional data communication networks. The rapid development of computer networks has made people's lives more and more convenient. In the process of using computer networks, it is necessary to actively strengthen the security control of file transmission on computer networks. Take appropriate measures to avoid network file transmission security risks.

Always-optimally-coordinated candidate selection algorithm for peer-to-peer files sharing system in mobile self-organized networks

In order to improve the performance of peer-to-peer files sharing system under mobile distributed en-vironments,a novel always-optimally-coordinated(AOC)criterion and corresponding candidate selectionalgorithm are proposed in this paper.Compared with the traditional min-hops criterion,the new approachintroduces a fuzzy knowledge combination theory to investigate several important factors that influence filestransfer success rate and efficiency .Whereas the min-hops based protocols only ask the nearest candidatepeer for desired files,the selection algorithm based on AOC comprehensively considers users' preferencesand network requirements with flexible balancing rules.Furthermore,its advantage also expresses in theindependence of specified resource discovering protocols,allowing for scalability.The simulation resultsshow that when using the AOC based peer selection algorithm,system performance is much better thanthe min-hops scheme,with files successful transfer rate improved more than 50% and transfer time re-duced at least 20%.

Repeated sending file delivery protocol in satellite networking communication

Satellite networking communications in navigation satellite system and spacebased deep space exploration have the features of a long delay and high bit error rate （BER）. Through analyzing the advantages and disadvantages of the Consulta tive Committee for the Space Data System （CCSDS） file delivery protocol （CFDP）, a new improved repeated sending file delivery protocol （RSFDP） based on the adaptive repeated sending is put forward to build an efficient and reliable file transmission. According to the estimation of the BER of the transmission link, RSFDP repeatedly sends the lost protocol data units （PDUs） at the stage of the retransmission to improve the success rate and reduce time of the retransmission. Theoretical analyses and results of the Opnet simulation indicate that the performance of RSFDP has significant improvement gains over CFDP in the link with a long delay and high BER. The realizing results based on the space borne filed programmable gate array （FPGA） platform show the applicability of the proposed algorithm.

Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.

Efficient Search in P2P File Sharing System

A new routing algorithm of peer-to-peer file sharing system with routing indices was proposed, in which a node forwards a query to neighbors that are more likely to have answers based on its statistics. The proposed algorithm was tested by creating a P2P simulator and varying the input parameters, and was compared to the search algorithms using flooding （FLD） and random walk （RW）. The result shows that with the proposed design, the queries are muted effectively, the network flows are reduced remarkably, and the peer-to-peer file sharing system gains a good expansibility.

SCHEDULE ARRANGEMENT AND OPTIMIZATION OF THE FILE TRANSFER NETWORK

This project was designated as Meritorious of Mathematical Contest inModeling (MCM'94). We have been required tu solve a problem of findins thebest schedule of a file transfer network in order to niake the niaktispan the smallestone. Three situations with

Research on Real-Time High Reliable Network File Distribution Technology

The rapid development of Internet of Things(IoT)technology has made previously unavailable data available,and applications can take advantage of device data for people to visualize,explore,and build complex analyses.As the size of the network and the number of network users continue to increase,network requests tend to aggregate on a small number of network resources,which results in uneven load on network requests.Real-time,highly reliable network file distribution technology is of great importance in the Internet of Things.This paper studies real-time and highly reliable file distribution technology for large-scale networks.In response to this topic,this paper studies the current file distribution technology,proposes a file distribution model,and proposes a corresponding load balancing method based on the file distribution model.Experiments show that the system has achieved real-time and high reliability of network transmission.

Area and Timing Estimation in Register Files Using Neural Networks

The increase in issue width and instructions window size in modern processors demand an increase in the size of the register files, as well as an increase in the number of ports. Bigger register files implies an increase in power consumed by these units as well as longer access delays. Models that assist in estimating the size of the register file, and its timing early in the design cycle are critical to the time-budget allocated to a processor design and to its performance. In this work, we discuss a Radial Base Function (RBF) Artificial Neural Network (ANN) model for the prediction of time and area for standard cell register files designed using optimized Synopsys Design Ware components and an UMC130 nm library. The ANN model predictions were compared against experimental results (obtained using detailed simulation) and a nonlinear regression-based model, and it is observed that the ANN model is very accurate and outperformed the non-linear model in several statistical parameters. Using the trained ANN model, a parametric study was carried out to study the effect of the number of words in the file (D), the number of bit in one word (W) and the total number of Read and Write ports (P) on the latency and area of standard cell register files.

远程办公的协作方式——博科File Area Networks助力美国DRMP

Dyer，Riddle，Mills ＆ Precourt公司（DRMP）是美国著名的工程设计公司，为大型零售业、主题公园及政府等用户提供各种设计服务，在佛罗里达州、乔治亚州、北卡罗来纳州、南卡罗来纳州等多个地区设有19个分支办公室，员工超过500人。

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

基于d3.js的miRNA调控网络图绘制模块的设计

对d3.js类库绘图功能进行研究,设计并实现了miRNA调控网络图绘制模块。模块能够将存储在数据库中的miRNA及其调控的靶基因等相关数据读取出来并形成JSON文件,结合d3.js可视化库,绘制有向的网络图及权重网络图。通过网络图能够在有限空间内更加清晰地展示miRNA及其靶基因之间的关系。

