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Performance of Enterprise Web Applications in Docker Containers on VMware vSphere 6.5

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Docker containers are growing in popularity as a deployment platform for enterprise applications. However, the performance impact of running these applications in Docker containers on virtualized infrastructures is not well understood. A new white paper is available that uses the open source Weathervane performance benchmark to investigate the performance of an enterprise web application running in Docker containers in VMware vSphere 6.5 virtual machines (VMs).  The results show that an enterprise web application can run in Docker on a VMware vSphere environment with not only no degradation of performance, but even better performance than a Docker installation on bare-metal.

Weathervane is used to evaluate the performance of virtualized and cloud infrastructures by deploying an enterprise web application on the infrastructure and then driving a load on the application.  The tests discussed in the paper use three different deployment configurations for the Weathervane application.

  • VMs without Docker containers: The application runs directly in the guest operating systems in vSphere 6.5 VMs, with no Docker containers.
  • VMs with Docker containers: The application runs in Docker containers, which run in guest operating systems in vSphere 6.5 VMs.
  • Bare-metal with Docker containers: The application runs in Docker containers, but the containers run in an operating system that is installed on a bare-metal server.

The figure below shows the peak results achieved when running the Weathervane benchmark in the three configurations.  The results using Docker containers include the impact of tuning options that are discussed in detail in the paper.

Some important things to note in these results:

  • The performance of the application using Docker containers in vSphere 6.5 VMs is almost identical to that of the same application running in VMs without Docker.
  • The application running in Docker containers in VMs outperforms the same application running in Docker containers on bare metal by about 5%. Most of this advantage can be attributed to the sophisticated algorithms employed by the vSphere 6.5 scheduler.

The results discussed in the paper, along with the results of previous investigations of Docker performance on vSphere, show that vSphere 6.5 is an ideal platform for deploying applications in Docker containers.

What-If? Resource Management with vSphere DRS

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vSphere Distributed Resource Scheduler (DRS) provides a simple and easy way to manage your cluster resources. DRS works well, out of the box for most vSphere installations.

For cases where more flexibility is desired in how the cluster is managed, DRS provides many options in the form of cluster rules, settings and advanced options.

Often the impact of using rules in a DRS cluster is not very well understood. The settings and advanced options are not very well documented. Imagine if it was possible to play around with rules in your cluster before actually applying them, or changing the DRS migration threshold in your cluster without changing the setting in your live cluster – and yet, be able to visualize the impact of those actions in your cluster?

Introducing – DRS Dump Insight – to help with simple queries regarding DRS behavior, like the following.

  • What if I dropped all the affinity rules in my cluster?
  • What if I set cluster advanced option “AggressiveCPUActive”?
  • What if I changed the DRS migration threshold from 3 to 5?

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Introducing DRS DumpInsight

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In an effort to provide a more insightful user experience and to help understand how vSphere DRS works, we recently released a fling: DRS Dump Insight.

DRS Dump Insight is a service portal where users can upload drmdump files and it provides a summary of the DRS run, with a breakup of all the possible moves along with the changes in ESX hosts resource consumption before and after DRS run.

Users can get answers to questions like:

  • Why did DRS make a certain recommendation?
  • Why is DRS not making any recommendations to balance my cluster?
  • What recommendations did DRS drop due to cost/benefit analysis?
  • Can I get all the recommendations made by DRS?

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SPBM Performance Whitepaper

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VMware Storage Policy Based Management (SPBM) is a storage policy framework that helps administrators match VM workload requirements against storage capabilities. SPBM runs as an independent service in the vCenter Server. We recently released a white paper that covers SPBM performance in two sections.

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New White Paper: Fast Virtualized Hadoop and Spark on All-Flash Disks – Best Practices for Optimizing Virtualized Big Data Applications on VMware vSphere 6.5

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A new white paper is available showing how to best deploy and configure vSphere 6.5 for Big Data applications such as Hadoop and Spark running on a cluster with fast processors, large memory, and all-flash storage (Non-Volatile Memory Express storage and solid state disks). Hardware, software, and vSphere configuration parameters are documented, as well as tuning parameters for the operating system, Hadoop, and Spark.

