BenchmarkXPRT Blog banner

Category: Benchmark metrics

The CloudXPRT Preview is almost here

Justin Greene
By Justin Greene
on June 4, 2020

We’re happy to announce that we’re planning to release the CloudXPRT Preview next week! After we take the CloudXPRT Preview installation and source code packages live, they will be freely available to the public via CloudXPRT.com and the BenchmarkXPRT GitHub repository. All interested parties will be able to publish CloudXPRT results. However, until we begin the formal results submission and review process in July, we will publish only results we produce in our own lab. We’ll share more information about that process and the corresponding dates here in the blog in the coming weeks.

We do have one change to report regarding the CloudXPRT workloads we announced in a previous blog post. The Preview will include the web microservices and data analytics workloads (described below), but will not include the AI-themed container scaling workload. We hope to add that workload to the CloudXPRT suite in the near future, and are still conducting testing to make sure we get it right.

If you missed the earlier workload-related post, here are the details about the two workloads that will be in the preview build:

  • In the web microservices workload, a simulated user logs in to a web application that does three things: provides a selection of stock options, performs Monte-Carlo simulations with those stocks, and presents the user with options that may be of interest. The workload reports performance in transactions per second, which testers can use to directly compare IaaS stacks and to evaluate whether any given stack is capable of meeting service-level agreement (SLA) thresholds.
  • The data analytics workload calculates XGBoost model training time. XGBoost is a gradient-boosting framework  that data scientists often use for ML-based regression and classification problems. The purpose of the workload in the context of CloudXPRT is to evaluate how well an IaaS stack enables XGBoost to speed and optimize model training. The workload reports latency and throughput rates. As with the web-tier microservices workload, testers can use this workload’s metrics to compare IaaS stack performance and to evaluate whether any given stack is capable of meeting SLA thresholds.

The CloudXPRT Preview provides OEMs, the tech press, vendors, and other testers with an opportunity to work with CloudXPRT directly and shape the future of the benchmark with their feedback. We hope that testers will take this opportunity to explore the tool and send us their thoughts on its structure, workload concepts and execution, ease of use, and documentation. That feedback will help us improve the relevance and accessibility of CloudXPRT testing and results for years to come.

If you have any questions about the upcoming CloudXPRT Preview, please feel free to contact us.

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Cloud, CloudXPRT, container scaling, Hosted cloud, Hybrid cloud, IaaS, Machine learning, microservices, Performance benchmarking, Source code, web microservices | Tagged , , , , , , |

More information about the CloudXPRT results submission process

Justin Greene
By Justin Greene
on May 28, 2020

Earlier this month, we discussed the possibility of using a periodic results submission process for CloudXPRT instead of the traditional rolling publication process that we’ve used for the other XPRTs. We’ve received some positive responses to the idea, and while we’re still working out some details, we’re ready to share the general framework of the process we’re planning to use.

  • We will establish a results review group, which only official BenchmarkXPRT Development Community members can join.
  • We will update the CloudXPRT database with new results once a month, on a pre-published schedule.
  • Two weeks before each publication date, we will stop accepting submissions for consideration for that review cycle.
  • One week before each publication date, we will send an email to the results review group that includes the details of that month’s submissions for review.
  • The results review group will serve as a sanity check process and a forum for comments on the month’s submissions, but we reserve the right of final approval for publication.
  • We will not restrict publishing results outside of the monthly review cadence, but we will not automatically add those results to the results database.
  • We may add externally published results to our database, but will do so only after vetting, and only on the designated day each month.

Our goal is to strike a balance between allowing the tech press, vendors, or other testers to publish CloudXPRT results on their own schedule, and simultaneously building a curated results database that OEMs or other parties can use to compete for the best results.

We’ll share more details about the review group, submission dates, and publications dates soon. Do you have questions or comments about the new process? Let us know what you think!

Justin

Posted in Benchmark metrics, BenchmarkXPRT development community, Cloud, CloudXPRT, Performance benchmarking, results, results submission, Server, Servers, Storage | Tagged , , , , , |

Principled Technologies and the BenchmarkXPRT Development Community release the CrXPRT 2 benchmark for Chromebooks

Justin Greene
By Justin Greene
on April 20, 2020

Durham, NC, April 20— Principled Technologies and the BenchmarkXPRT Development Community have released CrXPRT 2, a free app that measures Chromebook battery life, as well as how fast a Chromebook handles everyday tasks like playing video games, watching movies, editing pictures, and doing homework. Testers can install the app on Chromebooks from the Chrome Web Store or by clicking the Chrome Web Store button on CrXPRT.com.