基于区块链和IPFS 远程医疗的安全方法研究

COVID-19的大规模感染给人民生活造成了不便,比如就医难、医疗学术交流共享不便、医疗资源不平衡,从而衍生远程医疗。目前远程医疗数据仍然以集中式存储、互联网协议(Internet Protocol,IP)传输和签证机构(Certificate Authority,CA)颁发的证书为主,导致数据安全性低、网络通讯延迟和CA维护成本高。本文提出一种SMSSS-CA签名机制、智能合约和私有区块链解决CA维护成本高、医疗数据隐私泄露和安全性低的问题。此外,还提出命名数据网络(Named Data Network,NDN)集成星际文件系统(InterPlanetary File System,IPFS)、联盟区块链和智能合约,用来提高远程会诊时SMSSS-CA签名或验签时获取患者病例和共享会诊信息的响应速度和降低区块链传播开销。实验分别从NS3和NDNSIM两种实验环境对比,结果表明,该方法比现有方案更安全和共享更及时。

NFS over Lustre性能评测与分析

传统的网络文件系统难以满足高性能计算系统的I/O需求,基于对象存储的全局并行文件系统Lustre可以有效地解决传统文件系统在可扩展性、可用性和性能上存在的问题。该文介绍了Lustre文件系统的结构及其优势,对NFS over Lustre进行了性能测试,并将测试结果与Lustre文件系统、NFS网络文件系统及本地磁盘Ext3文件系统的性能进行了比较分析,给出了性能差异的原因,提出了一种可行的解决方法。

并行网络文件系统PNFS性能评测与分析

传统的网络文件系统难以满足高性能计算系统的I/O需求,并行网络文件系统——PNFS可以有效地解决传统网络文件系统在可扩展性、可用性和性能上存在的问题。首先对PNFS的体系结构进行了设计,实现了元数据服务器与存储服务器的分离,消除了由于集中服务器结构引发的I/O瓶颈问题。然后,对PNFS的原型系统进行了性能测试,并与相同环境下NFS的测试结果进行比较与分析,结果表明PNFS能够为客户端提供并行访问文件数据的能力,有着较高的I/O读写带宽和较低的访问延迟,同时实现了客户端I/O带宽与存储服务器规模之间的线性可扩展关系,能较好地满足高性能计算中的I/O需求。

基于网络药理学和分子对接探究祖卡木颗粒治疗支气管哮喘的作用机制

目的利用网络药理学和分子对接技术预测祖卡木颗粒(zukamu granules,ZKMG)治疗支气管哮喘的作用机制,并通过体内实验验证预测结果。方法数据库检索ZKMG活性化合物、靶点以及潜在的支气管哮喘靶点,用ZKMG以及支气管哮喘的交集靶点构建药物-成分-靶点网络,构建蛋白-蛋白相互作用网络图(PPI),采用基因本体论(GO)和京都基因和基因组百科全书(KEGG)进行富集分析,并通过网络药理学结果进行分子对接。腹腔注射卵清蛋白(ovalbumin,OVA)建立支气管哮喘模型,经体内实验验证ZKMG对支气管哮喘的作用机制。结果ZKMG中含有194种潜在活性成分,关键成分包括槲皮素、山奈酚、木犀草素等;其治疗支气管哮喘的关键靶点有176个,靶点主要涉及的生物学过程包括信号传导、凋亡过程的负调控、血管生成,通过TNF、血管内皮生长因子A(VEGFA)、MAPK等信号通路对支气管哮喘发挥治疗作用。结论分子对接结果显示关键成分与对应靶点具有较好的结合活性。体内实验验证,ZKMG可改善支气管哮喘肺部炎症情况以及氧化应激水平,能够下调p-p38、p-ERK1/2、p-IκBα以及NF-κBp65的表达。ZKMG对支气管哮喘具有潜在的治疗作用。

基于网络药理学和动物实验探究利咽方治疗慢性咽炎的机制

目的:基于网络药理学和动物实验,探讨利咽方治疗慢性咽炎的作用机制。方法:从TCMSP数据库中收集利咽方中的有效成分及作用靶点,从GeneCards数据库中收集慢性咽炎的相关靶点,然后通过Venny和STRING网站得到交集靶点,对交集靶点进行KEGG通路分析和Gene Ontology富集分析。并采用Tongg氨水直接喷雾法建立大鼠慢性咽炎模型对上述网络药理学的结果进行验证。结果:共收集利咽方中141个活性化合物和205个作用靶点以及1950个疾病靶点,取交集得到112个靶点,交集靶点的PPI网络分析得到IL-6、TNF和IL-β等重要靶点。KEGG通路显示,与利咽方治疗慢性咽炎关联度较高的通路为癌症相关通路。GO富集分析结果表明,利咽方的治疗作用生物过程主要涉及无机物反应;细胞组分主要涉及膜筏、细胞器外膜、囊腔等位置;分子功能主要涉及细胞因子受体结合、DNA转录因子结合等功能。动物实验结果显示,利咽方能有效降低血清中TNF-α、IL-1β、IL-6的水平,降低大鼠咽喉黏膜组织中TLR-4、NF-κBp65及磷酸化I-κBα蛋白水平。结论:通过网络药理学预测了利咽方的有效成分及其治疗慢性咽炎的潜在靶点,同时进行的动物实验表明利咽方可通过调节TLR-4/NF-κB信号通路,治疗氨水诱导的大鼠慢性咽炎。

基于pNFS的小文件间数据预读机制研究

随着信息技术飞速发展,小文件的数量越来越多.高效管理海量小文件、提供低延时的小文件访问服务,是并行网络文件系统的研究热点.小文件数据读取访问中,由于数据读取粒度小并且不同小文件之间的数据访问空间连续性差,难以发挥数据存储设备的大粒度顺序访问的性能优势,导致小文件的访问性能远远低于大文件的访问性能.在扩展只读目录授权机制框架下,提出一种小文件间数据预读机制,充分发挥了数据存储设备大粒度顺序访问性能的优势,大幅降低了小文件数据读取访问延迟.典型应用环境实测表明,对比pNFS系统,小文件数据顺序读取访问性能最高可提升8.92倍,随机读取性能最高可提升11.82倍.