The IT hero over the last decade has been the CIO who has been able to reduce costs, rather than innovate and drive the top line for businesses. The cloud is starting to change this trajectory, Michelle Bailey, senior vice president of digital infrastructure and data initiatives at 451 Research, said Thursday at Rackspace::Solve New York.
Amid all of the buzz around cloud technology, it is critical for IT organizations to understand the economic aspects of this technology beyond differences between CapEx and OpEx that have been discussed ad nauseam.
Are you new to OpenStack? Then you won’t want to miss this! This Wednesday, join me, Niki Acosta, Rackspace Cloud Evangelista, and Scott Sanchez, Rackspace Director of Strategy, as we provide a no-nonsense introduction to OpenStack powered public, private and hybrid clouds. Attendees will learn how companies of all sizes are leveraging OpenStack and the automation it enables to fuel innovation and reduce time to market.
As a Solutions Architect within Rackspace’s Big Cloud team, I have the good fortune of working with both private cloud and public cloud customers. I often see businesses adopt hybrid cloud strategies, whereby services consumed span both these platforms. Deciding where to put different workloads ultimately begs the question: how do public and private cloud platforms stack up against each other from a performance perspective?
We’ve been hearing a lot lately from customers who are frustrated by the limitations of one-size-fits all clouds, whether they’re based on public cloud or private cloud or bare metal servers. These customers want each of their workloads to run where it runs best and most cost-effectively. And that’s what we at Rackspace work to deliver to them, through our hybrid cloud.
Academic and scientific research often involves the construction of mathematical and numerical models to solve scientific and engineering problems. Traditionally, these complex and intensive computational models have been implemented on super computers or high-performance computing (HPC) infrastructure. These models are difficult to setup and operate, and can create a painful experience for researchers who often have to wait in a long line to use their university’s super computing infrastructure, whether it’s for a few hours or a few days.