Scale UP : This process is also called as Vertical scaling. With this approach you add more hardware like RAM, Processors and Network Interface cards. This provides more memory real estate and bandwidth for computation and hence your application will be faster. Having said that, if we can keep on adding new hardware to existing servers at one point of time they fail or they give us negative performance. So Scale up is always has limitations. While scaling up, the OS and application software also has to support the increased H/W and provide us better performance.
- 1. It doesn’t introduce additional maintenance cost and support cost.
- 2. The current blade server provide plug in architecture to add new H/W. So adding new H/W is a fairly simple process.
- 3. Lot of software vendors support Scale up, so getting support is more easier.
- 4. It’s claimed that Scale up provides much better performance and low maintenance cost compared to Scale out process keeping all the variables constant.
- 1. We cannot simply keep on adding new H/W to existing servers. It whole lot depends on the server architecture and the software too.
- 2. Change in technology compatibility. As technology is changing rapidly, we are not sure how well the new H/W will have the compatibility with old ones.
- 3. The underlined software also should be designed for H/W Fail overs.
Scale Out : Scale out also called as Horizontal Scaling. You add more no of servers to existing infrastructure and do a load balancing. This approach catching up due the technologies like Map/Reduce. In scaling out there is no limitation. Until you can do the load balancing, you can keep adding the new servers to existing cluster and these servers need not to be high end like Scale Up scenarios. They can be commodity H/W.
- 1. Easier to add low end servers as per the demand.
- 2. The servers can be instantly scaled out or scaled down based on the demand.
- 3. This provides right economy of scale.
- 4. In case one node fails in the grid, the system does not stand still, the other nodes will take the load automatically and hence the H/W failure is not a big risk.
- 5. Companies like Aster Data Systems and GreenPlum Software provided enough ground on the Map/Reduce technologies.
- 1. Maintenance and support cost may be higher compared Scale Up process.
- 2. The scalability is much more depends on the net work bandwith provided by the infrastructure. However in practice Scale out process will provide less performance over scale up process.
- 3. Unless you use open source, the licensing cost and initial acquisition cost is more in Scale Out approach.
In typical enterprise, both the Vertical and Horizontal scaling used to take the benefit of both the approaches and they are co-existence. They start with a large vertical architecture server, adding resources as-needed. Once the single server approaches its limit then they scale out. In some cases the scale out is more practical than scale up. For example companies like social networking, they need to scale out rather than scale up. In case the performance is the key, then scale up may be the better choice over scale out.