vmware-vsphere-sizing

Markus Kraus wrote a Power CLI script in order to make it easier for him and his colleagues the recurring task of the data collection in order to make a VMware vSphere Sizing. This collects some information in an existing VMware vSphere environment as a basis for a new hardware sizing.

If you can read German, please check the complete article here

The focus of the collected data is the server sizing. The current version also contains some storage data.

Of course, these data alone are not sufficient to create sizing. For Markus a qualified sizing means a holistic view of the requirements and processes of the customer.

vSphere sizing

Photo courtesy of Markus Kraus

Complete output of the current version 1.2

Want that information on your mobile…

and just without the hazzle of connecting to a system with a running PowerShell, PowerShell and the script installed. We’re more than happy to let you know that we added that great script module from Markus to OpBot!

You can use it a bit different, as we changed the command name as well as the command parameters, but it’s as simple to use as it can get.

Tell OpBot posh cluster-summary and that’s it:

posh vsphere sizing

To list all the clusters just use list clusters command.

Download OpBot and simplify your IT life today

More information on the native PowerCLI command

vSphere sizing

Photo courtesy of Markus Kraus

Output without stats

_vSphere sizing

Photo courtesy of Markus Kraus

_

Also a detailed output of the recording is possible

Calling the VMware vSphere sizing script

Markus created the script as a PowerShell module with the aim of easy handling.

vSphere sizing

Photo courtesy of Markus Kraus

For further processing the data can of course be exported:

vSphere sizing

Photo courtesy of Markus Kraus

For a large number of vSphere clusters, the call may also look like this:

vSphere sizing

Photo courtesy of Markus Kraus

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Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance

Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace

Goal:

No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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