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Journal articles on the topic 'Web server clusters'

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Published: 7 June 2025
Last updated: 11 July 2025

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1

Ramana, Kadiyala, and M. Ponnavaikko. "AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2083. http://dx.doi.org/10.11591/ijece.v9i3.pp2083-2093.

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With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster-based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster.
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Kadiyala, Ramana, and Ponnavaikko M. "AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2083–93. https://doi.org/10.11591/ijece.v9i3.pp2083-2093.

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With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster.  
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3

Wei, Li Feng, Jian Wei Ji, and Li Qiang Zhao. "The Research and Design of Two-Level Load Balancer Based on Web Server Cluster." Advanced Materials Research 282-283 (July 2011): 765–69. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.765.

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At present, the extendible Web server clusters are the important framers for the high performance network servers. The architecture based on load balancer is often used on web server clusters. The load balancer is the main bottleneck in improving performance of cluster. For it is hard to complete the task of assigning the requests from the client while collecting the real load information of the web servers efficiently. In this paper, we propose a model of two levels load balancer for web cluster to resolve the contradiction. When web requests are arrive, the first-level balancer continuously assigned them by to the second-level which consist of some load balancers connect to real web servers and receive real-time load information of them. Round robin algorithm is used at the first-level load balancer to schedule the client’s requests in order to shorten the response time. The proposed algorithm in our model is run at the second-level load balancers to schedule the requests to real web servers according to the real load information. At last, a simple test is carried out to make sure the performance of the model. The result of the test shows that the model has made better load balancing.
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Chuchuen, Yanapat, Kritwara Rattanaopas, and Sarapee Chunkaew. "Quality of Service on Link Aggregation Network Virtualization for Docker Containers." Applied Mechanics and Materials 886 (January 2019): 227–32. http://dx.doi.org/10.4028/www.scientific.net/amm.886.227.

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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.
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Han, Li, Xin Cao, and Ya Na Zhang. "Research on Request Handling Mechanism of Web Server Clusters." Advanced Materials Research 1022 (August 2014): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amr.1022.261.

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This paper analyzes the factors that affect the performance of Web architecture cluster system characteristics studied two typical Web server cluster architecture, proposed a new distributed Web server cluster architecture, and in-depth analysis of its structural features, this architecture is verified in terms of scalability and reliability has advantages.
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Chiang, Mei-Ling, and Tsung-Te Hou. "A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services." Symmetry 12, no. 12 (2020): 1942. http://dx.doi.org/10.3390/sym12121942.

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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.
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Santana, Carlos, Julius C. B. Leite, and Daniel Mossé. "Power management by load forecasting in web server clusters." Cluster Computing 14, no. 4 (2011): 471–81. http://dx.doi.org/10.1007/s10586-011-0187-2.

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Tseng, Chun-Wei, and Chu-Sing Yang. "System support for web hosting services on server clusters." Computers & Electrical Engineering 33, no. 3 (2007): 208–20. http://dx.doi.org/10.1016/j.compeleceng.2003.02.001.

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Satake, Shinsuke, Hiroshi Inai, and Tsuyoshi Arai. "Effectiveness of Server Load Estimation by using Request File Size for Web Server Clusters." IEEJ Transactions on Electronics, Information and Systems 128, no. 10 (2008): 1543–49. http://dx.doi.org/10.1541/ieejeiss.128.1543.

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Satake, Shinsuke, Hiroshi Inai, and Tsuyoshi Arai. "Effectiveness of server load estimation by using requested file size for web server clusters." Electronics and Communications in Japan 94, no. 2 (2011): 59–66. http://dx.doi.org/10.1002/ecj.10274.

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11

Zhu, Huican, Ben Smith, and Tao Yang. "Hierarchical resource management for Web server clusters with dynamic content." ACM SIGMETRICS Performance Evaluation Review 27, no. 1 (1999): 198–99. http://dx.doi.org/10.1145/301464.301567.

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Bertini, Luciano, Julius C. B. Leite, and Daniel Mossé. "Power optimization for dynamic configuration in heterogeneous web server clusters." Journal of Systems and Software 83, no. 4 (2010): 585–98. http://dx.doi.org/10.1016/j.jss.2009.10.040.

