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Category: Datacenter

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 , , , , , , |

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 , , , , , , , |

The XPRT activity we have planned for first half of 2020

Justin Greene
By Justin Greene
on January 23, 2020

Today, we want to let readers know what to expect from the XPRTs over the next several months. Timelines and details can always change, but we’re confident that community members will see CloudXPRT Community Preview (CP), updated AIXPRT, and CrXPRT 2 releases during the first half of 2020.

CloudXPRT

Last week, Bill shared some details about our new datacenter-oriented benchmark, CloudXPRT. If you missed that post, we encourage you to check it out and learn more about the need for a new kind of cloud benchmark, and our plans for the benchmark’s structure and metrics. We’re already testing preliminary builds, and aim to release a CloudXPRT CP in late March, followed by a version for general availability roughly two months later.

AIXPRT

About a month ago, we explained how the number of moving parts in AIXPRT will necessitate a different development approach than we’ve used for other XPRTs. AIXPRT will require more frequent updating than our other benchmarks, and we anticipate releasing the second version of AIXPRT by mid-year. We’re still finalizing the details, but it’s likely to include the latest versions of ResNet-50 and SSD-MobileNet, selected SDK updates, ease-of-use improvements for the harness, and improved installation scripts. We’ll share more detailed information about the release timeline here in the blog as soon as possible.

CrXPRT 2

As we mentioned in December, we’re working on CrXPRT 2, the next version of our benchmark that evaluates the performance and battery life of Chromebooks. You can find out more about how CrXPRT works both here in the blog and at CrXPRT.com.

We’re currently testing an alpha version of CrXPRT 2. Testing is going well, but we’re tweaking a few items and refining the new UI. We should start testing a CP candidate in the next few weeks, and will have firmer information for community members about a CP release date very soon.

We’re excited about these new developments and the prospect of extending the XPRTs into new areas. If you have any questions about CloudXPRT, AIXPRT, or CrXPRT 2, please feel free to ask!

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, BenchmarkXPRT, BenchmarkXPRT development community, Chromebooks, Cloud, CloudXPRT, Collaborative benchmark development, Community Preview, CrXPRT, Datacenter, ResNet-50, SSD-MobileNet v1 | Tagged , , , , , , , , |

CloudXPRT is on the way

Bill Catchings
By Bill Catchings
on January 16, 2020

A few months ago, we wrote about the possibility of creating a datacenter XPRT. In the intervening time, we’ve discussed the idea with folks both in and outside of the XPRT Community. We’ve heard from vendors of datacenter products, hosting/cloud providers, and IT professionals that use those products and services.

The common thread that emerged was the need for a cloud 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 on-premises, hosted elsewhere, or some combination of the two (hybrid clouds). Regardless of where clouds reside, applications are increasingly using them in latency-critical, highly available, and high-compute scenarios.

Existing datacenter benchmarks do not give a clear indication of how applications will perform on a given IaaS infrastructure, so the benchmark should use cloud-native components on the actual stacks used for on-prem and public cloud management.

We are planning to call the benchmark CloudXPRT. Our goal is for CloudXPRT to address the needs described above while also including the elements that have made the other XPRTs successful. We plan for CloudXPRT to

  • Be relevant to on-prem (datacenter), private, and public cloud deployments
  • Run on top of cloud platform software such as Kubernetes
  • Include multiple workloads that address common scenarios like web applications, AI, and media analytics
  • Support multi-tier workloads
  • Report relevant metrics including both throughput and critical latency for responsiveness-driven applications and maximum throughput for applications dependent on batch processing

CloudXPRT’s workloads will use cloud-native components on an actual stack to provide end-to-end performance metrics that allow users to choose the best IaaS configuration for their business.

We’ve been building and testing preliminary versions of CloudXPRT for the last few months. Based on the progress so far, we are shooting to have a Community Preview of CloudXPRT ready in mid- to late-March with a version for general availability ready about two months later.

Over the coming weeks, we’ll be working on getting out more information about CloudXPRT and continuing to talk with interested parties about how they can help. We’d love to hear what workflows would be of most interest to you and what you would most like to see in a datacenter/cloud benchmark. Please feel free to contact us!

Bill

Posted in benchmark, BenchmarkXPRT, BenchmarkXPRT development community, Cloud, CloudXPRT, Collaborative benchmark development, Datacenter, Future of performance evaluation, Hosted cloud, Hybrid cloud, IaaS, On-premise, Performance benchmarking, Server | Tagged , , , , , , , , |

CES 2020: AI in action and a “smart” future

Justin Greene
By Justin Greene
on January 9, 2020

During last year’s Consumer Electronics Show (CES), one question kept coming to mind as I walked the floor: Are we approaching the tipping point where AI truly affects most people in meaningful ways on a daily basis? I think it’s safe to say that we’ve reached that point as a result of AI integration with phones. After all, for many of us, AI improves the quality of our photography, recommends words and phrases as we text and search the web, and lets us know when to allow extra drive time because traffic is heavy.

