understanding-vmware-vsphere-terminology-when-switching-from-microsoft-hyper-v

Whether you’re making the switch from Microsoft HyperV to vSphere, or just need to be able to discuss the two platforms intelligently, you will find that there are a number of terms that are just enough different between the two as to make conversations a bit confusing. Here is a list of VMware vSphere terms and the corresponding terminology in Microsoft Hyper-V.

VMware vSphere………………..Microsoft Hyper-V

VMware vSphere Terminology

Once you get accustomed to the different ways that vSphere and Hyper-V express things and get a feel for the differences in development, it is actually pretty easy to go back and forth between the two platforms.

VMware vCenter Server and Microsoft Hyper-V Virtual Machine Manager

Both platforms have their own management tools. VSphere calls theirs the vCenter Server and vCenter Client, while Hyper-V calls theirs the System Center Virtual Machine Manager, blessedly shortened to SCVMM. SCVMM includes both a server and a client component.

VMware Cluster and Windows Failover Cluster

Virtual server clustering is an essential step in allowing for high availability. Both platforms require hosts to connect to LUNs (shared-storage logical unit numbers), where the VM (virtual machine) disk files are stored. Clustering empowers failover in the event of host failure and also enables load balancing in the VMs.

VMware vMotion and Hyper-V Live Migration

VMs can be migrated among hosts when clustered on shared storage. This migration allows for no downtime. VMware’s is called vMotion, while Hyper-V’s is called Live Migration. The process of migration works essentially the same way on both platforms.

VMware HA and High-Availability Virtual Machines

VMware vSphere Cloud

Clustering allows for failover among clusters. These two platforms handle failover a bit differently, but Hyper-V makes it a little harder to determine where virtual machines should restart when recovering from a failure.

Each of these platforms provides high-availability clustering capabilities, and each works in about the same way. Hyper-V clusters just might be a little harder to build. However, the real difference lies in the calculations regarding failover. When a host fails in either platform, the VMs also fail. It’s critical that the failed VMs restart at the right place. In the most recent version of Hyper-V and SCVMM, the failed VMs restart depending on configurations assigned in Windows Failover Clustering. The administrator is able to affect placement by way of suggesting which host each VM should restart on, but the settings have to be configured manually, which is quite difficult.

Though VMware and Hyper-V do have such differences, once a developer masters the terminology and a few such idiosyncrasies, it isn’t difficult at all to master both platforms.

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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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