An under-reported trend in storage these days is the mounting dissatisfaction with server-centric storage infrastructure as conceived by proprietary server hypervisor vendors and implemented as exclusive software-defined storage stacks.  A few years ago, the hypervisor vendors seized on consumer anger around overpriced “value-add” storage arrays to insert a “new” modality of storage, so-called software-defined storage, into the IT lexicon.  Touted as a solution for everything that ailed storage – and as a way to improve virtual machine performance in the process – SDS and hyper-converged infrastructure did rather well in the market.  However, the downside of creating silo’ed storage behind server hosts was that storage efficiency declined by 10 percent or more on an enterprise-wide basis; companies were realizing less bang for the buck with software-defined storage than with the enterprise storage platforms they were replacing.

Liquidity is a term you are more likely to hear on a financial news channel than at a technology trade show.  As an investment-related term, liquidity refers the amount of capital available to banks and businesses and to how readily it can be used.  Assets that can be converted quickly to cash (preferably with minimal loss in value) in order to meet immediate and short term obligations are considered “liquid.” When it comes to data storage, liquid storage assets can be viewed as those that can be allocated to virtually any workload at any time without compromising performance, cost-efficiency/manageability, resiliency, or scalability.  High liquidity storage supports any workload operating under any OS, hypervisor, or container technology, accessed via any protocol (network file systems, object storage, block network, etc.), without sacrificing data protection, capacity scaling, or performance optimization.

Design a ROBO scenario must match finally the reality of the customers’ needs, its constraints but also the type of workload and the possible availability solutions of them.