vmware-vsphere-capacity-planning-simplified

No matter how well a infrastructure is designed and a certain buffer is planned, you always need to keep the procurement time to get new hardware in mind. It´s extremely important to start planning ahead when new virtual machines are going to be deployed soon. 

Capacity Planning

We support capacity planning by selecting template VMs in your running infrastructure and multiplying their real load to predict potential future usage. While all features are preconfigured (as always to make it as simple as it gets for our customers) you can tweak pretty much everything to fit your needs.

The first thing you see after loading our dashboard is the help – wait a minute! Just to make sure you know where to get all that:

Download the Dashboard here

Import it like that:

Import the dashboard

availability rate

scale up rate

number of vm 1

sample vm 1

number of vm 2

sample vm 2

How many must be up and running

rate

working hosts

0.5

1 out of 2 working

0.67

2 out of 3 working

0.75

3 out of 4 working

0.8

4 out of 5 working

0.9

9 out of 10 working

1

all Hosts working

Calculate with bigger infrastructure

rate

Infrastructure size

1

current size

1.25

add 25% additional resources to infrastructure

1.6

add 60% additional resources to infrastructure

2

double the infrastructure

2.5

add 150% additional resources to infrastructure

select the number of sample vm 1 for the calculation

select the vm 1 used for calculation

select the number of sample vm 2 for the calculation

select the vm 2 used for calculation

The assessment is based on a 85% limit for Memory and CPU Usage. It can be changed by editing the threshold values and value mappings within the panels.

The help explains how to use the navigation to chose the different VM templates including the number of future systems and the expected load for calculation (Percentile of the selected historic timerange).

Capacity Planning Navigation

The result is a overall view of your infrastructure and if the new virtual machines still fit into it. If they don´t fit you see the a capacity planning information, what percentage (CPU or Memory) the new systems exceed the available infrastructure.

Exceeds or not

Of course you can use the availability rate to take redundancy into account and the scale up rate to plan additional ESXi host resources.

VMware vSphere Capacity Planning results

The rows below the overall view show the used template VMs and the required virtual hardware as well as the utilization according to real historic data.

You can give Performance Analyzer a free run by downloading here and import the dashboard.

CNIL
Metrics and Logs

(formerly, Opvizor Performance Analyzer)

VMware vSphere & Cloud
PERFORMANCE MONITORING, LOG ANALYSIS, LICENSE COMPLIANCE!

Monitor and Analyze Performance and Log files:
Performance monitoring for your systems and applications with log analysis (tamperproof using immudb) and license compliance (RedHat, Oracle, SAP and more) in one virtual appliance!

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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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via email.

CodeNotary — Webinar

White Paper — Registration

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