That said, scale-up approaches offer several advantages: They have a perceived simplicity as they typically only have one computing element where you do configuration and management; in some cases, a scale-up architecture may require less power and cooling; scale-up approaches have been around longer and are more familiar to administrators, generally offering a good feature set and functionality to suit their purpose.
But data growth leads to performance problems in a scale-up architecture, and the reason is simple. Because the architectures include a single computing element that houses all network ports, processor and memory, their performance is limited by the capabilities of that component. As data inevitably grows, only capacity (meaning more workload) can be added until such time that the maximum capacity of that controller is reached.
This leads to two significant problems:
" During the period of data growth, the length of all processes also grows. This includes backup time, deduplication time, replication time and recovery time. Obviously, if you throw more workload at a fixed resource and do not provide additional processing power, it takes longer to complete that work.
" At maximum capacity, you are faced with a fork-lift upgrade to a more powerful controller, which can be costly.
Scale-out architectures handle data growth differently. In a scale-out architecture, each building block of the architecture either does include or can include additional elements of performance, including network ports, processors, memory and, yes, disk. As a result, as data grows and capacity is added, processing power is also added.
This means data growth does not lead to longer times for backups, deduplication, replication and recovery. If the workload is quadrupled, the processing power of the architecture is also quadrupled. And there is no "maximum capacity." While vendors may limit how many devices can coexist in a singly managed system, there is never the need for a forklift upgrade as devices can continue to be added individually, even if that means starting a "new system."
Another difficulty found with the scale-up approach relates to system sizing. Many scale-up vendors offer a variety of controller sizes, meaning controllers can handle different amounts of maximum disk. And as you would expect, more powerful controllers that allow for more capacity come at a higher cost. So as a purchaser of this approach, a customer has to decide whether to purchase a controller that can handle a larger environment than currently needed, or purchase a smaller controller knowing they will reach maximum capacity sooner.