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13

Tang, Cheng-Jen, Miau-Ru Dai, Chi-Cheng Chuang, and Yu-Sheng Chiu. "Energy management for the homogeneous server clusters offering web services." Energy Efficiency 9, no. 5 (2015): 1115–44. http://dx.doi.org/10.1007/s12053-015-9412-9.

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14

Moreno-Vozmediano, Rafael, Ruben S. Montero, and Ignacio M. Llorente. "Elastic management of web server clusters on distributed virtual infrastructures." Concurrency and Computation: Practice and Experience 23, no. 13 (2011): 1474–90. http://dx.doi.org/10.1002/cpe.1709.

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15

Chen, Shanyu, Xiaoyu He, Ruilin Li, Xiaohong Duan, and Beifang Niu. "HotSpot3D web server: an integrated resource for mutation analysis in protein 3D structures." Bioinformatics 36, no. 12 (2020): 3944–46. http://dx.doi.org/10.1093/bioinformatics/btaa258.

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Abstract Motivation HotSpot3D is a widely used software for identifying mutation hotspots on the 3D structures of proteins. To further assist users, we developed a new HotSpot3D web server to make this software more versatile, convenient and interactive. Results The HotSpot3D web server performs data pre-processing, clustering, visualization and log-viewing on one stop. Users can interactively explore each cluster and easily re-visualize the mutational clusters within browsers. We also provide a database that allows users to search and visualize proximal mutations from 33 cancers in the Cancer Genome Atlas. Availability and implementation http://niulab.scgrid.cn/HotSpot3D/. Supplementary information Supplementary data are available at Bioinformatics online.
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Zierep, Paul F., Adriana T. Ceci, Ilia Dobrusin, Sinclair C. Rockwell-Kollmann, and Stefan Günther. "SeMPI 2.0—A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases." Metabolites 11, no. 1 (2020): 13. http://dx.doi.org/10.3390/metabo11010013.

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Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening.
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Zhang, Lin, Li Xiao-Ping, and Su Yuan. "A content-based dynamic load-balancing algorithm for heterogeneous web server cluster." Computer Science and Information Systems 7, no. 1 (2010): 153–62. http://dx.doi.org/10.2298/csis1001153z.

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According to the different requests of Web and the heterogeneity of Web server, the paper presents a content-based loadbalancing algorithm. The mechanism of this algorithm is that a corresponding request is allocated to the server with the lowest load according to the degree of effects on the server and a combination of load state of server. Besides, apply a method of random distributing base-probability to assign each request to an appropriate server in terms of their weight. All the parameters that will be used in the algorithm can be acquired by simulated test. Experimental results suggest that this algorithm can balance the load of web server clusters effectively, make full use of the existing source of software and hardware, highly improve the server's performance, and even make the best use of the web server.
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Liu, Yan-Lin, Cheng-Ting Shih, Yuan-Jen Chang, Shu-Jun Chang, and Jay Wu. "Performance Enhancement of a Web-Based Picture Archiving and Communication System Using Commercial Off-the-Shelf Server Clusters." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/657417.

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The rapid development of picture archiving and communication systems (PACSs) thoroughly changes the way of medical informatics communication and management. However, as the scale of a hospital’s operations increases, the large amount of digital images transferred in the network inevitably decreases system efficiency. In this study, a server cluster consisting of two server nodes was constructed. Network load balancing (NLB), distributed file system (DFS), and structured query language (SQL) duplication services were installed. A total of 1 to 16 workstations were used to transfer computed radiography (CR), computed tomography (CT), and magnetic resonance (MR) images simultaneously to simulate the clinical situation. The average transmission rate (ATR) was analyzed between the cluster and noncluster servers. In the download scenario, the ATRs of CR, CT, and MR images increased by 44.3%, 56.6%, and 100.9%, respectively, when using the server cluster, whereas the ATRs increased by 23.0%, 39.2%, and 24.9% in the upload scenario. In the mix scenario, the transmission performance increased by 45.2% when using eight computer units. The fault tolerance mechanisms of the server cluster maintained the system availability and image integrity. The server cluster can improve the transmission efficiency while maintaining high reliability and continuous availability in a healthcare environment.
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Tang, Cheng-Jen, and Miau-Ru Dai. "Modeling and Analysis of Queueing-Based Vary-On/Vary-Off Schemes for Server Clusters." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/594264.

