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Figure 1-4 shows that our application starts to deviate from linear scalability after about 50 concurrent users With a higher number of concurrent sessions, the line migrates toward an exponential trend Notice that I'm drawing attention to the nature of the line, not the numbers to which the line corresponds As we discussed earlier, scalability analysis is not the same as performance analysis; (that is, a slow application is not necessarily unable to scale) While we are interested in the average time per request from a performance standpoint, we are more interested in performance trends with higher concurrent demand, or how well an application deals with increased load, when it comes to scalability Figure 1-5 shows that a theoretical application should maintain a constant number of transactions per second This makes sense: Even though our average response time may increase, the amount of work done per unit time remains the same (Think of a kitchen faucet: It is reasonable that even though it takes longer to wash 100 dishes than to wash one, the number of dishes per second should remain constant) Notice that our mythical application becomes less productive after 50 concurrent users In this sense, it would be better to replicate our application and limit the number of concurrent users to 50 if we want to achieve maximum throughput

hen considering board service after the pomp, circumstance, and corporate rituals are stripped away what is left is oversight, the heavy lifting hidden in a beautifully wrapped honor and recognition package called the public company director Oversight is art rather than applied science Because every company is different, there is no single approach that works for everyone To do the job correctly requires time, effort, skill, integrity, and judgment To complicate matters, there are different types of oversight functions performed by directors They have a responsibility to monitor the business, make certain that risks are properly assessed and managed, and ensure that management is adhering to company policies and code of ethics Each of these perspectives (operational, risk, and compliance) require different but interconnected oversight skills The goal of this chapter is to prepare supervisory black-belts masters capable of monitoring the company from all three perspectives

Call SetActive()

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Table 1-4 Linearly Scalable Application Response and Throughput Times Concurrent Users 1 50 100 150 200 Average Response Time (ms) 10 500 1000 1500 2000 Throughput (tps) 100 100 100 100 100

Analyzing response time and throughput trends, as we have done here, is important for gauging the scalability of your system Figures 1-4 and 1-5 show how to compare an application and its theoretical potential Figure 1-4 illustrates the efficiency from the client's point of view, where the focus is on latency; Figure 1-5 shows application efficiency from the server's point of view, where the focus is on productivity (work done per time unit)

Although monitoring progress and redirecting corporate activities toward strategic objectives is not a new concept, it is where many organizations fail In these cases, feedback loops are not functioning, may be functioning but ignored, or do not exist at all One of the more popular frameworks for monitoring and redirecting business activities and behavior is the balanced scorecard presented in 1996 by Robert S Kaplan and David P Norton in their book of the same name Their framework focuses on four perspectives: (1) financial,

Wait Loop CActiveScheduler::Start()

In measuring throughput, we have ignored the cost of the systems we are analyzing If a system costing $100 can handle 1,000 transactions per second and a system costing $500,000 can handle 1,200 transactions per second, the latter obviously has better throughput but it's gained at a much higher cost The idea of measuring throughput and its relationship to price is something that has been popularized by the Transaction Processing Council (TPC), which has created database benchmarks, better known as the TPC-style benchmarks There are three TPC benchmarks: TPC-A, TPC-B, and TPC-C The most recently developed (as of this writing) is the TPC-C It measures database transaction processing in terms of how efficiently it supports a mythical ordering system Specifically, it measures how many "new order" transactions can be handled while the system is busy handling four other types of order-related transactions (payment, status, etc) While the TPC specification is meant to measure database throughput, you can use the same principle with your systems After all, Web application transactions are at their core a set of database transactions Although it is unlikely that you will benchmark your system against another, you can measure how well your system is improving or lagging over its own evolution For example, if release 1 of your application requires $100,000 worth of hardware and software and nets 10,000 transaction per second, you can calculate a price/performance index by dividing the price by the performance:

*Portions of this chapter are taken or adapted from Scott Green, Manager s Guide to the Sarbanes-Oxley Act (Hoboken, NJ: John Wiley & Sons, 2004)

Service completes and uses RequestComplete() to notify the Active Scheduler (by signalling its thread semaphore) and to post a completion result

This doesn't mean that it costs $10 to execute a transaction on your system It is simply a measure of throughput as it relates to the overall cost of the system Suppose that a year later, release 2 of your application requires $150,000 worth of hardware and handles 40,000 transactions per second The release 2 price/performance index would be:

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