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Big Data Strategies: Limitations with Scale-up and Scale –Out Systems


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Big Data Strategies: Limitations with Scale-up and Scale –Out Systems


Deploying a scale-out solution has required significant engineering effort; the system developer often needs to handcraft the mechanisms for data partitioning and reassembly, not to mention the logic to schedule the work across the cluster and handle individual machine failures.


The traditional approaches to scale-up and scale-out have not been widely adopted outside large enterprises, government, and academia. The purchase costs are often high, as is the effort to develop and manage the systems. These factors alone put them out of the reach of many smaller businesses. In addition, the approaches themselves have had several weaknesses that have become apparent over time:


Firstly, as scale-out systems get large, or as scale-up systems deal with multiple CPUs, the difficulties caused by the complexity of the concurrency in the systems have become significant. Effectively utilizing multiple hosts or CPUs is a very difficult task, and implementing the necessary strategy to maintain efficiency throughout execution of the desired workloads can entail enormous effort.


Secondly, Hardware advances—often couched in terms of Moore's law—have begun to highlight discrepancies in system capability. CPU power has grown much faster than network or disk speeds have; once CPU cycles were the most valuable resource in the system, but today, that no longer holds. Whereas a modern CPU may be able to execute millions of times as many operations as a CPU 20 years ago would, memory and hard disk speeds have only increased by factors of thousands or even hundreds. It is quite easy to build a modern system with so much CPU power that the storage system simply cannot feed it data fast enough to keep the CPUs busy.


As just hinted, taking a scale-up approach to scaling is not an open-ended tactic. There is a limit to the size of individual servers that can be purchased from mainstream hardware suppliers, and even more niche players can't offer an arbitrarily large server. At some point, the workload will increase beyond the capacity of the single, monolithic scale-up server, so then what? The unfortunate answer is that the best approach is to have two large servers instead of one. Then, later, three, four, and so on. Or, in other words, the natural tendency of scale-up architecture is—in extreme cases—to add a scale-out strategy to the mix.


In software development, though this gives some of the benefits of both approaches, it also compounds the costs and weaknesses; instead of very expensive hardware or the need to manually develop the cross-cluster logic, this hybrid architecture requires both.


As a consequence of this end-game tendency and the general cost profile of scale-up architectures, they are rarely used in the big data processing field and scale-out architectures are the de facto standard.


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