Performance Of Web Services Research Articles

Optimisasi Web Service REST API Menggunakan Load Balancer dan Cache dengan Algoritma Round Robin (Studi Kasus: Madani Infosphere)

In today's digital era, the use of REST API Web Services is increasing, becoming a major foundation in web application development due to its simplicity and scalability. However, increased demand and data complexity often lead to a decrease in Web Service performance, especially in terms of response time and service availability. This research focuses on the performance optimisation of Madani Infosphere Web Service, an information platform developed by the non-profit organisation MADANI Berkelanjutan. This platform previously used a single server architecture that was prone to performance issues. To overcome this, this research applies load balancer technology with a round robin algorithm to distribute the workload evenly across multiple servers and uses a cache to store frequently accessed data. The results show that the use of load balancer with round robin algorithm is effective in overcoming potential server overload, making Web Service performance more stable and responsive. In addition, the cache implementation successfully reduces server workload and speeds up response time. Thus, this optimisation can improve the efficiency and availability of Madani Infosphere Web Services.

Mapping Web Service Characteristics to Queueing Theory Models for Performance Analysis

The article presents a comprehensive analysis of mapping the characteristics of Web services to queueing theory models, specifically the M/M/1 queueing model. The authors aim to establish a theoretical foundation for modeling the performance of Web services and deriving response time formulae. The key points covered in the article are as follows: 1. Analyzing the characteristics of Web services: The article examines the six fundamental characteristics of queueing systems, including arrival patterns, service patterns, queue discipline, system capacity, and the number of servers, and maps them to the Web service environment. 2. Representation using Kendall's notation: The Web service characteristics are represented using Kendall's notation, resulting in the M/M/1/∞/FCFS model, where M denotes exponential distributions for arrival and service patterns, 1 represents a single server, ∞ represents infinite system capacity, and FCFS represents the first-come, first-served queue discipline. 3. Derivation of response time formulae: The article provides a detailed derivation of the response time formulae for the M/M/1 model using stochastic processes, Markov chains, and the birth-death process. The derivation incorporates steady-state conditions and results in a formula that calculates the probability of completing a request within a user-specified response time. 4. Practical applications: The derived response time formula can be used by service providers to determine the probability of completing user requests within specified Service Level Agreements (SLAs). By setting a threshold value (filter value), the service provider can select requests with a higher probability of completion for further processing. Overall, the article contributes to the understanding of Web service performance modeling by establishing a theoretical foundation based on queueing theory. The mapping of Web service characteristics to the M/M/1 model and the derivation of response time formulae provide valuable insights for service providers to analyze and optimize the performance of their Web services while adhering to SLAs

Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines

Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines

Decoding Efficiency: A Technical Exploration of Apache, Nginx and Varnish Cache Server through Comprehensive Performance Metrics

Web servers play a crucial role in delivering web content efficiently to users. When a web server receives a request, it processes the request for the requested resources. One method to optimize this process is through the use of cache servers. Cache servers store frequently accessed data in memory, reducing the need to retrieve data from the original source each time a request is made. By leveraging cache servers, web servers can significantly enhance their performance by reducing response times. When a cache server successfully serves a request with a cache hit, it eliminates the need for the web server to process the request and retrieve the data, thereby speeding up the response time. This efficiency is crucial for improving user experience, as faster response times lead to quicker loading of web pages and reduced latency. Through this technical exploration of Apache, Nginx, and Varnish cache servers, we aim to analyze and compare their performance metrics to determine the most effective solution for reducing response times and optimizing web server efficiency. Understanding how cache servers impact performance in terms of cache hit ratio, cache miss ratio, client connections, CPU usage, memory usage, error rates, requests per second, and bandwidth will provide valuable insights into selecting the best cache server solution for improved web server performance. Keywords— Cache Server, Cache, Response time, Apache, Nginx, Varnish.

