![]() As the computing done in data centers has become more intensive, however, it might exceed the capacity of a single server. The idea of a data center started as a place where multiple servers could be co-located and called on demand for computing. ![]() And a move to this architecture must be done in an evolutionary manner, without disrupting the older architecture resources already in place. But composing and interconnecting the resources isn’t trivial. Instead, various resources are pooled together and allocated as jobs require. There is a new movement, generally referred to as “data-center disaggregation,” that moves away from the server as the basic unit. “Everyone’s trying to make data center resources a service on tap - both on the software side and now adding hardware as a service on tap,” said Arif Khan, group director of product marketing for PCIe, CXL and interface IP at Cadence. The scale is big enough that simple architectural changes that they can influence and control today can turn into millions of dollars of savings.”Īdding that flexibility sounds straightforward enough, but it represents a massive change. “A few years back, they were buying servers from PC vendors. “Amazon, Microsoft, and Google are operating at a much bigger scale,” said Rakesh Renganathan, director of marketing, power management, power and sensors solutions business unit at Infineon. ![]() ![]() This has led to a complete reorganization in the hyper-scaler data centers to use compute resources more efficiently, and the idea now is beginning to percolate through other data centers. With the current model, existing resources cannot be leveraged because the server blade is the basic unit of partition. But when a server is selected, some of those resources go unused, despite being needed somewhere else in the data center. Traditionally, data centers were built with racks of servers, each server providing computing, memory, interconnect, and possibly acceleration resources. Data centers are undergoing a fundamental shift to boost server utilization and improve efficiency, optimizing architectures so available compute resources can be leveraged wherever they are needed. ![]()
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