- Chapter 5 A Step-By-Step Approach to Capacity Planning in Client/Server Systems
- 2 Adequate Capacity
- 3 A Methodology for Capacity Planning in C/S Environments
- 4 Understanding the Environment
- 5 Workload Characterization
- 6 Workload Forecasting
- 7 Performance Modeling and Prediction
- 8 Development of a Cost Model
- 9 Cost/Performance Analysis
- 10 Concluding Remarks
- BIBLIOGRAPHY
5.6 Workload Forecasting
Workload forecasting is the process of predicting how system workloads will vary in the future. Through this process one can answer questions such as: \How will the number of e-mail messages handled per day by the server vary over the next 6 mo?" \How will the number of hits to the corporate intranet's Web server vary over time?" Answering such questions involves evaluating an organization's workload trends if historical data are available and/or analyzing the business or strategic plans of the organization, and then mapping these business plans to changes in business processes (e.g., staff increases and paperwork reduction initiatives will yield 50% more e-mail and Internet usage and 80% more hits on the corporate Web server).
During workload forecasting, basic workload components are associated to business processes so that changes in the workload intensity of these components can be derived from the business process and strategic plans.
Figure 5.8. Workload model validation.
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Excample 5.2:
Consider the C/S transactions in the example of Fig. 5.3. Assume that we plot the number of invoices issued for each of the past 6 mon, months numbered 1{6, as depicted in Fig. 5.9. By using linear regression, we can establish a relationship between the number of monthly invoices and the month number. In this example,
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NumberInvoices = 250 3 Month + 7; 250:
If we now need to forecast the number of invoices for the next month, we can use the above linear relationship to predict that 9,000 invoices will be issued next month. The number of invoices can be associated with the number of transactions of various types submitted to the database server. We can use regression techniques (see Chap. 11) to establish relationships between the number of invoices and the number of C/S transactions of various types, such as sales, purchase requisition, marketing, and finance. These relationships allow us to predict the arrival rate of these types of transactions. For example, if each invoice generates, on the average, 5.4 sales transactions, we can predict that next month, the database server will receive 9; 000 3 5:4 = 48; 600 sales transactions. If we divide this amount by the 22 working days of a month and by the 8 business hours of a day, we get an arrival rate of 276 sales transactions/hour.
Chapter 11 discusses workload forecasting techniques, such as moving averages, exponential smoothing, and linear regression, in detail.