The CrXPRT 2 performance test, which measures a Chromebook’s speed, gives testers an overall score and individual scores for each workload. In addition to an estimated battery life expressed in hours and minutes, the battery life test produces a separate performance score and a frames per second (FPS) rate for a built-in HTML5 gaming component. CrXPRT is user-friendly, delivering results that consumers can understand.

“CrXPRT is a popular, easy-to-use benchmark run by manufacturers, tech journalists, and consumers all around the world,” said Bill Catchings, co-founder of Principled Technologies, which administers the BenchmarkXPRT Development Community. “CrXPRT 2 continues CrXPRT’s legacy of providing relevant and reliable performance and battery life data for Chrome OS devices.”

CrXPRT is part of the BenchmarkXPRT suite of performance evaluation tools, which includes AIXPRT, CloudXPRT, WebXPRT, TouchXPRT, HDXPRT, and MobileXPRT. The XPRTs help users get the facts before they buy, use, or evaluate tech products such as servers, desktops, laptops, and tablets.

To learn more about the BenchmarkXPRT Development Community, go to www.BenchmarkXPRT.com.

About Principled Technologies, Inc.
Principled Technologies, Inc. is a leading provider of technology marketing and learning & development services. It administers the BenchmarkXPRT Development Community.

Principled Technologies, Inc. is located in Durham, North Carolina, USA. For more information, please visit www.PrincipledTechnologies.com.

Company Contact
Justin Greene
BenchmarkXPRT Development Community
Principled Technologies, Inc.
1007 Slater Road, Ste. 300 Durham, NC 27703
BenchmarkXPRTsupport@PrincipledTechnologies.com

Posted in battery life, benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Chrome OS, Chromebooks, CrXPRT, Google Chrome, Performance benchmarking, Press Releases | Tagged , , , , , , , |

More details about CloudXPRT’s workloads

Justin Greene
By Justin Greene
on April 2, 2020

About a month ago, we posted an update on the CloudXPRT development process. Today, we want to provide more details about the three workloads we plan to offer in the initial preview build:

  • In the web-tier microservices workload, a simulated user logs in to a web application that does three things: provides a selection of stock options, performs Monte-Carlo simulations with those stocks, and presents the user with options that may be of interest. The workload reports performance in transactions per second, which testers can use to directly compare IaaS stacks and to evaluate whether any given stack is capable of meeting service-level agreement (SLA) thresholds.
  • The machine learning (ML) training workload calculates XGBoost model training time. XGBoost is a gradient-boosting framework  that data scientists often use for ML-based regression and classification problems. The purpose of the workload in the context of CloudXPRT is to evaluate how well an IaaS stack enables XGBoost to speed and optimize model training. The workload reports latency and throughput rates. As with the web-tier microservices workload, testers can use this workload’s metrics to compare IaaS stack performance and to evaluate whether any given stack is capable of meeting SLA thresholds.
  • The AI-themed container scaling workload starts up a container and uses a version of the AIXPRT harness to launch Wide and Deep recommender system inference tasks in the container. Each container represents a fixed amount of work, and as the number of Wide and Deep jobs increases, CloudXPRT launches more containers in parallel to handle the load. The workload reports both the startup time for the containers and the Wide and Deep throughput results. Testers can use this workload to compare container startup time between IaaS stacks; optimize the balance between resource allocation, capacity, and throughput on a given stack; and confirm whether a given stack is suitable for specific SLAs.

We’re continuing to move forward with CloudXPRT development and testing and hope to add more workloads in subsequent builds. Like most organizations, we’ve adjusted our work patterns to adapt to the COVID-19 situation. While this has slowed our progress a bit, we still hope to release the CloudXPRT preview build in April. If anything changes, we’ll let folks know as soon as possible here in the blog.

If you have any thoughts or comments about CloudXPRT workloads, please feel free to contact us.

