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Check out our new CloudXPRT video!

Justin Greene
By Justin Greene
on August 13, 2020

Many businesses want to move critical applications to the cloud, but choosing the right cloud-based infrastructure as a service (IaaS) platform can be a complex and costly project. We developed CloudXPRT to help speed up and simplify the process by providing a powerful benchmarking tool that allows users to run multiple workloads on cloud platform software in on-premises and popular public cloud environments.

To help spread the word about what CloudXPRT can do and why it matters to businesses, we’ve published a new video, Choose the best IaaS configuration for your business with CloudXPRT, on YouTube and CloudXPRT.com. If you know anyone who is evaluating cloud options, or who would be interested in CloudXPRT testing or results, we encourage you to share the video with them. As always, if you have any questions about CloudXPRT, please let us know!

Justin

Video: Choose the best IaaS configuration for your business with CloudXPRT.

Posted in benchmark, Benchmarking, BenchmarkXPRT, Cloud, CloudXPRT, data analytics, IaaS, On-premise, Performance benchmarking, Server, Servers, Video, web microservices | Tagged , , , , , , , , , , , , |

A CloudXPRT build with bug fixes is on the way

Justin Greene
By Justin Greene
on July 9, 2020

We want to let CloudXPRT testers know that updated installer packages are on the way. The packages will include several fixes for bugs that we discovered in the initial CloudXPRT Preview release (build 0.95). The fixes do not affect CloudXPRT test results, but do help to facilitate installation and remove potential sources of confusion during the setup and testing process.

Along with a few text edits and other minor fixes, we made the following changes in the upcoming build:

  • We updated the data analytics setup code to prevent error messages that occurred when the benchmark treated one-node configurations as a special case.
  • We configured the data analytics workload to use a go.mod file for all the required go modules. With this change, we can explicitly state the release version of the necessary go modules, and updates to the latest go release won’t break the benchmark. This change also removes the need to include large gosrc.tar.gz files in the source code.
  • We added a cleanup utility script for the web microservices workload. If something goes wrong during configuration or a test run, testers can use this script to clean everything and start over.
  • We fixed an error that prevented the benchmark from successfully retrieving the cluster_config.json file in certain multi-node setups.
  • In the web microservices workload, we changed the output format of the request rate metric from integer to float. This change allows us to report workload data with a higher degree of precision.
  • In the web microservices workload, we added an overall summary line to results log file that reports the best throughput numbers from the test run.
  • In the web microservices code, we modified a Kubernetes option that the benchmark used to create the Cassandra schema. Prior to this change, the option generated an inconsequential but distracting error message about TTY input.

We haven’t set the release date for the updated build yet, but when we do, we’ll announce it here in the blog. If you have any questions about CloudXPRT, please let us know!

Justin

Posted in benchmark, Benchmarking, BenchmarkXPRT, Cloud, CloudXPRT, data analytics, Kubernetes, Servers, web microservices | Tagged , , , , , , , |

The CloudXPRT Preview is here!

Justin Greene
By Justin Greene
on June 11, 2020

The CloudXPRT Preview installation packages are now available on CloudXPRT.com and the BenchmarkXPRT GitHub repository! The CloudXPRT Preview includes two workloads: web microservices and data analytics (you can find more details about the workloads here). Testers can use metrics from the workloads to compare IaaS stack (both hardware and software) performance and to evaluate whether any given stack is capable of meeting SLA thresholds. You can configure CloudXPRT to run on local datacenter, Amazon Web Services, Google Cloud Platform, or Microsoft Azure deployments.

Several different test packages are available for download from the CloudXPRT download page. For detailed installation instructions and hardware and software requirements for each, click the package’s readme link. The Helpful Info box on CloudXPRT.com also contains resources such as links to the CloudXPRT master readme and the CloudXPRT GitHub repository. Soon, we will add a link to the CloudXPRT Preview source code, which will be freely available for testers to download and review.

All interested parties may now 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 anticipate adding the first set of those within the coming week.

We’re thankful for all the input we received during the initial CloudXPRT development process, and we welcome feedback on the CloudXPRT Preview. If you have any questions about CloudXPRT, or would like to share your comments and suggestions, please let us know.

Justin

Posted in benchmark, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, CloudXPRT, Clusters, Datacenter, Hosted cloud, On-premise, Performance benchmarking, results, results submission, Server, Servers, Source code, web microservices | Tagged , , , , , , , , |

BenchmarkXPRT releases a preview of CloudXPRT, a benchmark for measuring the performance of cloud-first applications deployed on modern on-prem or hosted IaaS platforms

Justin Greene
By Justin Greene
on June 11, 2020

Durham, NC, June 10 —Principled Technologies and the BenchmarkXPRT Development Community release the CloudXPRT Preview, a free benchmark that can accurately measure the performance of modern, cloud-first applications deployed on modern infrastructure as a service (IaaS) platforms, whether those platforms are paired with on-premises (datacenter), private cloud, or public cloud deployments.

The CloudXPRT Preview includes web microservices and data analytics workloads. Testers can use metrics from both workloads to compare IaaS stack (both hardware and software) performance and to evaluate whether any given stack is capable of meeting SLA thresholds. CloudXPRT can be configured to run on local datacenter, AmazonWeb Services™, Google Cloud Platform™, or Microsoft Azure™ deployments.

“Existing datacenter benchmarks don’t make it easy to understand how applications will perform on a given IaaS infrastructure,” said Bill Catchings, co-founder of Principled Technologies, which administers the BenchmarkXPRT Development Community. “CloudXPRT uses cloud-native components on a hardware and software stack to provide end-to-end performance metrics that allow users to choose the best configuration for their business.”

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. Testers may also freely access the CloudXPRT source code.

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

To learn more about the BenchmarkXPRT Development Community, go to www.BenchmarkXPRT.com or contact a BenchmarkXPRT Development Community representative directly by sending a message to BenchmarkXPRTsupport@PrincipledTechnologies.com.

About Principled Technologies, Inc.
Principled Technologies, Inc. is a leading provider of technology marketing, as well as learning and 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 27704
BenchmarkXPRTsupport@PrincipledTechnologies.com

Posted in BenchmarkXPRT, Cloud, CloudXPRT, data analytics, Performance benchmarking, Press Releases, Server, Servers, 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 , , , , , |

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 , , , , , , |

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