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Tag Archives: AWS

vMotion across hybrid cloud: performance and best practices

VMware Cloud on AWS is a hybrid cloud service that runs the VMware software-defined data center (SDDC) stack in the Amazon Web Services (AWS) public cloud. The service automatically provisions and deploys a vSphere environment on a bare-metal AWS infrastructure, and lets you run your applications in a hybrid IT environment across your on-premises data centers and AWS global infrastructure. A key benefit of VMware Cloud on AWS is the ability to vMotion workloads back and forth from your on-premises data center to the AWS public cloud as capacity and data privacy require.

In this blog post, we share the results of our vMotion performance tests across our hybrid cloud environment that consisted of a vSphere on-premises data center located in Wenatchee, Washington and an SDDC hosted in an AWS cloud, in various scenarios including hybrid migration of a database server. We also describe the best practices to follow when migrating virtual machines by vMotion across hybrid cloud.

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New white paper: Big Data performance on VMware Cloud on AWS: Spark machine learning and IoT analytics performance on-premises and in the cloud

By Dave Jaffe

A new white paper is available comparing Spark machine learning performance on an 8-server on-premises cluster vs. a similarly configured VMware Cloud on AWS cluster.

Here is what the VMware Cloud on AWS cluster looked like:

Screenshot of cluster configuration

VMware Cloud on AWS configuration for performance tests

Three standard analytic programs from the Spark machine learning library (MLlib), K-means clustering, Logistic Regression classification, and Random Forest decision trees, were driven using spark-perf. In addition, a new, VMware-developed benchmark, IoT Analytics Benchmark, which models real-time machine learning on Internet-of-Things data streams, was used in the comparison. The benchmark is available from GitHub.

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Oracle Database Performance with VMware Cloud on AWS

You’ve probably already heard about VMware Cloud on Amazon Web Services (VMC on AWS). It’s the same vSphere platform that has been running business critical applications for years, but now it’s available on Amazon’s cloud infrastructure. Following up on the many tests that we have done with Oracle databases on vSphere, I was able to get some time on a VMC on AWS setup to see how Oracle databases perform in this new environment.

It is important to note that VMC on AWS is vSphere running on bare metal servers in Amazon’s infrastructure. The expectation is that performance will be very similar to “regular” onsite vSphere, with the added advantage that the hardware provisioning, software installation, and configuration is already done and the environment is ready to go when you login. The vCenter interface is the same, except that it references the Amazon instance type for the server.

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SQL Server Performance of VMware Cloud on AWS

In the past, I’ve always benchmarked performance of SQL Server VMs on vSphere with “on-premises” infrastructure.  Given the skyrocketing interest in the cloud, I was very excited to get my hands on VMware Cloud on AWS – just in time for Amazon’s AWS Summit!

A key question our customers have is: how well do applications (like SQL Server) perform in our cloud?  Well, I’m happy to report that the answer is great!

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Measuring Cloud Scalability Using the Weathervane Benchmark

Cloud-based deployments continue to be a hot topic in many of today’s corporations.  Often the discussion revolves around workload portability, ease of migration, and service pricing differences.  In an effort to bring performance into the discussion we decided to leverage VMware’s new benchmark, Weathervane.  As a follow-on to Harold Rosenberg’s introductory Weathervane post we decided to showcase some of the flexibility and scalability of our new large-scale benchmark.  Previously, Harold presented some initial scalability data running on three local vSphere 6 hosts.  For this article, we decided to extend this further by demonstrating Weathervane’s ability to run within a non-VMware cloud environment and scaling up the number of app servers.

Weathervane is a new web-application benchmark architected to simulate modern-day web applications.  It consists of a benchmark application and a workload driver.  Combined, they simulate the behavior of everyday users attending a real-time auction.  For more details on Weathervane I encourage you to review the introductory post.

Environment Configuration:
Cloud Environment: Amazon AWS, US West.
Instance Types: M3.XLarge, M3.Large, C3.Large.
Instance Notes: Database instances utilized an additional 300GB io1 tier data disk.
Instance Operating System: Centos 6.5 x64.
Application: Weathervane Internal Build 084.

Testing Methodology:
All instances were run within the same cloud environment to reduce network-induced latencies.  We started with a base configuration consisting of eight instances.  We then  scaled out the number of workload drivers and application servers in an effort to identify how a cloud environment scaled as application workload needs increased.  We used Weathervane’s FindMax functionality which runs a series of tests to determine the maximum number of users the configuration can sustain while still meeting QoS requirements.  It should be noted that the early experimentation allowed us to identify the maximum needs for the other services beyond the workload drivers and application servers to reduce the likelihood of bottlenecks in these services.  Below is a block diagram of the configurations used for the scaled-out Weathervane deployment.

Fig1

Results:
For our analysis of Weathervane cloud scaling we ran multiple iterations for each scale load level and selected the average.  We automated the process to ensure consistency.  Our results show both the number of users sustained as well as the http requests per second as reported by the benchmark harness.

Fig2

As you can see in the above graph, for our cloud environment running Weathervane, scaling the number of applications servers yielded nearly linear scaling up to five application servers. The delta in scaling between the number of users and the http requests per second sustained was less than 1%.  Due to time constraints we were unable to test beyond five application servers but we expect that the scaling would have continued upwards well beyond the load levels presented.

Although just a small sample of what Weathervane and cloud environments can scale to, this brief article highlights both the benchmark and cloud environment scaling.  Though Weathervane hasn’t been released publicly yet, it’s easy to see how this type of controlled, scalable benchmark will assist in performance evaluations of a diverse set of environments.  Look for more Weathervane based cloud performance analysis in the future.