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Agile Big Data Clusters: DriveScale Enables Bare Metal Cloud

We've been writing recently about the hot, potentially inevitable, trend, towards a dense IT infrastructure in which components like CPU cores and disks are not only commoditized, but deployed in massive stacks or pools (with fast matrixing switches between them). Then a layered provisioning solution can dynamically compose any desired "physical" server or cluster out of those components. Conceptually this becomes the foundation for a bare-metal cloud. DriveScale today announces their agile architecture with this approach, aimed first at solving big data multi-cluster operational challenges. 

By being able to carve out big data clusters on demand from a large densely racked pool of compute nodes and JBODs, big data clients can get exactly the infrastructure they want with their provisioned resources completely dedicated and isolated to them. IT can deliver multi-tenancy services physically carved out of one maintained pool of infrastructure. At the same time each cluster is fully elastic and adjustable on demand.

Note that a bare-metal cloud doesn't completely solve every performance problem for those cluster clients that don't do much internal capacity planning or job QoS isolation within their provisioned cluster. But it does seem that if you'd layer in something like Pepperdata in each cluster you would then have complete control of both performance and capacity within and between clusters.

We envision cloud solutions with these architectures will soon be able to offer customers a choice of virtual machines (in which the client doesn't need to know or care what the infrastructure actually is) or actual physical instances assembled on the fly. These dynamic physical machines have natural isolation, known processing power and reserved capacity, and yet are still essentially elastic. And physically provisioned clusters might be ideal not just for expansive big data clustering, but for large container-centric architectures too.

  • Premiered: 05/19/16
  • Author: Mike Matchett
Topic(s): DriveScale Big Data Composable Cloud


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