The best practices were tested on a 13-server cluster, with Hadoop installed on vSphere as well as on bare metal. Workloads for both Hadoop (TeraSort and TestDFSIO) and Spark Machine Learning Library routines (K-means clustering, Logistic Regression classification, and Random Forest decision trees) were run on the cluster. Configurations with 1, 2, and 4 VMs per host were tested as well as bare metal. Among the 4 virtualized configurations, 4 VMs per host ran fastest due to the best utilization of storage as well as the highest percentage of data transfer within a server. The 4 VMs per host configuration also ran faster than bare metal on all Hadoop and Spark tests but one.

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DRS Lens – A new UI dashboard for DRS

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DRS Lens provides an alternative UI for a DRS enabled cluster. It gives a simple, yet powerful interface to monitor the cluster real time and provide useful analyses to the users. The UI is comprised of different dashboards in the form of tabs for each cluster being monitored.

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The Extreme Performance Series at VMworld 2017

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I’m excited to announce that the “Extreme Performance Series” is back for its 5th year, and with 7 additional sessions, it’s our largest year ever! These sessions are created and presented by VMware’s best and most distinguished performance engineers, principals, architects and gurus. You do not want to miss these advanced sessions.

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Introducing VMmark3: A highly flexible and easily deployed benchmark for vSphere environments

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VMmark 3.0, VMware’s multi-host virtualization benchmark is generally available here.  VMmark3 is a free cluster-level benchmark that measures the performance, scalability, and power of virtualization platforms.

VMmark3 leverages much of previous VMmark generations’ technologies and design.  It continues to utilize a unique tile-based heterogeneous workload application design. It also deploys the platform-level workloads found in VMmark2 such as vMotion, Storage vMotion, and Clone & Deploy.  In addition to incorporating new and updated application workloads and infrastructure operations, VMmark3 also introduces a new fully automated provisioning service that greatly reduces deployment complexity and time.

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NEW VMworld 2017 Bootcamp – vSphere Advanced Performance Design, Configuration and Troubleshooting

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New this year for VMworld 2017 in Las Vegas, we will be offering a pre-VMworld bootcamp focused on vSphere platform performance. Specific SQL and Oracle bootcamps will still be offered, but we have had many requests for a workload agnostic program. This bootcamp will enable you to confidently support all your virtual workloads and give you an opportunity to directly interact with VMware Performance Engineering.

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Introducing TPCx-HS Version 2 – An Industry Standard Benchmark for Apache Spark and Hadoop clusters deployed on premise or in the cloud

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Since its release on August 2014, the TPCx-HS Hadoop benchmark has helped drive competition in the Big Data marketplace, generating 23 publications spanning 5 Hadoop distributions, 3 hardware vendors, 2 OS distributions and 1 virtualization platform. By all measures, it has proven to be a successful industry standard benchmark for Hadoop systems. However, the Big Data landscape has rapidly changed over the last 30 months. Key technologies have matured while new ones have risen to prominence in an effort to keep pace with the exponential expansion of datasets. One such technology is Apache Spark.

spark-logo-trademarkAccording to a Big Data survey published by the Taneja Group, more than half of the respondents reported actively using Spark, with a notable increase in usage over the 12 months following the survey. Clearly, Spark is an important component of any Big Data pipeline today. Interestingly, but not surprisingly, there is also a significant trend towards deploying Spark in the cloud. What is driving this adoption of Spark? Predominantly, performance.

Today, with the widespread adoption of Spark and its integration into many commercial Big Data platform offerings, I believe there needs to be a straightforward, industry standard way in which Spark performance and price/performance could be objectively measured and verified. Just like TPCx-HS Version 1 for Hadoop, the workload needs to be well understood and the metrics easily relatable to the end user.

Continuing on the Transaction Processing Performance Council’s commitment to bringing relevant benchmarks to the industry, it is my pleasure to announce TPCx-HS Version 2 for Spark and Hadoop. In keeping with important industry trends, not only does TPCx-HS support traditional on premise deployments, but also cloud.

I envision that TPCx-HS will continue to be a useful benchmark standard for customers as they evaluate Big Data deployments in terms of performance and price/performance, and for vendors in demonstrating the competitiveness of their products.

 

Tariq Magdon-Ismail

(Chair, TPCx-HS Benchmark Committee)

 

Additional Information:  TPC Press Release

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