A lot of what we hear in the cloud washed IT conversation today is that scale out architectures are the preferred method of deployment over scale up designs. As the theory goes, scale out architectures provide more and more compute connectivity and storage capacity as you add nodes.

Most vendor marketing has jumped onto the scale out bandwagon leaving implementations that also scale up indistinguishable from the rest. Properly conceived scale out architectures allow us to add additional discrete workloads to an existing system by adding nodes to the underlying infrastructure. The additional nodes accommodate processing required to support the additional separate workloads. This kind of share nothing design, allows us to stack a large quantity of discrete workloads together and manage the entire system is one entity, even though it can be very hard to balance workload across the nodes (silo’ed).

But what about the large quantity of monolithic workloads that need scalable performance but are not able to be distributed across independent nodes? Take for example a single database instance that begins life with just a few users, and is supported well on a single node of a scale out architecture. As this instance grows, more users are added, the database grows beyond the confines of the single node. In many pure scale out implementations, adding additional nodes is ineffectual. The additional nodes do not share in the burden of the workload from the first node. They are designed merely to accommodate additional discrete workloads.

EMC scale out and up products such as VMAX Isilon, provide the cost benefits of a scale out model with the performance benefits of a scale up model. Purchasing only a few nodes initially, IT gains the cost benefit of starting small, and as workloads increase, additional nodes can be brought online, and data and workload is automatically redistributed across all nodes. This creates a share everything approach that supports the ability to scale up discrete applications and while also providing the ability to scale out stacking additional applications into the array along with the first. Software-based controls provide for multi-tenancy, tiering and performance optimization, workload priorities, and quality of service. This is as opposed to scale out architectures where rigid hardware defined partitions separate components of the solution.

I hope this helps demystify “scale out” a little. Just because you can manage a cluster of independent nodes, doesn’t mean it’s an integrated system. Unless workload tasks are shared across the entire resource pool, scale out does not confer the benefits of scale up, where scale up-and-out designs provide the best of both worlds.