You need IT infrastructure that you can count on even when you run into the rare network outage, equipment failure, or power issue. When your systems run into trouble, that’s where one or more of the three primary availability strategies will come into play: high availability, fault tolerance, and/or disaster recovery.
While each of these infrastructure design strategies has a role in keeping your critical applications and data up and running, they do not serve the same purpose. Simply because you operate a High Availability infrastructure does not mean you shouldn’t implement a disaster recovery site — and assuming otherwise risks disaster indeed.
What’s the difference between HA, FT, and DR anyway? Do you really need DR if you have HA set up?
You’re ready to start deploying and migrating applications into Microsoft’s Azure cloud platform — but there are four deployment models to contend with. Which should you choose? Each has strengths and weaknesses depending on the service you are setting up. Some might require more attention than others, but offer additional control. Others integrate services like load balancing or Operating Systems as more of a Platform as a Service.
Learn the differences between Azure Service Fabric, Azure Virtual Machines, Azure Containers, and Azure App Services, and when you might want to choose one over another. Green House Data is also ready to help you decide which of your business applications belong in which bucket — and we can help you administrate them, too.
As cloud adoption rates have increased and cloud models for enterprise IT mature, multicloud deployments have become more and more popular. They happen for a variety of reasons: some cloud platforms are better suited for specific applications, others may have security or compliance measures that are necessary. They might be located in different physical sites, fostering failover and disaster recovery or serving satellite markets. For many users, avoiding being locked in with a single vendor is huge for negotiation and data sovereignty.
Going multicloud isn’t a simple task, however, especially if you want to manage everything with a simple workflow. Here are the biggest stumbling blocks companies are facing when implementing multicloud.
When managing a virtualized environment you’ll naturally want to monitor your compute resources such as memory, CPU, storage, and bandwidth in order to keep an eye on any possible performance issues.
We’ve covered monitoring before – like how much information to collect, how granular you need to get, how to check load averages, and configuring vSphere Alarms for resource consumption. Today we’re taking a closer look at CPU performance monitoring in particular.
Often times the CPU is the first potential culprit to check when you encounter a struggling virtual machine. Learn the differences between CPU metrics, some common problems, and best practices for provisioning CPU cores in this blog.
Green House Data announced the addition of Azure cloud to our stable of managed cloud services this week. For some, this may come as a bit of a shock. We’ve been a VMware shop since the company was formed, with the gBlock Cloud hosted within our data centers on the vSphere platform.
We’ll continue to offer our own hosted VMware cloud as well as VMware cloud management on behalf of our clients, but we’ve expanded our scope to include Azure managed services. There are a number of reasons for this shift in strategy, which ultimately allows clients a wider breadth of service options to best suit their IT infrastructure goals.
We thought everyone finally had cloud terminology all cleared up. You’ve certainly seen the countless blogs about IaaS, PaaS, and SaaS; not to mention the ever-proliferating surveys and reports on hybrid cloud being the deployment flavor du jour.
But things aren’t as clear as we might want them to be. For example, tell me what you think of when you hear the term “public cloud.”
Is it a hyperscale provider like AWS, Azure, or Google? It is, isn’t it? If not, you probably work with or for an organization similar to Green House Data, which has a public cloud offering with some major differences from the hyperscale players.
So how can we clear up the cloud? Has public become synonymous with hyperscale and self-provisioning? Has private cloud fallen by the wayside? And what should your business focus on, anyway?
Businesses of all types are collecting more data — sometimes this is inadvertent, sometimes it is completely intentional. With more and more devices connected to the internet and your corporate network, in addition to software that can store and analyze that data, information sprawl is a real factor in IT infrastructure strategy.
In order to better plan for data management, you should ask yourself these four questions. They can help guide your data strategy to maintain security, minimize risk, plan for costs, and improve performance.
The term “cloud” may only have reached our collective consciousness in the past few years, but the concepts involved in cloud computing date back many decades. Starting with utility computing and moving on to virtualization and grid computing, distributing compute resources has long been a way to minimize costs involved with IT infrastructure.
Let’s see how we moved from the mainframe to Salesforce with this quick history of cloud computing.