Predictable. "The performance needs to be predictable," Kirsch says. "If I add 6TB this week and 6TB next week, I want that same linear scalability in terms of performance. I don't want to have to re-architect my application or re-educate my users. It should just scale in a predictable fashion. I want it to be pay as you grow. Don't make me overinvest today. I know that Moore's Law is going to give me faster computing next month and that drives are going to get denser over time. Let me take advantage of that in my storage infrastructure. And please, let this be shared symmetric architecture. Don't force me to understand differences in your architecture. Allow me to scale this system as I need it."
Efficient. "Let me leverage all the resources in my storage system, regardless of where they are," Kirsch says. "Let me get great utilization out of my physical disk drives, not 50 or 55 percent, but over 80 percent of that storage should be utilized for my data. Regardless of where the CPU is or the compute or the cache, let me take advantage of that. Whether the application over here is hot or the application over there, I want the storage system to maximize the performance of that application. And please, integrate tiering into this system." In other words, you shouldn't have to move data around to optimize performance or optimize capacity. Scale-out NAS for big data needs to be intelligent enough to automate that for you.
Available. "This has to be available all the time," Kirsch says. "Take advantage of an N-way architecture. Allow me to survive more than two failures. Allow me to survive when a rack goes down in my environment. I want this to be on all the time. And let it be flexible. Let me align the availability of the protection of the system with the needs of my business units. If they're willing to invest more, I can give them greater availability. If the data is less valuable, I can give them less availability." Boiled down, since a scale-out NAS storage infrastructure is built on commodity hardware, there's an assumption that hardware will fail and the system has to be designed to deal with a higher rate of hardware failure.
Enterprise-proven. "As the technology has matured, it's no longer this side project that's outside of IT," Kirsch says. "It's a key part of IT. It's got to have snapshots, replication, quotas, and all the other traditional IT features. This technology really evolved out of an HPC root, but if you're going to build a scale-out system, ultimately you've got to build it to fit into an enterprise environment."