Front-end Server Research Articles

Integration of the Barcelona Supercomputing Center for CMS computing: Towards large scale production

The CMS experiment is working to integrate an increasing number of High Performance Computing (HPC) resources into its distributed computing infrastructure. The case of the Barcelona Supercomputing Center (BSC) is particularly challenging as severe network restrictions prevent the use of CMS standard computing solutions. The CIEMAT CMS group has performed significant work in order to overcome these constraints and make BSC resources available to CMS. The developments include adapting the workload management tools, replicating the CMS software repository to BSC storage, providing an alternative access to detector conditions data, and setting up a service to transfer produced output data to a nearby storage facility. In this work, we discuss the current status of this integration activity and present recent developments, such as a front-end service to improve slot usage efficiency and an enhanced transfer service that supports the staging of input data for workflows at BSC. Moreover, significant efforts have been devoted to improving the scalability of the deployed solution, automating its operation, and simplifying the matchmaking of CMS workflows that are suitable for execution at BSC.

A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms

Most congestion control algorithms (CCAs) are designed for specific network environments. As such, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising a one-size-fits-all algorithm (which is a likely impossible task), we propose a system to dynamically switch between the most suitable CCAs for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure the stack to use the most suitable CCA for individual flows. We build a machine learning model to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically switching between CCAs. The framework also takes application requirements of performance into consideration to fine-tune the selection based on application-layer needs. Moreover, to reduce the overhead introduced by machine learning on individual front-end servers, we (optionally) implement the CCA selection process in the cloud, which allows the share of models and the selection among front-end servers. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. The results demonstrate the effectiveness of Antelope via dynamic adjusting the CCAs for individual flows. Specifically, Antelope achieves an average 16% improvement in throughput compared with BBR, and an average 19% improvement in throughput and 10% reduction in delay compared with CUBIC.

Robust Image Forgery Detection Over Online Social Network Shared Images

In servers and mobile users, an image is sent over the social network or exchanged. Due to the fact that it contains delicate personal information, the privacy of that data is crucial. A hacker may use social information about a person to discredit them if their image is hacked. Text-based encryption can be used in mobile cloud computing under the current architecture. There are various ways to store data securely utilizing mobile computing, including end-to-end encryption of data transmission and dynamic credential generation that only generates text. We'll be creating a brand-new wavelet watermarking technique called the discrete wavelet transform for use in real-time social network applications like Facebook, and this study suggests an efficient image forgery detection method that recognizes a manipulated foreground or background. Using this technique, images can be used and safely kept on servers. We categorize the image as either common or delicate, and we also enhance the project to include copy right implementation. Run copyright algorithms referred to as wavelet transform algorithms when employing sensitive techniques. After that, provide the receiver secure access to download the pictures. Using C#.NET as the front end and SQL SERVER as the back end, experimental results show the efficiency of current algorithms in real-time social network contexts and a comparison of their privacy rates

Corpus-Based Japanese Reading Teaching Database Cloud Service Model

With the widespread application of mobile Internet technology, big data, and artificial intelligence, the demand for computing resources in the whole society is growing rapidly. Database cloud service is an online managed high-availability database service built on a cloud computing platform. It can be used as a basic service of the cloud computing platform and has the characteristics of high service availability and high data reliability. The purpose of this paper is to design a corpus-based Japanese reading teaching database cloud service system and integrate a machine learning-based Structure Query Language (SQL) injection detection function for the system. The proposed work is as follows: (1) Design the basic framework of database cloud service. The module of cloud database service is introduced, and the realization logic of main system functions is analyzed. (2) A SQL injection classifier based on feature engineering and machine learning is designed for database cloud services, which can determine whether the input SQL command is an injection statement. The classifier is deployed between the database instance and the front-end server to integrate the SQL injection detection function into the cloud database instance. Then use the support vector machine algorithm to perform simulation experiments on the classifier model to compare the classification performance of the support vector machine algorithm when using different kernel functions. It is found that when the classification decision threshold is 0.5, the classifier using linear kernel function or radial basis kernel function has better classification performance for SQL sentences.