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A cloud system usually consists of a lot of server clusters handling various applications. To satisfy the increasing demands, especially for the front-end web applications, the computing capacity of a cloud system is often allocated for the peak demand. Such installation causes resource underutilization during the off-peak hours. Vary-On/Vary-Off (VOVO) schemes concentrate workloads on some servers instead of distributing them across all servers in a cluster to reduce idle energy waste. Recent VOVO schemes adopt queueing theory to model the arrival process and the service process for determining the number of powered-on servers. For the arrival process, Poisson process can be safely assumed in web services due to the large number of independent sources. On the other hand, the heavy-tailed distribution of service times is observed in real web systems. However, there are no exact solutions to determine the performance forM/heavy-tailed/mqueues. Therefore, this paper presents two queueing-based sizing approximations for Poisson and non-Poisson governed service processes. The simulation results of the proposed approximations are analyzed and evaluated by comparing with the simulated system running at full capacity. This relative measurement indicates that the Pareto distributed service process may be adequately modeled by memoryless queues when VOVO schemes are adopted.
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Franzén, Oscar, and Johan L. M. Björkegren. "alona: a web server for single-cell RNA-seq analysis." Bioinformatics 36, no. 12 (2020): 3910–12. http://dx.doi.org/10.1093/bioinformatics/btaa269.

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Abstract Summary Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes. Availability and implementation The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/. Supplementary information Supplementary data are available at Bioinformatics online.
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Romero-Zaliz, R., C. del Val, J. P. Cobb, and I. Zwir. "Onto-CC: a web server for identifying Gene Ontology conceptual clusters." Nucleic Acids Research 36, Web Server (2008): W352—W357. http://dx.doi.org/10.1093/nar/gkn323.

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Bertini, Luciano, Julius C. B. Leite, and Daniel Mossé. "Power and performance control of soft real-time web server clusters." Information Processing Letters 110, no. 17 (2010): 767–73. http://dx.doi.org/10.1016/j.ipl.2010.06.013.

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Turcan, Alistair, Anna Zivkovic, Dylan Thompson, Lorraine Wong, Lauren Johnson, and Filip Jagodzinski. "CGRAP: A Web Server for Coarse-Grained Rigidity Analysis of Proteins." Symmetry 13, no. 12 (2021): 2401. http://dx.doi.org/10.3390/sym13122401.

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Elucidating protein rigidity offers insights about protein conformational changes. An understanding of protein motion can help speed drug development, and provide general insights into the dynamic behaviors of biomolecules. Existing rigidity analysis techniques employ fine-grained, all-atom modeling, which has a costly run-time, particularly for proteins made up of more than 500 residues. In this work, we introduce coarse-grained rigidity analysis, and showcase that it provides flexibility information about a protein that is similar in accuracy to an all-atom modeling approach. We assess the accuracy of the coarse-grained method relative to an all-atom approach via a comparison metric that reasons about the largest rigid clusters of the two methods. The apparent symmetry between the all-atom and coarse-grained methods yields very similar results, but the coarse-grained method routinely exhibits 40% reduced run-times. The CGRAP web server outputs rigid cluster information, and provides data visualization capabilities, including a interactive protein visualizer.
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Beresnev, A., A. Semenov, and E. Panidi. "HEXAGONAL GRIDS APPLIED TO CLUSTERING LOCATIONS IN WEB MAPS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 435–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-435-2022.

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Abstract. One of the popular ways to clutter reduction techniques is to combine neighboring points into one marker that somehow shows that it contains multiple entities – this way is called clustering. In this paper, we present a JavaScript library to define optimal size of clusters and render them. Moreover, markers have to present heterogeneous data inside of clusters.The presented library relies on server side clustering, no matter if is it a real-time clustering or a static bunch of hexagonal grids. For the library, a server provides the bunch of grid layers by different cell sizes – from smaller to larger. The library relies on data fetching provided by external library, such as Mapbox/Maplibre, so it can work with both GeoJSON and vector tiles. Using the HTML Canvas to render the marker allows to full customizing the marker image: manage the colors and proportions of cluster fractions and the size.
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Ferruz, Noelia, Steffen Schmidt, and Birte Höcker. "ProteinTools: a toolkit to analyze protein structures." Nucleic Acids Research 49, W1 (2021): W559—W566. http://dx.doi.org/10.1093/nar/gkab375.

