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Cisco data center fabric – vision, evolution, benefits

Among all the IT domains, perhaps the most action is in the data center, and by extension, in the cloud. Virtualization has taken root, and delivered a lot of operational efficiency. It has provided some interesting challenges as well. Virtual Machine (VM) mobility is one.  Tracking workloads as they move between servers, within and across data centers is more fun than most people imagined. So, how does one take this dynamic environment, and leverage it to fulfill requirements such as:

  • Delivering anything as a service – handling heterogeneous workloads for any application
  • Dealing with VM mobility – optimizing resource allocation across any location
  • Offering dynamic response – responding to real-time requirements at any scale

How does one solve these emerging challenges to achieve the next levels of productivity and efficiency?

For quite some time, Cisco has believed in the promise of “going beyond silos” (Yeah, that’s the campaign we launched as well, for those of you who saw the recent ads). But awareness campaigns apart, the concept is pretty simple – how do we take some of the traditional silos in the data center like the network, compute, storage and application services and bring them together – holistically – to deliver better efficiency, resource utilization, simplicity and cost benefits.

Fundamentally, this is the promise of Cisco’s data center fabric approach – it delivers on the vision of a high-performance, shared infrastructure, that brings together the network, compute, storage access elements, and L4-7 application services into a tightly integrated resource. It is open, integrated, flexible, scalable, resilient and secure.  And it is built off a vision that Cisco has been executing for 3+ years now on the foundation of Unified Fabric, Unified Network Services and Unified Computing. This foundation will form the bedrock for customers looking to move towards cloud-based models exploring application independence, location freedom and massive scale.

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