In traditional computing environment, once the resource capacity of a system is enhanced

manually the status of the system is retained (ever after) till further human intervention, even

if those resources do not get utilized. This under-utilization causes wastage of resources and

increases the cost of computing. Yet, very little can be done about it.

The main reason behind this problem is that the infrastructure architecture is not dynamic

in traditional computing system, which prevents implementation of dynamic scaling. Static

scaling requires system shut-down (system to restart) and hence is avoided unless it becomes

extremely essential. For this reason, in traditional static scaling environment, although

resource capacity expansion was sometime considered a possible option, capacity contraction

was beyond imagination because service disruption had to be avoided.

In contrast to the actual definition, conventionally, scalability has been about supplying

additional capacity to a system. It was uncommon to reduce capacity of a system, although

technically it was always possible. No one ever thought of migrating to a system of lesser

capability, even when workload was reduced below the average level. System designers have

built computing systems by arranging resources to meet peak demand, wasting resources and

increasing cost.

In the traditional static scaling approach, computing system requires a ‘restart’ for the scaling

effect to take place which causes service disruption.

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