“Web Services Performance Prediction with Confusion Matrix and K-Fold Cross Validation to Provide Prior Service Quality Characteristics”

The Information Technology sector has experienced substantial growth, especially in the realm of application development, over the last few decades. This ongoing evolution highlights the complexity of contemporary IT applications, which no longer rely on a singular component. Developers now integrate a multitude of components from diverse sources, including users and vendors. Thoroughly examining the quality of external software components before integration into an application is imperative for ensuring optimal service performance. Numerous methodologies and approaches are available for appraising the quality of software components, including the Software as a Service Model (SaaS), Quality of Experience (QoE), and alternative models for identifying quality. These models employ traditional techniques to assess factors like availability, integrity, accessibility, security, performance, and reliability, collectively contributing to the measurement of Quality of Service (QoS). The suggested method enables thorough scrutiny of software components through a 360-degree evaluation, utilizing the Confusion Matrix for predicting performance. This evaluation method ranks web services based on throughput and response time, providing tangible values for decision-making by service users. The classification mechanism aids in categorizing standards within a benchmark web service dataset. By utilizing this performance measuring method, one can determine service quality through the confusion matrix, aiding in the identification of the best web services and contributing to the optimization of application performance.

Research on high performance web service construction method based on JavaScript asynchronous programming technique

Abstract In the face of massive concurrent user access in the era of big data, how to build high-performance web services has become one of the difficulties to be solved by network applications. This paper utilizes Node.js architecture to fully utilize the event-based programming and non-blocking I/O characteristics of JavaScript language to improve the CPU utilization of the web service side. It also proposes the strategy of predicting user-accessed resources for cache replacement through a Gaussian mixture model to further optimize the performance of high-concurrency web services. The performance of the high concurrency web framework constructed using Node.js technology plays excellent, the performance of multi-threaded Node.js is better than single-threaded, and compared with Apache architecture, the average response time, request-response rate, and data throughput test of Node.js architecture in high concurrency scenarios is more advantageous than Apache architecture. The Gaussian mixture clustering model is effective in dealing with data with high-dimensional features, and the average accuracy of the four clustering processes of each algorithm is, in descending order, GMM (79.67%), BIRCH (78.09%), and K-means (77.52%). In addition, the cache replacement strategy based on Gaussian Mixture Model is more effective, with an accuracy rate of nearly 80% and a byte hit rate of nearly 45% when the cache capacity reaches 214KB, which are both higher than the traditional cache replacement strategy with the same cache capacity.

Reducing Mobile Web Latency Through Adaptively Selecting Transport Protocol

To improve the performance of mobile web services, a new transport protocol, QUIC, has been recently proposed as a substitute for TCP. However, with pros and cons of QUIC, it is challenging to decide whether and when to use QUIC in large-scale real-world mobile web services. Complex temporal correlation of network conditions, high user heterogeneity in a nationwide deployment, implementation diversity of QUIC variants limited, and resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans, an adaptive transport protocol selection mechanism, to switch transport protocols for mobile web services online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, adopts an online learning method to keep pace with implementation variation, and switches transport protocols at the request level to reach high performance with acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Baidu</i> . Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 25.8% on average compared to the usage of a single protocol.

APACHE WEB SERVER RESPONSE TIME OPTIMIZATION

This article explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytical methods, we show that MaxClients has a concave-upward effect on response time, and hence hill-climbing techniques can be used to determine the optimal value of MaxClients. We investigate two optimizers that use hill climbing—one based on Newton’s method and one based on fuzzy control. A third technique is a heuristic that exploits the relationship between bottleneck utilization and response time minimization. In all cases, online optimization reduces the response time by a factor of 10 or more compared to the static default value. The trade-offs between online schemes are as follows. Newton’s method is well known but does not provide consistent results for highly variable data such as response times. Fuzzy control is more reliable but converges slowly. The heuristic works well in our prototype system, but it can be difficult to generalize because it requires knowledge of resource bottlenecks and the ability to measure their use. This topic is relevant in the context of a rapidly growing number of web applications and high requirements for the performance and efficiency of web servers. This topic explores how to improve the speed and response time of the Apache web server using various techniques and settings, such as optimizing server settings, using caching, data compression, optimizing request routing, and more. The purpose of this research is to improve the performance and response speed of the Apache web server, which can be useful for developers and administrators of web applications and services. The speed and response time of web servers are critical factors in meeting user needs and achieving business goals for web applications and services. Apache is one of the most widely used web servers in the world, so optimizing the response time of the Apache server is an important task for many developers and administrators of web applications and services. This study will examine various approaches and techniques for optimizing the response time of the Apache web server, including configuring server parameters, using caching, data compression, optimizing request routing, and others. The results of the study can be useful for developers and administrators of web applications and services that deal with the use of the Apache web server. Optimizing Apache server response time can significantly improve the performance and efficiency of web applications and services, which in turn can lead to user satisfaction and business goals.