However, for me, the most intriguing aspects of this year’s CES are the glimpses of how AI will change every area of our lives, with and without mobile devices. The show floor is jam-packed with ways to integrate AI with everything from athletic shoes to pet care to the kitchen sink. Many of these ideas are fascinating on their own, and they’re all part of a much bigger picture. The next few years will see increased AI utilization in medicine, transportation, agriculture, water and energy distribution, natural resource protection, and many more areas. Our personal smart devices will connect to smart vehicles, smart homes, smart grids, and smart cities. In the near future, CES shows won’t need AI sections because AI will be a part of everything.

At each step of this journey, people will need objective data about how well their tech can handle the demands of common AI workloads. We’re excited that AIXPRT is already becoming a go-to tool for testing inference performance on laptops, desktops, and servers. There’s much more to come with AIXPRT in 2020, along with news about XPRTs in the datacenter, so stay tuned to the blog for exciting developments in the weeks to come!

I’ll leave you with pics from three of my favorite displays at this year’s show. The first is a model of Toyota’s Woven City. Toyota announced plans to build an entire mini city on existing company land near Mount Fuji. The city will house 2,000 people and will serve as an enormous real-time lab where designers and engineers can test ubiquitous AI and sensor technology. Toyota will also design the city to be fully sustainable with the use of hydrogen fuel cells and solar panels.

The second picture shows the electric Hyundai Urban Air Mobility prototype. Hyundai is partnering with Uber on this project, and the planned vertical take-off and landing (VTOL) craft will seat five passengers plus a pilot, have a range of 60 miles, and be able to recharge in less than 10 minutes. These concepts aren’t new, but battery and material sciences technologies are progressing to the point that this one may get off the ground!

The third picture shows BrainCo’s AI Prosthetic Hand display. The hand provides amputees with new levels of dexterity compared to previous prosthetics, and it uses AI to learn from the user’s patterns of movement. The idea is that the accuracy of gestures and grips will improve over time, allowing users to accomplish tasks that are impossible with existing technology. A young man in the booth was using the hand to paint beautiful and precise Chinese calligraphy. Very cool!

Justin

Posted in AI, AIXPRT, CES, Datacenter, Future of performance evaluation, Machine learning, Mobile devices, recommender system, Trade Shows | Tagged , , , , , , |

More, faster, better: The future according to Mobile World Congress 2019

Mark Van Name
By Mark Van Name
on March 7, 2019

More is more data, which the trillions of devices in the coming Internet of Things will be pumping through our air into our (computing) clouds in hitherto unseen quantities.

Faster is the speed at which tomorrow’s 5G networks will carry this data—and the responses and actions from our automated assistants (and possibly overlords).

Better is the quality of the data analysis and recommendations, thanks primarily to the vast army of AI-powered analytics engines that will be poring over everything digital the planet has to say.

Swimming through this perpetual data tsunami will be we humans and our many devices, our laptops and tablets and smartphones and smart watches and, ultimately, implants. If we are to believe the promise of this year’s Mobile World Congress in Barcelona—and of course I do want to believe it, who wouldn’t?—the result of all of this will be a better world for all humanity, no person left behind. As I walked the show floor, I could not help but feel and want to embrace its optimism.

The catch, of course, is that we have a tremendous amount of work to do between where we are today and this fabulous future.

We must, for example, make sure that every computing node that will contribute to these powerful AI programs is up to the task. From the smartphone to the datacenter, AI will end up being a very distributed and very demanding workload. That’s one of the reasons we’ve been developing AIXPRT. Without tools that let us accurately compare different devices, the industry won’t be able to keep delivering the levels of performance improvements that we need to realize these dreams.

We must also think a lot about how to accurately measure all other aspects of our devices’ performance, because the demands this future will place on them are going to be significant. Fortunately, the always evolving XPRT family of tools is up to the task.

The coming 5G revolution, like all tech leaps forward before it, will not come evenly. Different 5G devices will end up behaving differently, some better and some worse. That fact, plus our constant and growing reliance on bandwidth, suggests that maybe the XPRT community should turn its attention to the task of measuring bandwidth. What do you think?

One thing is certain: we at the Benchmark XPRT Development Community have a role to play in building the tools necessary to test the tech the world will need to deliver on the promise of this exciting trade show. We look forward to that work.

Posted in 5G, AI, AIXPRT, Automation, Benchmarking, BenchmarkXPRT development community, Datacenter, Future of performance evaluation, Internet of things, IoT, Machine learning, Mobile devices, Mobile World Congress, Networking, Performance of computing devices, Phones, Tablets, Trade Shows | Tagged , , , , , , , , , , , |

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