A new paper describes the DRS enhancements in vSphere 6.7, which include new initial placement, host maintenance mode enhancements, DRS support for non-volatile memory (NVM), and enhanced resource pool reservations.
Resource pool and VM entitlements—old and new models
A summary of the improvements follows:
DRS in vSphere 6.7 can now take advantage of the much faster placement and more accurate recommendations for all DRS configurations. vSphere 6.5 did not include support for some configurations like VMs that had fault tolerance (FT) enabled, among others.
Starting with vSphere 6.7, DRS uses the new initial placement algorithm to come up with the recommended list of hosts to be placed in maintenance mode. Further, when evacuating the hosts, DRS uses the new initial placement algorithm to find new destination hosts for outgoing VMs.
DRS in vSphere 6.7 can handle VMs running on next generation persistent memory devices, also known as Non-Volatile Memory (NVM) devices.
There is a new two-pass algorithm that allocates a resource pool’s resource reservation
to its children (also known as divvying).
PerfPsychic our AI-based performance analyzing tool, enhances its accuracy rate from 21% to 91% with more data and training when debugging vSAN performance issues. What is better, PerfPsychic can continuously improve itself and the tuning procedure is automated. Let’s examine how we achieve this in the following sections.
How to Improve AI Model Accuracy
Three elements have huge impacts on the training results for deep learning models: amount of high-quality training data, reasonably configured hyperparameters that are used to control the training process, and sufficient but acceptable training time. In the following examples, we use the same training and testing dataset as we presented in our previous blog.
We in VMware’s Performance team create and maintain various tools to help troubleshoot customer issues—of these, there is a new one that allows us to more quickly determine storage problems from vast log data using artificial intelligence. What used to take us days, now takes seconds. PerfPsychic analyzes storage system performance and finds performance bottlenecks using deep learning algorithms.
Let’s examine the benefit artificial intelligence (AI) models in PerfPsychic bring when we troubleshoot vSAN performance issues. It takes our trained AI module less than 1 second to analyze a vSAN log and to pinpoint performance bottlenecks at an accuracy rate of more than 91%. In contrast, when analyzed manually, an SR ticket on vSAN takes a seasoned performance engineer about one week to deescalate, while the durations range from 3 days to 14 days. Moreover, AI also wins over traditional analyzing algorithms by enhancing the accuracy rate from around 80% to more than 90%.
With the release of vSphere 6.7, VMware added iSER (iSCSI Extensions for RDMA) as a native supported storage protocol to ESXi. With iSER run over iSCSI, users can boost their vSphere performance just by replacing the regular NICs with RDMA-capable NICs. RDMA (Remote Direct Memory Access) allows the transfer of memory from one computer to another. This is a direct transfer and minimizes CPU/kernel involvement. By bypassing the kernel, we get extremely high I/O bandwidth and low latency. (To use RDMA, you must have an HCA/Host Channel Adapter device on both the source and destination.) In this blog, we compare standard iSCSI performance vs. iSER performance to see how iSER can release the full potential of your iSCSI storage.
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:
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.
We published a paper that shows how VMware is helping advance PMEM technology by driving the virtualization enhancements in vSphere 6.7. The paper gives a detailed performance analysis of using PMEM technology on vSphere using various workloads and scenarios.
These are the key points that we cover in this white paper:
We explain how PMEM can be configured and used in a vSphere environment.
We show how applications with different characteristics can take advantage of PMEM in vSphere. Below are some of the use-cases:
How PMEM device limits can be achieved under vSphere with little to no overhead of virtualization. We show virtual-to-native ratio along with raw bandwidth and latency numbers from fio, an I/O microbenchmark.
How traditional relational databases like Oracle can benefit from using PMEM in vSphere.
How scaling-out VMs in vSphere can benefit from PMEM. We used Sysbench with MySQL to show such benefits.
How modifying applications (PMEM-aware) can get the best performance out of PMEM. We show performance data from such applications, e.g., an OLTP database like SQL Server and an in-memory database like Redis.
Using vMotion to migrate VMs with PMEM which is a host-local device just like NVMe SSDs. We also characterize in detail, vMotion performance of VMs with PMEM.
We outline some best practices on how to get the most out of PMEM in vSphere.
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.
The recently described Meltdown/Spectre vulnerabilities have implications throughout the tech industry, and the VMmark virtualization benchmark is no exception. In deciding how to approach the issue, the VMmark team’s goal was to address the impact of the these vulnerabilities while maintaining the value and integrity of the benchmark.
Applying the full set of currently available Meltdown/Spectre mitigations is likely to have a significant impact on VMmark scores. Because the mitigations are expected to continue evolving for some time, that impact might even change. If the VMmark team were to require the full set of mitigations in order for a submission to be compliant, that might make new submissions non-competitive with older ones, and also introduce more “noise” into VMmark scores as the mitigations evolve. While our intention for the future is that eventually all new VMmark results will be obtained on virtualization platforms that have the full set of Meltdown/Spectre mitigations, we have chosen to take a gradual approach.
Beginning May 8, 2018, all newly-published VMmark results must comply with a number of new requirements related to the Meltdown and Spectre vulnerabilities. These requirements are detailed in Appendix C of the latest edition of the VMmark User’s Guide.
Before performing any VMmark benchmark runs intended for publication, check the VMmark download page to make sure you’re using the latest edition of the VMmark User’s Guide. If you have questions, you can reach the VMmark team at firstname.lastname@example.org.
VMware and Microsoft have collaborated to validate and support the functionality and performance scalability of SQL Server 2017 on vSphere-based Linux VMs. The results of that work show SQL Server 2017 for Linux installs easily and has great performance within VMware vSphere virtual machines. VMware vSphere is a great environment to be able to try out the new Linux version of SQL Server and be able to also get great performance.