Pengembangan Aplikasi Tiga-Tingkat Menggunakan Metode Scrum pada Aplikasi Presensi Karyawan Glints Academy

The rapid development of technology requires a software development management system that can be adaptive in rapidly changing circumstances. Scrum is an agile method that has the advantage of being agile and adaptive. Glints Academy holds an Industry Project Exploration as the program to prepare students for the rapid development of technology and reduce the gap between the education field and industrial field by MBKM program from the Ministry of Education and Culture. This study aims to apply the Scrum method in a heterogeneous developer team and divergent ability backgrounds to build an application with three-level architecture. The developer team is college students who come from different regions spread across Indonesia with full online implementation. Scrum is used because it is advantageous to other methods in a relatively fast-changing environment and also provides good quality control. The sprints were carried out in two sprints with two weeks of development in each sprint. The application built is an employee attendance application with a three-tier architecture: client, server, and data. The client-tier application is a front-end server built using the React.js framework while the server-tier and data-tier are built-in back-end servers with the Node.js and Express.js frameworks. JWT (JSON Web Token) authentication determines access role to functions and resources available on the back-end server. The result is a web application that fulfills the entire product backlog determined by the product owner. The results of this research are this method can used to develop features enhancement in the middle of the application development process without affecting the main feature development and this method is effectively used for different team developer backgrounds and during its online development

Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

Optimization Research and Exploration of New Rural Cooperative Medical Information System

<span style="line-height: 114%; letter-spacing: 0.1pt; font-family: 'Times','serif'; font-size: 10pt; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-bidi-font-family: 'Times New Roman'; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The New Rural Cooperative Medical Care Information System (NRCMS) used a single relational database to store most of the data in the early days. The scheme is difficult to expand and has little concurrent ability, so it is not suitable to deal with semi-structured, Unstructured data. Then along came distributed technologies such as Hadoop, which had the advantage of using inexpensive machines to store and process data. But HADOOP was designed to distribute data equally among the nodes in the cluster for storage and processing, ignoring the differences in the storage and computing capabilities of the cluster nodes themselves, and the performance of individual nodes even affects the performance of the collation system, and it is master-slave node processing data, there are data can tamper, system terminal is too single and so on. The contribution of this paper lies in the overall structure and technical route of the new rural cooperative medical system, and the optimization method of data flow of heterogeneous nodes, which can effectively save the scale and cost of the cluster, with the help of blockchain technology to ensure the security and credibility of data, optimize the front-end cluster service architecture, support multi-channel and high-concurrent key information push mode, has a very good promotion.</span>

The first disk-based custodial storage for the ALICE experiment

We proposed a disk-based custodial storage as an alternative to tape for the ALICE experiment at CERN to preserve its raw data. The proposed storage system relies on Redundant Array of Independent Nodes (RAIN) layout – the implementation of erasure coding in the EOS storage suite, which is developed by CERN – for data protection and takes full advantage of high-density Just-Bunch-Of-Disks (JBOD) enclosures to maximize storage capacity as well as to achieve cost-effectiveness comparable to tape. The system we present provides 18 PB of total raw capacity from the 18 set of high-density JBOD enclosures attached to 9 EOS front-end servers. In order to balance between usable space and data protection, the system will stripe a file into 16 chunks on the 4-parity enabled RAIN layout configured on top of 18 containerized EOS FSTs. Although the reduction rate of available space increases up to 33:3% with this layout, the estimated annual data loss rate drops down to 8:6 × 10−5%. In this paper, we discuss the system architecture of the disk-based custodial storage, 4-parity RAIN layout, deployment automation, and the integration to the ALICE experiment in detail.

A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

With the rapid development of the Internet of Things (IoT) technology, diversified applications deploy extensive sensors to monitor objects, such as PM2.5 air quality monitoring. The sensors transmit data to the server periodically and continuously. However, a single server cannot provide efficient services for the ever-growing IoT devices and the data they generate. This study bases on the concept of symmetry of architecture and quantities in system design and explores the load balancing issue to improve performance. This study uses the Linux Virtual Server (LVS) and virtualization technology to deploy a virtual machine (VM) cluster. It consists of a front-end server, also a load balancer, to dispatch requests, and several back-end servers to provide services. These receive data from sensors and provide Web services for browsing real-time sensor data. The Hadoop Distributed File System (HDFS) and HBase are used to store the massive amount of received sensor data. Because load-balancing is critical for resource utilization, this study also proposes a new load distribution algorithm for VM-based server clusters that simultaneously provide multiple services, such as sensor services and Web service. It considers the aggregate load of all back-end servers on the same physical server that provides multiple services. It also considers the difference between physical machines and VMs. Algorithms such as those for LVS, which do not consider these factors, can cause load imbalance between physical servers. The experimental results demonstrate that the proposed system is fault tolerant, highly scalable, and offers high availability and high performance.

