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4.3.10 Scalability
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Scalability is the ability of a system to accomplish more work while maintaining the quality (i.e., without degradation) of the products produced or services provided by the system. There are different ways to calculate the work produced or performed by the system and it usually depends on the kind of product produced or services rendered.
For software systems, here are some of the common metrics used to quantify the products produced or the services provided :
- Number of transactions or events per unit of time (i.e., 5,000 credit card transactions a second1))
- Number of tweets processed per unit of time (i.e., 6,000 tweets a second2))
- Number of hours of videos uploaded per minute (i.e., 500 hours of fresh video per minute3))
- Number of searches per day (i.e., 3.5 billion searches per day4)).
Scalability is about being able to increase the output of products and services without major disruptions, interruptions or increased costs. Often, because of the Economies of Scale, the estimates for the costs should actually decline.
There are different approaches to Scalability:
- Scaling Up is is generally applied to centralized or decentralized systems or products. It amounts to just adding more resources with more powerful versions. This works until you have reached the limits on the availability of more power. For example, the speed of the Central Processing Unit (CPU), memory or even the network. In decentralized systems(i.e., Cloud Computing) the scaling can either be Vertical Scaling or Horizontal Scaling.
- Scaling Out is usually associated with Distributed System which by its nature allows for more Network Nodes replicated with prepacked applications (i.e., Distributed Application (ĐApp or DApp)) to be added with minimal overhead cost. In essence, more nodes offering redundant products and services. Another approach is to divide up the problem by functionality. For example, if accessing customer data is the bottleneck, then add more nodes that access the same data. If the access to the actual data is a bottleneck, then adding replications to the data store is recommended.
Both Scaling up and scaling out are valid alternatives.
DIDO Specifics
