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?
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?
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.
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.