Mutexion: Mutually Exclusive Compression System for Mitigating Compression Side-Channel Attacks

To enhance the performance of web services, web servers often compress data to be delivered. Unfortunately, the data compression technique has also introduced a side effect called compression side-channel attacks (CSCA) . CSCA allows eavesdroppers to unveil secret strings included in the encrypted traffic by observing the length of data. A promising defense technique called Debreach was recently proposed to mitigate CSCA by excluding all secret data in a web page during the compression process. Although Debreach has proven to be safe against CSCA and outperforms other approaches, the exclusion of all secret data from compression eventually resulted in a decreased compression efficiency. In this paper, we present a highly efficient CSCA mitigation system called “Mutexion” ( Mut ually ex clusive compress ion ) which allows us to fully take advantage of compression over an entire web page, including secret data. The key idea behind Mutexion is to fully take advantage of all the matching subsequences within a web page except only for those between secret data and user-controlled data (potentially controlled by an attacker) during the compression process. This approach of Mutexion effectively prevents side-channel leaks of secret data under CSCA misusing user-controlled data in a web page while minimizing the degradation in compression efficiency. It is required for our compressor to trace both secret data and user-controlled data in its compression process of web pages. To meet this requirement, we provide techniques to enable automated annotation of secret and user-controlled data in web pages. We implemented Mutexion as a fully working system to test live web pages and evaluated its performance with respect to security and compression efficiency. Our evaluation results demonstrated that Mutexion effectively prevents CSCA and also achieves almost the same compression ratio as the original zlib, which is vulnerable to CSCA, with a slight increase (0.032 milliseconds (7.9%) on average) in execution time.

Simulasi Kinerja Web Server Pada Jaringan LAN (Local Area Network) Kampus Menggunakan NS2 (Network Simulator 2)

Web server is a computer that connected to the network or internet which can give information such as text, audio and video. To reach efficiency of design and optimal reliable level, a big scale web server needs that measurement of network design performance. This final task discussed about modeling and simulation of web server design on Local Area Network University of Lampung to find out reachable optimal performance of web server. Research has been done by create modeling of client and server on software simulator. And simulation of web server model design on LAN network has been done by make implementation of simulation scenario. Modeling and simulation will give approximation on web server performance level on LAN network as the consideration of the making of principle realization of web server on LAN network. Based on the result of simulation, web server optimum design on LAN network is on the design with 100 nodes. The design gives average performance level of throughput almost reach the average maximum value of throughput with the minimum average of delay and jitter and the lowest percentage packet loss performance compared with the simulation which is used the other quantity of node.

Early Guessing of Performance Using Simulation as Part of Service Development

In Service-Oriented Architectures (SOA), the key problem is the quick and accurate evaluation of web service performance. Despite the fact that the integration of the simulation step into the development cycle of softwares/web services can allow to learn early the behavior of the performance of software/web service, it is still a challenge to use simulation as part of service development. This integration can be used to assess the performance of a family of web services by developing one of them. In this paper, we propose a methodology that shows how the simulation step can be integrated to the development cycle of a family of services using a model-based approach to describe the services and by choosing a reference web service to be developed and used to guess the performance of the remaining services in the family.

