Today, in most data centers, cloud, no Structured Query Language (No-SQL) and analytics infrastructures have been largely deployed on a direct-attached storage (DAS) architecture and is generally a Total Cost of Ownership (TCO) -driven deployment.
The DAS approach binds the compute and storage resources together, preventing independent scaling and tech refresh cycles. The converged DAS model works very well at smaller scale, but as the infrastructure grows to a substantial size, wasted compute or storage can greatly affect the TCO of the environment. Since the DAS model is constrained by the available slots in a server, scale is limited and often quickly outgrown. In some compute heavy environments, there may be enough DAS allocated to the servers, but the work load needs more Central Processing Units (CPUs), therefore some of the allocated DAS stays unused when additional nodes are added. In addition, since the compute infrastructure is usually on a more aggressive tech-refresh cycle than the storage, converging them together in a single solution limits the flexibility for the tech-refresh. There is a trend to disaggregate at least the warm, cold, and archive data from the compute capacity, and use storage servers in separate racks as Internet Small Computer System Interface (iSCSI) targets to carve out the storage capacity. Hot data, especially if it resides on Solid State Disks (SSDs), is not easily moved to a disaggregated model because of network bandwidth and throughput requirements.