It generates multi-agent workload scenarios based on test cases defined in an XML file, and displays stats and error reporting results in real-time. Pylot is a Python-based performance and scalability testing tool for web services. Output includes connection rate, connection time statistics (min, max, median, stddev), request/reply rate, and network throughput. ![]() Httperf is an HTTP workload generator command-line tool which can generate a number of different types of HTTP traffic, including GET/HEAD/PUT/POST requests, HTTP pipelining, SSL traffic, stateful sessions with cookie, etc. It supports real-time reports via the Graphite protocol, and can be integrated via extensions with other third-party building tools such as Maven, Jenkins, SBT. Using a lightweight asynchronous testing engine, it can easily simulate thousands of concurrent users whose web browsing behaviors and scenarios (e.g., login, browse product listings, add a product to cart, check out) are independently scripted. Gatling is an open-source protocol-agnostic load testing tool primarily used to benchmark HTTP servers and web services. ![]() Features include GET/POST/PUT/DELETE requests, basic authentication, cookie, HTTPS with SSL/TLS, browser cache emulation, and CSS/image/JavaScript fetching. It can perform functional unit testing, as well as stress and longevity testing. FunkLoadįunkLoad is a web server load testing tool written Python. Per-client status and statistics are logged to a file. Simulated clients can conduct various tasks, such as authenticated login (POST or GET/POST), GET/POST/PUT requests from batch configuration with probabilistic distribution, FTP passive/active operations, HTTP logoff (POST, GET/POST, GET with cookie), etc. curl-loaderĬurl-loader is a command-line application workload generator which can simulate multiple HTTP/HTTPS FTP/FTPS clients. It provides highly pluggable testing architecture via extensible data visualization GUI. It can be used to test the performance of web-server backends powered by server-side languages (e.g., PHP, Java, ASP.NET) or databases (e.g., JDBC, LDAP, MongoDB). Apache JMeterĪpache JMeter is a cross-platform Java-based GUI program designed to stress test any web application. Testing results include requests per second, time per request, transfer rate, connection time statistics (min, max, median, mean), etc. Support for POST/PUT/GET requests, as well as basic password authentication is available. It can send an arbitrary list of (concurrent) web requests. ApacheBenchĪpacheBench (ab) is a standard command-line web server benchmark tool bundled with Apache HTTP server. A typical benchmark tool injects synthetic workloads or replays real-world traces to a web server, and measures web server performance and scalability in terms of varying metrics (e.g., response time, throughput, number of requests per second, CPU load, etc).įor those of you who want to find out how your web server or web service will measure up under different workload conditions, here are a list of web server benchmark tools available on Linux platforms. To compare and optimize web server performance under such a wide array of factors, we often perform load test (or stress test) using a web server micro-benchmark tool. The results indicate the great challenge of our CMRCīenchmark.What are good web server benchmarking tools for LinuxĪs far as web server performance is concerned, there are many different factors at play, e.g., front-end application design, network latency/bandwidth, web server configuration, server-side in-memory cache, raw hardware capability, server load of shared hosting, etc. Besides, we implement three strong baselines to tackle Well-annotated natural responses rather than the specific spans or short phrase ![]() The topics of conversations areĬollected from social media platform and cover 33 domains, trying to beĬonsistent with real scenarios. Model's comprehension ability more reasonably. Each turn of aĬonversation is assigned with a response-related passage, aiming to evaluate Hot-topic driven conversations with 4,742 turns in total. Model's generalization ability towards diverse domains. To this end, we propose the first Chinese CMRCīenchmark Orca and further provide zero-shot/few-shot settings to evaluate Thus, model's comprehension ability towards real scenarios are hard However, existing CMRC benchmarks in whichĮach conversation is assigned a static passage are inconsistent with real Questions in conversations, which has been a hot research topic in recent yearsīecause of its wide applications. Authors: Nuo Chen, Hongguang Li, Yinan Bao, Junqing He, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li Download PDF Abstract: The conversational machine reading comprehension (CMRC) task aims to answer
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