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Abstract The experimental characterization and computational prediction of protein structures has become increasingly rapid and precise. However, the analysis of protein structures often requires researchers to use several software packages or web servers, which complicates matters. To provide long-established structural analyses in a modern, easy-to-use interface, we implemented ProteinTools, a web server toolkit for protein structure analysis. ProteinTools gathers four applications so far, namely the identification of hydrophobic clusters, hydrogen bond networks, salt bridges, and contact maps. In all cases, the input data is a PDB identifier or an uploaded structure, whereas the output is an interactive dynamic web interface. Thanks to the modular nature of ProteinTools, the addition of new applications will become an easy task. Given the current need to have these tools in a single, fast, and interpretable interface, we believe that ProteinTools will become an essential toolkit for the wider protein research community. The web server is available at https://proteintools.uni-bayreuth.de.
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Theodosiou, Athina, and Vasilis J. Promponas. "LaTcOm: a web server for visualizing rare codon clusters in coding sequences." Bioinformatics 28, no. 4 (2011): 591–92. http://dx.doi.org/10.1093/bioinformatics/btr706.

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27

Pascal Andreu, Victòria, Jorge Roel-Touris, Dylan Dodd, Michael A. Fischbach, and Marnix H. Medema. "The gutSMASH web server: automated identification of primary metabolic gene clusters from the gut microbiota." Nucleic Acids Research 49, W1 (2021): W263—W270. http://dx.doi.org/10.1093/nar/gkab353.

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Abstract Anaerobic bacteria from the human microbiome produce a wide array of molecules at high concentrations that can directly or indirectly affect the host. The production of these molecules, mostly derived from their primary metabolism, is frequently encoded in metabolic gene clusters (MGCs). However, despite the importance of microbiome-derived primary metabolites, no tool existed to predict the gene clusters responsible for their production. For this reason, we recently introduced gutSMASH. gutSMASH can predict 41 different known pathways, including MGCs involved in bioenergetics, but also putative ones that are candidates for novel pathway discovery. To make the tool more user-friendly and accessible, we here present the gutSMASH web server, hosted at https://gutsmash.bioinformatics.nl/. The user can either input the GenBank assembly accession or upload a genome file in FASTA or GenBank format. Optionally, the user can enable additional analyses to obtain further insights into the predicted MGCs. An interactive HTML output (viewable online or downloadable for offline use) provides a user-friendly way to browse functional gene annotations and sequence comparisons with reference gene clusters as well as gene clusters predicted in other genomes. Thus, this web server provides the community with a streamlined and user-friendly interface to analyze the metabolic potential of gut microbiomes.
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GERNER-SMIDT, PETER, JENNIFER KINCAID, KRISTY KUBOTA, et al. "Molecular Surveillance of Shiga Toxigenic Escherichia coli O157 by PulseNet USA." Journal of Food Protection 68, no. 9 (2005): 1926–31. http://dx.doi.org/10.4315/0362-028x-68.9.1926.

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PulseNet USA is the national molecular subtyping network system for foodborne disease surveillance. Sixty-four public health and food regulatory laboratories participate in PulseNet USA and routinely perform pulsed-field gel electrophoresis of Shiga toxigenic Escherichia coli isolated from humans, food, water, and the environment on a real-time basis. Clusters of infection are detected in three ways within this system: through rapidly alerting the participants in the electronic communication forum, the PulseNet Web conference; through cluster analysis by the database administrators at the coordinating center at the Centers for Disease Control and Prevention of the patterns uploaded to the central server by the participants; and by matching profiles of strains from nonhuman sources with recent human uploads to the national server. The strengths, limitations, and scope for future improvements of PulseNet are discussed with examples from 2002. In that year, notices of 30 clusters of Shiga toxigenic E. coli O157 infections were posted on the Web conference, 26 of which represented local outbreaks, whereas four were multistate outbreaks. Another 27 clusters were detected by central cluster detection performed at the Centers for Disease Control and Prevention, of which five represented common source outbreaks confirmed after finding an isolate with the outbreak pattern in the implicated food. Ten food isolates submitted without suspicion of an association to human disease matched human isolates in the database, and an epidemiologic link to human cases was established for six of them.
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Shan, Zhiguang, Chuang Lin, Dan C. Marinescu, and Yang Yang. "Modeling and performance analysis of QoS-aware load balancing of Web-server clusters." Computer Networks 40, no. 2 (2002): 235–56. http://dx.doi.org/10.1016/s1389-1286(02)00253-0.