Perbandingan Kinerja Web Server Terhadap Performa Integrasi Multi Basis Data

The research is focused for the purpose of analyzing the performance of which web server is able to receive more requests with a short connection time to support the performance of the integration application and can simulate the user with the jmeter application to test the web server. The performance test process is carried out using the benchmark test method using the Jmeter application by adjusting the input scenario and then analyzing the results based on the TIPHON standard. The results obtained from several variations of test parameters are known that the server's ability to send responses with the largest bytes is the apache web server, while the lowest error rate is nginx, while in terms of server network infrastructure, the apache web server is better because it can send a faster response. good and stable compared to nginx.Based on the test results above, it can be concluded that the apache web server has more effective results in web service performance against multi-database

Comparison of Performance Analysis of Comodo SSL and Let's Encypt SSL (Case Study: Bukalapak and Tokopedia)

The world is becoming connected with the internet, the arrival of the internet has greatly influenced new network technology. The number of internet users is increasing day by day, there is an increase in online transactions. As a growing number of websites have sprung up with easy information transmission facilities, it has been seen that it has caused a huge increase in fraud. Broad e-commerce spectrum (B2B / B2C / C2C), banking, finicial trading and others. Business applications require very high data exchange for security. Secure Sockets Layer (SSL), and its influence on web server performance. Secure SocketLayer (SSL) is the most popular protocol used on the internet to facilitate communication security through authentication, encryption, and decryption. Although the use of SSL provides adequate security, this can cause a decrease in performance compared to insecure protocols as well as a decrease in the percentage of downloads compared to insecure protocols. In this paper, we analyze the performance and impact of SSL on web performance and the type of SSL used by Bukalapak e-commerce and Tokopedia

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer.

Simple SummaryThe OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Web Service Performance Analysis and Research of Distributed LAMP Architecture

There are many websites on the Internet in the early stage of development, its Web service performance is not strong enough, resulting in the failure to run the system and finally the system crash. Based on this situation, in order to ensure the accurate and efficient service of the Internet website, it is necessary to analyze and study the service performance of the Web. Distributed LAMP architecture studies and analyzes the performance of Web services, which can accurately discover the performance defects and problems of Web, and research a more appropriate configuration of service performance, and the final results presented are more accurate and effective. With the deepening development of the Internet application, the website has become a key window for many people’s information sources. Through the Internet website, we can not only understand the world more comprehensively, but also learn more knowledge. Therefore, building Web services with better performance is something we should consider. In this paper, the performance of the Web server was optimized and improved through the analysis and research of the load of the Web server, and the tuning test was carried out through distributed LAMP architecture by observing the Web service’s access to users, data information maintenance and monitoring, etc.

Web Service Performance Analysis using the GTMetrix Web Monitoring Tool

In this paper, delivering a high-level Quality of Service (QoS) has emerged as one of the top priorities for any web service provider as a result of the increasing use of the Internet. With the support of a performance monitoring tool, many factors affecting the performance of web services are examined. Different web services are routinely monitored to determine the different factors that affect their ability to provide services to an enormous number of users. The performance evaluation parameter used is the web service's load time. When the same web service is monitored at various times, the variation in load times is carefully analyzed to determine the factors that lead to this variation and how they affect the performance of any web service.

Performance Analysis and Optimization of Nginx-based Web Server

Performance optimization and analysis of Web server is one of the key technologies of Web server. Through the analysis of the work flow and working principle of the Web server, some influencing factors that are in line with the current environment and have feasibility are selected for targeted optimization, such as value of a thread or process, live time, cache size. Through optimization, the comprehensive performance of Web server based on Nginx has been greatly improved.

Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem

Many web-based software systems have been developed in the form of composite services. It is important to accurately predict the Quality of Service (QoS) value of atomic web services because the performance of such composite services depends greatly on the performance of the atomic web service adopted. In recent years, collaborative filtering based methods for predicting the web service QoS values have been proposed. However, they are mainly faced with a cold start problem that is difficult to make reliable prediction due to highly sparse historical data, newly introduced users and web services, and the existing work only deals with the case of newly introduced users. In this article, we propose a Location-based Matrix Factorization using a Preference Propagation method (LMF-PP) to address the cold start problem. LMF-PP fuses invocation and neighborhood similarity, and then the fused similarity is utilized by preference propagation. LMF-PP is compared with existing approaches on the real world dataset. Based on the experimental results, LMF-PP shows better performance than existing approaches in cold start environments as well as in warm start environments.

How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP’s terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase’s ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase–ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase–inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, Kd, and Ki). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson’s R correlation coefficient is reached by DSX-ONLINE over the Ki dataset. When homology models are used instead of Ki-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).