What do recommendation engines for Amazon and Netflix have to do with better cloud computing? Thanks to a ground-breaking system from Stanford University, one is inspiring the other. Most servers use around 20 percent of capacity when running with their typical workloads. There are several reasons for this, the main one being that cloud service users tend to overestimate the amount of compute they’ll need. Other reasons include systems inefficiency as workloads are passed between newer and older physical processing cores or hardware. And changes to applications, where a code rewrite may impose a bigger load on the server, also impact efficiency. On top of all of this, other applications that share the workspace may interfere with performance. Professor Christos Kozyrakis, associate professor of electrical engineering and computer science at Stanford’s multi-scale architecture and systems team, said: “Everyone has the problem of underperforming servers.” Datacentres today are the base stations for countless processes and computations going on all over the networked world at once. If we can successfully schedule application workloads in shared environments as much as possible, everybody benefits. Whether it’s Google, Amazon, or your own infrastructure, those workloads are spread across different infrastructures, different locations and sometimes even… Read full this story
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