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Xu, Ling, Zhaobin Dong, Lu Fang, et al. "OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species." Nucleic Acids Research 47, W1 (2019): W52—W58. http://dx.doi.org/10.1093/nar/gkz333.

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Abstract OrthoVenn is a powerful web platform for the comparison and analysis of whole-genome orthologous clusters. Here we present an updated version, OrthoVenn2, which provides new features that facilitate the comparative analysis of orthologous clusters among up to 12 species. Additionally, this update offers improvements to data visualization and interpretation, including an occurrence pattern table for interrogating the overlap of each orthologous group for the queried species. Within the occurrence table, the functional annotations and summaries of the disjunctions and intersections of clusters between the chosen species can be displayed through an interactive Venn diagram. To facilitate a broader range of comparisons, a larger number of species, including vertebrates, metazoa, protists, fungi, plants and bacteria, have been added in OrthoVenn2. Finally, a stand-alone version is available to perform large dataset comparisons and to visualize results locally without limitation of species number. In summary, OrthoVenn2 is an efficient and user-friendly web server freely accessible at https://orthovenn2.bioinfotoolkits.net.
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Li, Hai Sheng, and Hong Yan Shi. "An Adaptive Load Balancing Algorithm Based on Discrete Uniform Distribution." Advanced Materials Research 108-111 (May 2010): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1392.

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Centralized cluster system is widely used by many web sites to increase service availability and balancing workload among multiple servers. In order to reduce the possibility which balancer becomes performance bottleneck in clusters, a dynamic adaptive weighted load-balanced algorithm is presented in this paper. The objective of the algorithm is that every working server sends a corresponding ask signals at different rates to balancer based on the discrete uniform distribution principle and the balancer assigning user requests according to an ask queue which consists of these signals and achieves the dynamic negative feedback on workload. Experiment results show that the algorithm is better than the static algorithm, as compared with the dynamic algorithm, it can also reduce the burden on load balancer.
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Yuggo, Afrianto, Sukoco Heru, and Wahjuni Sri. "Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1402–8. https://doi.org/10.12928/TELKOMNIKA.v16i3.5601.

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Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%.
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Benova, Lenka, and Ladislav Hudec. "Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs." Sensors 24, no. 3 (2024): 746. http://dx.doi.org/10.3390/s24030746.

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In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures.
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Kuzu, Omer Faruk, and Fahri Saatcioglu. "Abstract 7416: GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data." Cancer Research 84, no. 6_Supplement (2024): 7416. http://dx.doi.org/10.1158/1538-7445.am2024-7416.

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Abstract Identification of genotype-phenotype relationships is of utmost importance and a core effort in biology. Recent developments in efficient and precise gene targeting approaches coupled to omics methods have significantly improved deciphering of molecular interactions and relationships. However, many single gene perturbations can affect the expression of hundreds of other genes and analysis of the resulting omics-derived gene lists currently remains a significant challenge. Here we present Perturb-Seq based Gene Set Analyzer (GeneSetR), a user-friendly web-server that can analyze user-defined gene lists based on the data from a recently published genome-wide Perturb-Seq study, which targeted 9,866 genes with 11,258 sgRNAs in the K562 cell line. Through this tool, users can cluster gene lists following dimensionality reduction by various algorithms, undertake network analysis from RNA sequencing data, identify key nodes among the submitted genes, perform gene signature analyses, and generate heatmaps based on perturbation or gene expression data. GeneSetR enables researchers to readily identify gene clusters associated with specific phenotypes or biological processes, providing insights into the potential functional roles of these clusters and the role of single genes in them. With robust analysis capabilities, GeneSetR is a powerful resource to facilitate the exploration of genotype-phenotype relationships. Citation Format: Omer Faruk Kuzu, Fahri Saatcioglu. GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7416.
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35

Law, Philip J., Clotilde Claudel-Renard, Fourie Joubert, Abraham I. Louw, and Dave K. Berger. "MADIBA: A web server toolkit for biological interpretation of Plasmodium and plant gene clusters." BMC Genomics 9, no. 1 (2008): 105. http://dx.doi.org/10.1186/1471-2164-9-105.

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Lavanya, V., M. Saravanan, and E. P. Sudhakar. "Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment." Webology 17, no. 2 (2020): 735–45. http://dx.doi.org/10.14704/web/v17i2/web17064.