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, BenchmarkXPRT, Cloud, CloudXPRT, container scaling, Datacenter, Hybrid cloud, Machine learning, microservices, On-premise, recommender system, Servers, Wide and Deep | Tagged , , , , , , |

Thinking ahead to WebXPRT 4

Justin Greene
By Justin Greene
on March 5, 2020

It’s been about two years since we released WebXPRT 3, and we’re starting to think about the WebXPRT 4 development cycle. With over 529,000 runs to date, WebXPRT continues to be our most popular benchmark because it’s quick and easy to run, it runs on almost anything with a web browser, and it evaluates performance using the types of web technologies that many people use every day.

For each new version of WebXPRT, we start the development process by looking at browser trends and analyzing the feasibility of incorporating new web technologies into our workload scenarios. For example, in WebXPRT 3, we updated the Organize Album workload to include an image-classification task that uses deep learning. We also added an optical character recognition task to the Encrypt Notes and OCR scan workload, and introduced a new Online Homework workload that combined part of the DNA Sequence Analysis scenario with a writing sample/spell check scenario.

Here are the current WebXPRT 3 workloads:

  • Photo Enhancement: Applies three effects, each using Canvas, to two photos.
  • Organize Album Using AI: Detects faces and classifies images using the ConvNetJS neural network library.
  • Stock Option Pricing: Calculates and displays graphic views of a stock portfolio using Canvas, SVG, and dygraphs.js.
  • Encrypt Notes and OCR Scan: Encrypts notes in local storage and scans a receipt using optical character recognition.
  • Sales Graphs: Calculates and displays multiple views of sales data using InfoVis and d3.js.
  • Online Homework: Performs science and English assignment tasks using Web Workers and Typo.js spell check.

What new technologies or workload scenarios should we add? Are there any existing features we should remove? Would you be interested in an associated battery life test? We want to hear your thoughts and ideas about WebXPRT, so please tell us what you think!

Justin

Posted in AI, benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, image classification, JavaScript, Performance benchmarking, WebXPRT, WebXPRT 3, What makes a good benchmark? | Tagged , , , |

CloudXPRT development news

Justin Greene
By Justin Greene
on February 27, 2020

Last month, Bill announced that we were starting work on a new data center benchmark. CloudXPRT will measure the performance of modern, cloud-first applications deployed on infrastructure as a service (IaaS) platforms—on-premises platforms, externally hosted platforms, and hybrid clouds that use a mix of the two. Our ultimate goal is for CloudXPRT to use cloud-native components on an actual stack to produce end-to-end performance metrics that can help users determine the right IaaS configuration for their business.

Today, we want to provide a quick update on CloudXPRT development and testing.

  • Installation. We’ve completely automated the CloudXPRT installation process, which leverages Kubernetes or Ansible tools depending on the target platform. The installation processes differ slightly for each platform, but testing is the same.
  • Workloads. We’re currently testing potential workloads that focus on three areas: web microservices, data analytics, and container scaling. We might not include all of these workloads in the first release, but we’ll keep the community informed and share more details about each workload as the picture becomes clearer. We are designing the workloads so that testers can use them to directly compare IaaS stacks and evaluate whether any given stack can meet service level agreement (SLA) thresholds.
  • Platforms. We want CloudXPRT to eventually support testing on a variety of popular externally hosted platforms. However, constructing a cross-platform benchmark is complicated and we haven’t yet decided which external platforms the first CloudXPRT release will support. We’ve successfully tested the current build with on-premises IaaS stacks and with one externally hosted platform, Amazon Web Services. Next, we will test the build on Google Cloud Hosting and Microsoft Azure.
  • Timeline. We are on track to meet our target of releasing a CloudXPRT preview build in late March and the first official build about two months later. If anything changes, we’ll post an updated timeline here in the blog.

If you would like to share any thoughts or comments related to CloudXPRT or cloud benchmarking, please feel free to contact us.

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, Cloud, CloudXPRT, Collaborative benchmark development, container scaling, Datacenter, Future of performance evaluation, Hosted cloud, Hybrid cloud, IaaS, On-premise, Servers, web microservices | Tagged , , , , , , , |

Check out the other XPRTs:

Forgot your password?
© Principled Technologies, Inc. 2025   |   Privacy policy

Cookie Notice: Our website uses cookies to deliver a smooth experience by storing logins and saving user information. By continuing to use our site, you agree with our usage of cookies as our privacy policy outlines.

x