We have just published a new whitepaper on the performance of Oracle databases on vSphere 6.5 monster virtual machines. We took a look at the performance of the largest virtual machines possible on the previous four generations of four-socket Intel-based servers. The results show how performance of these large virtual machines continues to scale with the increases and improvements in server hardware.
Oracle Database Monster VM Performance on vSphere 6.5 across 4 generations of Intel-based four-socket servers
In addition to vSphere 6.5 and the four-socket Intel-based servers used in the testing, an IBM FlashSystem A9000 high performance all flash array was used. This array provided extreme low latency performance that enabled the database virtual machines to perform at the achieved high levels of performance.
Some similar tests with Microsoft SQL Server monster virtual machines were also recently completed on vSphere 6.5 by my colleague David Morse. Please see his blog post and whitepaper for the full details.
Weathervane is a performance benchmarking tool developed at VMware. It lets you assess the performance of your virtualized or cloud environment by driving a load against a realistic application and capturing relevant performance metrics. You might use it to compare the performance characteristics of two different environments, or to understand the performance impact of some change in an existing environment.
Weathervane is very flexible, allowing you to configure almost every aspect of a test, and yet is easy to use thanks to tools that help prepare your test environment and a powerful run harness that automates almost every aspect of your performance tests. You can typically go from a fresh start to running performance tests with a large multi-tier application in a single day.
Weathervane supports a number of advanced capabilities, such as deploying multiple independent application instances, deploying application services in containers, driving variable loads, and allowing run-time configuration changes for measuring elasticity-related performance metrics.
At the VMworld 2016 Barcelona keynote, CTO Ray O’Farrell proudly presented the performance improvements in vCenter 6.5. He showed the following slide:
Slide from Ray O’Farrell’s keynote at VMworld 2016 Barcelona, showing 2x improvement in scale from 6.0 to 6.5 and 6x improvement in throughput from 5.5 to 6.5.
As a senior performance engineer who focuses on vCenter, and as one of the presenters of VMworld Session INF8108 (listed in the top-right corner of the slide above), I have received a number of questions regarding the “6x” and “2x scale” labels in the slide above. This blog is an attempt to explain these numbers by describing (at a high level) the performance improvements for vCenter in 6.5. I will focus specifically on the vCenter Appliance in this post.
In a previous blog , we looked at how machine learning workloads (MNIST and CIFAR-10) using TensorFlow running in vSphere 6 VMs in an NVIDIA GRID configuration reduced the training time from hours to minutes when compared to the same system running no virtual GPUs.
Here, we extend our study to multiple workloads—3D CAD and machine learning—run at the same time vs. run independently on a same vSphere server.
This is episode 2 of a series of blogs on machine learning with vSphere. Also see:
Virtual SAN is a VMware storage solution that is tightly integrated with vSphere—making storage setup and maintenance in a vSphere virtualized environment fast and flexible. Virtual SAN 6.2 adds several features and improvements, including additional data integrity with software checksum, space efficiency features of RAID-5 and RAID-6, deduplication and compression, and an in-memory client read cache.
We ran several tests to compare the performance of Virtual SAN 6.1 and 6.2 to make sure they were on par with each other.
Remember that cool project with VMware, HP Enterprise, and IBM where four super huge monster virtual machines (VMs) of 120 vCPUs each were all running at the same time on a single server with great performance?
In addition to the four 120 vCPU VMs test, additional configurations were also run with eight 60 vCPU VMs and sixteen 30 vCPU VMs. This shows that plenty of large VMs can be run on a single host with excellent performance when using a solution that supports tons of CPU capacity and cutting edge flash storage.
The whitepaper not only contains all of the test results from the original presentation, but also includes additional details around the performance of CPU Affinity vs PreferHT and under-provisioning. There is also a best practices section that if focused on running monster VMs.
Ever wondered what it takes to debug performance issues on a VMware stack? How do you figure out if the performance issue is in your virtual machine, or the network layer, or the storage layer, or the hypervisor layer?
Here’s a handy tutorial that showcases a systematic approach for troubleshooting performance using tools like Esxtop, vSCSI stats and Net stats on a VMware stack. The tutorial also talks about some very useful optimizations and performance best practices.
Thanks to Ramprasad K. S. for putting together the slides based on his vast experience dealing with customer issues. Thanks also to Ramprasad and Sai Inabattini for presenting this at the CMG India 2nd Annual conference in Bangalore in November 2015, which was received very well.
VMware vSphere Fault Tolerance (FT) provides continuous availability to virtual machines that require a high amount of uptime. If the virtual machine fails, another virtual machine is ready to take over the job. vSphere achieves FT by maintaining primary and secondary virtual machines using a new technology named Fast Checkpointing. This technology is similar to Storage vMotion, which copies the virtual machine state (storage, memory, and networking) to the secondary ESXi host. Fast Checkpointing keeps the primary and secondary virtual machines in sync.
Performance studies have previously shown that there is no doubt virtualized servers can run a variety of applications near, or in some cases even above, that of software running natively (on bare metal). In a new white paper, we raise the bar higher and test “monster” vSphere virtual machines loaded with CPU and running the most taxing databases and transaction processing applications.
The benchmark workload, which we call Order-Entry, is based on an industry-standard online transaction processing (OLTP) benchmark called TPC-C. Both rigorous and demanding, the Order-Entry workload pushes virtual machine performance.
Note: The Order Entry benchmark is derived from the TPC-C workload, but is not compliant with the TPC-C specification, and its results are not comparable to TPC-C results.
The white paper quantifies the:
Performance differential between ESXi 6.0 and native
Performance differential between ESXi 6.0 and ESXi 5.1
Performance gains due to enhancements built into ESXi 6.0
The networking stack of vSphere is, by default, tuned to balance the tradeoffs between CPU cost and latency to provide good performance across a wide variety of applications. However, there are some cases where using a tunable provides better performance. An example is Web-farm workloads, or any circumstance where a high consolidation ratio (lots of VMs on a single ESXi host) is preferred over extremely low end-to-end latency. VMware vSphere 6.0 introduces the Dynamic Host-Wide Performance Tuning feature (also known as dense mode), which provides a single configuration option to dynamically optimize individual ESXi hosts for high consolidation scenarios under certain use cases. Later in this blog, we define those use cases. Right now, we take a look at how dense mode works from an internal viewpoint.