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In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.
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Sun, Tao, Mengci Li, Xiangtian Yu, et al. "3MCor: an integrative web server for metabolome–microbiome-metadata correlation analysis." Bioinformatics 38, no. 5 (2021): 1378–84. http://dx.doi.org/10.1093/bioinformatics/btab818.

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Abstract Motivation The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies. Availabilityand implementation 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. Supplementary information Supplementary data are available at Bioinformatics online.
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Carbone, Jacopo, Alessia Ghidini, Antonio Romano, Luca Gentilucci, and Francesco Musiani. "PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results." Molecules 27, no. 20 (2022): 6884. http://dx.doi.org/10.3390/molecules27206884.

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Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.
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Satake, Shinsuke, and Hiroshi Inai. "Special issue on Internet architecture technology papers: A nonprobabilistic server selection method based on periodically obtained load information for Web server clusters." Electronics and Communications in Japan (Part I: Communications) 89, no. 10 (2006): 1–12. http://dx.doi.org/10.1002/ecja.20309.

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40

Su, Gang, Binchen Mao, and Jin Wang. "MACO: A Gapped-Alignment Scoring Tool for Comparing Transcription Factor Binding Sites." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 4 (2006): 307–10. https://doi.org/10.3233/isb-00243.

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We have implemented a novel gapped-alignment algorithm to compare Position Frequency Matrices (PFMs) for Transcription Factor Binding Sites. The application compares an input PFM with those collected from public databases and outputs similarity scores, sequence alignments and related PFM clusters. MACO is freely accessible on a web server located at www.nicemice.cn/bioinfo/MACO. Source code is distributed upon request to the authors.
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Wang, Yi, Devin Coleman-Derr, Guoping Chen, and Yong Q. Gu. "OrthoVenn: a web server for genome wide comparison and annotation of orthologous clusters across multiple species." Nucleic Acids Research 43, W1 (2015): W78—W84. http://dx.doi.org/10.1093/nar/gkv487.

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Papatsenko, Dmitri. "ClusterDraw web server: a tool to identify and visualize clusters of binding motifs for transcription factors." Bioinformatics 23, no. 8 (2007): 1032–34. http://dx.doi.org/10.1093/bioinformatics/btm047.

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Blin, Kai, Simon Shaw, Alexander M. Kloosterman, et al. "antiSMASH 6.0: improving cluster detection and comparison capabilities." Nucleic Acids Research 49, W1 (2021): W29—W35. http://dx.doi.org/10.1093/nar/gkab335.

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Abstract Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell—antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.
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NASRAOUI, OLFA, and RAGHU KRISHNAPURAM. "AN EVOLUTIONARY APPROACH TO MINING ROBUST MULTI-RESOLUTION WEB PROFILES AND CONTEXT SENSITIVE URL ASSOCIATIONS." International Journal of Computational Intelligence and Applications 02, no. 03 (2002): 339–48. http://dx.doi.org/10.1142/s1469026802000646.

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We present a technique for simultaneously mining Web navigation patterns and maximally frequent context-sensitive itemsets (URL associations) from the historic user access data stored in Web server logs. A new hierarchical clustering technique that exploits the symbiosis between clusters in feature space and genetic biological niches in nature, called Hierarchical Unsupervised Niche Clustering (H-UNC) is presented. We use H-UNC as part of a complete system of knowledge discovery in Web usage data. Our approach does not necessitate fixing the number of clusters in advance, is insensitive to initialization, can handle noisy data, general non-differentiable similarity measures, and automatically provides profiles at multiple resolution levels. Our experiments show that our algorithm is not only capable of extracting meaningful user profiles on real Web sites, but also discovers associations between distinct URL pages on a site, with no additional cost. Unlike content based association methods, our approach discovers associations between different Web pages based only on the user access patterns and not on the page content. Also, unlike traditional context-blind association discovery methods, H-UNC discovers context-sensitive associations which are only meaningful within a limited context/user profile.
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Stahlecker, Jason, Erik Mingyar, Nadine Ziemert, and Mehmet Direnç Mungan. "SYN-View: A Phylogeny-Based Synteny Exploration Tool for the Identification of Gene Clusters Linked to Antibiotic Resistance." Molecules 26, no. 1 (2020): 144. http://dx.doi.org/10.3390/molecules26010144.

