Most companies plan their budgets annually, with expenditures split into quarters. Based on your expected normal growth and planned growth bursts, you can map out when you need the resources to be available. Working backward from that date, you need to figure out how long it takes from "go" until the resources are available.
How long does it take for purchase orders to be approved and sent to the vendor? How long does it take from receipt of a purchase order until the vendor has delivered the goods? How long does it take from delivery until the resources are available? Are there specific tests that need to be performed before the equipment can be installed? Are there specific change windows that you need to aim for to turn on the extra capacity? Once the additional capacity is turned on, how long does it take to reconfigure the services to make use of it? Using this information, you can provide an expenditures timetable.
Physical services generally have a longer lead time than virtual services. Part of the popularity of IaaS and PaaS offerings such as Amazonís EC2 and Elastic Storage are that newly requested resources have virtually instant delivery time.
It is always cost-effective to reduce resource delivery time because it means we are paying for less excess capacity to cover resource delivery time. This is a place where automation that prepares newly acquired resources for use has immediate value.
Advanced capacity planning
Large, high-growth environments such as popular Internet services require a different approach to capacity planning. Standard enterprise-style capacity planning techniques are often insufficient. The customer base may change rapidly in ways that are hard to predict, requiring deeper and more frequent statistical analysis of the service monitoring data to detect significant changes in usage trends more quickly. This kind of capacity planning requires deeper technical knowledge. Capacity planners will need to be familiar with concepts such as QPS, active users, engagement, primary resources, capacity limit and core drivers.
Additional math terms
Correlation coefficient: Describes how strongly measurements for different data sources resemble each other.
Moving average: A series of averages, each of which is taken across a short time interval (window), rather than across the whole data set.
Regression analysis: A statistical method for analyzing relationships between different data sources to determine how well they correlate, and to predict changes in one based on changes in another.
EMA: Exponential moving average. It applies a weight to each data point in the window, with the weight decreasing exponentially for older data points.
MACD: Moving average convergence/divergence. An indicator used to spot changes in strength, direction and momentum of a metric. It measures the difference between an EMA with a short window and an EMA with a long window.
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