Automated Identification of Domestic Violence in Written Child Welfare Records: Leveraging Text Mining and Machine Learning to Enhance Social Work Research and Evaluation

Objective: Child welfare agencies often lack information about the front-end service needs of the families they serve. Thus, the current study tests the feasibility of text mining and machine learning procedures for identifying problems related to domestic violence documented in child welfare investigation summaries. Method: We labeled child welfare investigation summaries (N = 1,402) for the presence or absence of an active domestic violence service need. Labeled documents were then used to develop text mining and machine learning models and test their accuracy and reliability. Results: Machine learning models achieved greater than 90% accuracy when compared with human coders. Fleiss kappa estimates of coding reliability between the top-performing model and human reviewers exceeded .80, indicating that our model could support human reviewers to complete this coding task. Conclusion: Results provide strong evidence that text mining and machine learning procedures can be a cost-effective solution for extracting meaningful insights from text data. Although unsuitable for case-level predictive analytics, insights derived from these procedures can be particularly useful for investigating the prevalence, temporal trends, and geographic distribution of domestic violence-related needs in the child welfare system. These methods could substantially enhance the use of text data in social work research and evaluation.

Aggregated Model of Data Network for the Provision of Demand Response in Generation and Transmission Expansion Planning

A two-stage Internet data center (IDC)-considered framework in generation and transmission expansion planning (GTEP) (referred to as IGTEP) is proposed to make full use of IDCs’ spatial and chronological load regulation potentials for demand response. Stage one aggregates the data network (DN) to match a transmission network structure. First, IDC load is modeled based on the DN configuration. Second, the aggregated DN (ADN) is proposed to model DN with aggregated front-end servers (FSs) and data flows, where an intuitively accurate mathematical relationship among IDCs is developed. Third, the virtual power network (VPN) is proposed to provide a model for ADN with electrical variables, where the coupling of DN and power network (PN) is proposed to develop a linear aggregated IDC load model. Stage two coordinates IDC DR and GTEP, in which the load coupling among multiple IDCs is managed by VPN and the corresponding IDC load model is compatible with the conventional GTEP load. Simulation results verify the efficiency of the proposed VPN and IDC load model. Simulation results also show that IDC DR may reduce the total IGTEP cost significantly, which implies that DN can play a role equivalent to power lines and generators in power system planning and operations.

Integrated Query Data Communication using Arti Q System for Placement Cell Automation

Maintaining database of students in colleges is being an issue for placement cell since the data is maintained online and it is difficult to get access the system. Hence come up with idea of maintaining a database in offline method using a integrated ARTI-Q system which is using simple tools like Microsoft visual studio(version 2008) as front end and Microsoft SQL server(version 2005) as back end. The main aim of this project is to move packets from source to destination without an internet connection. Packets will only communicate through the assistance of base stat ions in which they are transmitted as Unicode transmission. The high transmission baud rate 115200 is set to increase the transmission. The program must aim to consolidate all divisions and roles across a college into a centralized database that can meet specific needs of all those departments. The information about campus interview will be sent to the placement officer and he will put information about the drive in the server. The system will check criteria from database for the list of individuals meeting the company criteria and the information will be sent to non-placed students.

AN INTEGRATED THREE-FLOW APPROACH FOR FRONT-END SERVICE COMPOSITION

End-User Service Composition (EUSC) aims to enable end-user programmers who are not professional developers develop applications by composing or aggregating existing web services. Despite the effort, studies have shown that, end-user programmers are not able to deal with the technical complexities involved in EUSC. One way to deal with this issue is Front-End Service Composition (FESC), which allows end-user programmers to compose web services at the presentation layer of an application by configuring user interface (UI) widgets that represent the back-end web services. However, there are not many studies on FESC and the existing ones suffer two main problems, namely, lack of control flow and/or application flow visualization, and they still require end-user programmers to have certain technical knowledge in the service composition process that these end-user programmers generally do not possess. Following that, this study proposes an integrated three-flow approach (application flow, control flow and data flow) to deal with the current limitations of FESC. The approach generates the GUI of web services automatically, thus allowing the UI of the application to be developed at the same time the required web services are assembled. The approach allows end-user programmers to explicitly configure the three different types of flows involved in service composition. A proof-of-concept prototype, QuickWSC, that incorporates the three-flow approach was developed. It adopts a side-by-side multiple-view design to support visual configuration of the three flows in an uncluttered yet synchronized manner that adhered to established design guidelines. A user evaluation study was conducted on QuickWSC. The results show that it is easy to compose web services by explicitly specifying the three flows, that the configurations of the three flows integrated in the two views helps in composing application from web services, and that no technical knowledge is required to use QuickWSC

Img-Shelter : Privacy Protection of Images In Online Social Networks Using Watermarking Scheme

Social networking sites are very useful in sharing information, making friends and keeping in touch with old friends. It is an online service, platform, or site that focuses on facilitating the building of social networks and social elation among peoples for sharing interests, activities, backgrounds, or real-life connections. But with the increasing demand of social networking sites (SNS) privacy and security concern have also increased. Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users’ privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. In this paper, categorize the picture as sensitive or normal. If it is sensitive means, perform copyrights algorithms. Then provide the permission to the receiver end for download the images in secure manner. Experimental result can be shows that in real time environments using C#.NET as front end and SQL SERVER as back end and comparative study of existing algorithms based on computational time and privacy rate.