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The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline.
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Modi, Vivek, and Roland L. Dunbrack. "Kincore: a web resource for structural classification of protein kinases and their inhibitors." Nucleic Acids Research 50, no. D1 (2021): D654—D664. http://dx.doi.org/10.1093/nar/gkab920.

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Abstract The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore.
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Mao, Shunfu, Yue Zhang, Georg Seelig, and Sreeram Kannan. "CellMeSH: probabilistic cell-type identification using indexed literature." Bioinformatics 38, no. 5 (2021): 1393–402. http://dx.doi.org/10.1093/bioinformatics/btab834.

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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledge. Results Here, we introduce CellMeSH—a new automated approach to identifying cell types for clusters based on prior literature. CellMeSH combines a database of gene–cell-type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell-type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and is easily updated with new data. The probabilistic query method enables reliable information retrieval even though the gene–cell-type associations extracted from the literature are noisy. CellMeSH is also able to optionally utilize prior knowledge about tissues or cells for further annotation improvement. CellMeSH achieves top-one and top-three accuracies on a number of mouse and human datasets that are consistently better than existing approaches. Availability and implementation Web server at https://uncurl.cs.washington.edu/db_query and API at https://github.com/shunfumao/cellmesh. Supplementary information Supplementary data are available at Bioinformatics online.
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Magomedov, Shamil, and Artem Lebedev. "Protected Network Architecture for Ensuring Consistency of Medical Data through Validation of User Behavior and DICOM Archive Integrity." Applied Sciences 11, no. 5 (2021): 2072. http://dx.doi.org/10.3390/app11052072.

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The problem of consistency of medical data in Hospital Data Management Systems is considered in the context of correctness of medical images stored in a PACS (Picture Archiving and Communication System) and legality of actions authorized users perform when accessing MIS (Medical Information System) facilities via web interfaces. The purpose of the study is to develop a SIEM-like (Security Information and Event Management) architecture for offline analysis of DICOM (Digital Imaging and Communications in Medicine) archive integrity and users’ activity. To achieve amenable accuracy when validating DICOM archive integrity, two aspects are taken into account: correctness of periodicity of the incoming data stream and correctness of the image data (time series) itself for the considered modality. Validation of users’ activity assumes application of model-driven approaches using state-of-the-art machine learning methods. This paper proposes a network architecture with guard clusters to protect sensitive components like the DICOM archive and application server of the MIS. New server roles were designed to perform traffic interception, data analysis and alert management without reconfiguration of production software components. The cluster architecture allows the analysis of incoming big data streams with high availability, providing horizontal scalability and fault tolerance. To minimize possible harm from spurious DICOM files the approach should be considered as an addition to other securing techniques like watermarking, encrypting and testing data conformance with a standard.
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Faro, Alberto, Daniela Giordano, and Corrado Santorno. "Link-based Shaping of Hypermedia Webs Assisted by a Neural Agent." JUCS - Journal of Universal Computer Science 4, no. (7) (1998): 630–51. https://doi.org/10.3217/jucs-004-07-0630.

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The paper proposes a neural agent that performs self-organizing classification to assist in searching and contributing to webs of documents and in the process of document reuse. By applying the Kohonen self-organizing feature map (SOFM) algorithm to patterns of influence links among documents it is possible to originate clusters of documents that help infer the aspects that such documents implicitly share. The approach complements search techniques based on semantic indexes. The resulting classification is sensitive to the multiple aspects of a document that may belong to multiple classes with a varying degree and allows for treating effectively items that typically have a limited life span, either because they are means to the collaborative production of a more complex item, or because they belong to fast evolving domains. The method has been implemented by Lotus Notes Domino Web server for a case-based application in the domain of information systems design.
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Granata, Daniele, Luca Ponzoni, Cristian Micheletti, and Vincenzo Carnevale. "Patterns of coevolving amino acids unveil structural and dynamical domains." Proceedings of the National Academy of Sciences 114, no. 50 (2017): E10612—E10621. http://dx.doi.org/10.1073/pnas.1712021114.

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Patterns of interacting amino acids are so preserved within protein families that the sole analysis of evolutionary comutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general coevolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into a few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: They are spatially separated but individually compact. They have a direct functional bearing too, as shown for various reference cases. We conclude that even large-scale structural and functionally related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (spectrus.sissa.it/spectrus-evo_webserver).
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