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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 | Also 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 | Also tagged , , , ,

CloudXPRT is up next, and we’re thinking about how to handle results submission and publication

Justin Greene
By Justin Greene
on May 7, 2020

Last month, we provided an update on the CloudXPRT development process and more information about the three workloads that we’re including in the first build. We’d initially hoped to release the build at the end of April, but several technical challenges have caused us to push the timeline out a bit. We believe we’re very close to ready, and look forward to posting a release announcement soon.

In the meantime, we’d like to hear your thoughts about the CloudXPRT results publication process. Traditionally, we’ve published XPRT results on our site on a rolling basis. When we complete our own tests, receive results submissions from other testers, or see results published in the tech media, we authenticate them and add them to our site. This lets testers make their results public on their timetable, as frequently as they want.

Some major benchmark organizations use a different approach, and create a schedule of periodic submission deadlines. After each deadline passes, they review the batch of submissions they’ve received and publish all of them together on a single later date. In some cases, they release results only two or three times per year. This process offers a high level of predictability. However, it can pose significant scheduling obstacles for other testers, such as tech journalists who want to publish their results in an upcoming device review and need official results to back up their claims.

We’d like to hear what you think about the different approaches to results submission and publication that you’ve encountered. Are there aspects of the XPRT approach that you like? Are there things we should change? Should we consider periodic results submission deadlines and publication dates for CloudXPRT? Let us know what you think!

Justin

Posted in benchmark, Benchmarking, Cloud, CloudXPRT, Future of performance evaluation, Performance benchmarking, results, results submission | Also tagged , ,

Make confident choices about your company’s future tech with the XPRTs

Justin Greene
By Justin Greene
on April 23, 2020

Durham, NC, April 23, 2020 — Principled Technologies and the BenchmarkXPRT Development Community have released a video on the benefits of consulting the XPRTs before committing to new technology purchases.

AIXPRT, one of the battery of XPRT benchmark tools, runs image-classification and object-detection workloads to determine how well tech handles AI and machine learning.

CloudXPRT, another XPRT tool, accurately measures the end-to-end performance of modern, cloud-first applications deployed on infrastructure as a service (IaaS) platforms – allowing corporate decision-makers to select the best configuration for every objective.

All of the XPRTs give companies the real-world information necessary to determine which prospective future tech p – and which will disappoint

According to the video, “The XPRTs don’t just look at specs and features; they gauge a technology solution’s real-world performance and capabilities. So you know whether switching environments is worth the investment. How well solutions support machine learning and other AI capabilities. If next-gen releases beat their rivals or fall behind the curve.”

Watch the video at facts.pt/pyt88k5. To learn more about how AIXPRT, CloudXPRT, WebXPRT, MobileXPRT, TouchXPRT, CrXPRT, and HDXPRT can help IT decision-makers can make confident choices about future purchases, go to www.BenchmarkXPRT.com.

About Principled Technologies, Inc.
Principled Technologies, Inc. is the 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, Suite #300
Durham, NC 27703
BenchmarkXPRTsupport@PrincipledTechnologies.com

Posted in AI, AIXPRT, benchmark, Benchmarking, BenchmarkXPRT, Cloud, CloudXPRT, Cross-platform benchmarks, IaaS, image classification, object detection, Performance benchmarking, Press Releases | Also 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 | Also 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 | Also tagged , , , , , ,

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