Design and Implementation of Web-based Information System for Region-Specific Synthesised Integrated Farming Models in India

This application note addresses the design and implementation of a web-based RDBMS menu driven information system to provide the exhaustive information on existing farming systems prevailing in different Agro Climatic Zones in India (14) (29 centres across the country, India). This information system also recommends the required technological intervention to improve the existing farming models with the help of domain experts. This information system is developed using the Visual Studio development software. It uses C# and AP.NET as front end and SQL server as back end [1, 2].

Quality of Service on Link Aggregation Network Virtualization for Docker Containers

Docker engine is an extremely powerful tool for PaaS platform of cloud computing. It gives benefits for large-scale of internet services. Web service is basic service for everyone who requires to access internet that web infrastructure must has scalability with load-balance web server called reverse proxy. The key answers for a large-scale web must have multiple web servers working together with high speed bandwidth. Moreover, multiple clusters can find in the same data center there are required to assign priority and quality of each cluster service. We investigate load-balance assign link aggregation with network QoS by using pipework script and traffic control tool in frontend reverse proxy server on each cluster. Our research evaluates scenario of network QoS ratios which include 50/50, 60/40, 70/30 and 80/20. We compare network bandwidth between both web reverse proxy clusters. The results present our designed and implementation tool not only can control network QoS on each web reverse proxy cluster in all load-balance link aggregation modes which include round-robin, XOR and ALB but also those of clusters can access multiple network interface. In experiment, average network bandwidths in all QoS cases are around 200 MB per second for link aggregation of 2 gigabit interface.

Monitoring LHCb Trigger developments using nightly integration tests and a new interactive web UI

The LHCb Performance Regression (LHCbPR) framework allows for periodic software testing to be performed in a reproducible manner. LHCbPR provides a JavaScript based web front-end service, built atop industry standard tools such as AngularJS, Bootstrap and Django. This framework records the evolution of tests over time allowing for this data to be extracted for end-user analysis. The LHCbPR framework has been expanded to integrate the nightly testing and profiling. These developments allow for key performance metrics within the Trigger software to be monitored over time. Additionally, tests of the full physics reconstruction have been integrated into LHCbPR. These allow for tracking the effect that optimization work has on physics reconstruction performance. This presentation will describe the integration of these tests into LHCbPR as well as describing the structure and new features developed for the frontend web service.

Internet of Malicious Things: Correlating Active and Passive Measurements for Inferring and Characterizing Internet-Scale Unsolicited IoT Devices

Advancements computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed in the wild is still its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed local IoT realms. To support largescale empirical data analytics the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, addition to contributing to IoT cyber security situational awareness.

Enhancement of the forest road network accessibility using Information Systems

Accessibility of the forest road network is one of the most important factors in the sustainable management of forests. This study presents the development of an information system which enhances the accessibility of the forest road network. This project uses HTML5 and CSS3 for the frontend and WAMP Server with MySQL database as backend. End users will be able to receive the necessary information and coordinates via notifications on their Android smartphone using an application. This Android application uses the Google Maps app to display the route that should be followed. Forwarding information to users is operated through Firebase Cloud Messaging. Last but not least, it should be highlighted that free and open source software was used to implement the aforementioned system.

Understanding and improving JVM GC work stealing at the data center scale

Garbage collection (GC) is a critical part of performance in managed run-time systems such as the OpenJDK Java Virtual Machine (JVM). With a large number of latency sensitive applications written in Java the performance of the JVM is essential. Java application servers run in data centers on a large number of multi-core servers, thus load balancing in multi-threaded GC phases is critical. Dynamic load balancing in the JVM GC is achieved through work stealing, a well known and effective method to balance tasks across threads. This paper analyzes the JVM work stealing behaviour, and introduces a novel work stealing technique that improves performance, GC CPU utilization, scalability, and reduces the cost of jobs running on Google’s data-centers. We analyze both the DaCapo benchmark suite as well as Google’s data-center jobs. Our results show that the Gmail front-end server shows a 15-20% GC CPU reduction, and a 5% CPU performance improvement. Our analysis of a sample of ~59K jobs shows that GC CPU utilization improves by 38% geomean, 12% weighted geomean. GC pause time improves by 16% geomean, 20% weighted geomean. Full GC pause time improves by 34% geomean, 12% weighted geomean.