Month: May 2018

opvizor-performance-analyzer-cisco-ucs-performance

We´ve been very excited to announce the Cisco UCS Performance metrics integration last week. Many customers that are running their VMware vSphere environment on top of Cisco UCS were asking for a deeper integration of core Cisco UCS performance data additional to the vSphere metrics. We listened and integrated a simple way to add Cisco …

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sql-server-performance-of-vmware-cloud-on-aws

Please find here the original article from VMware’s performance team or scroll down for more information. In the past, I’ve always benchmarked performance of SQL Server VMs on vSphere with “on-premises” infrastructure.  Given the skyrocketing interest in the cloud, I was very excited to get my hands on VMware Cloud on AWS – just in time for …

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performance-comparison-of-containerized-machine-learning-applications-running-natively-with-nvidia-vgpus-vs-in-a-vm

This article is by Hari Sivaraman, Uday Kurkure, and Lan Vu from the Performance Engineering team at VMware. Please find here the original article and all related comments and blog posts. Performance Comparison of Containerized Machine Learning Applications Docker containers [6] are rapidly becoming a popular environment in which to run different applications, including those in machine …

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native-mac-learning-in-vsphere-6-7-removes-the-need-for-promiscuous-mode-for-nested-esxi

A new and again very helpful blog post from William Lam. Please find here William’s complete article with all comments. Always a pleasure to read William’s great posts! Over the years, several solutions have been developed here and here to help reduce the impact of promiscuous mode, which is a requirement for running Nested ESXi as a workload. Although these …

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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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