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Scalability is almost as easy to measure as performance is We know that scalability refers to an application's ability to accommodate rising resource demand gracefully, without a noticeable loss in QoS To measure scalability, it would seem that we need to calculate how well increasing demand is handled But how exactly do we do this Let's consider a simple example Suppose that we deploy an online banking application One type of request that clients can make is to view recent bank transactions Suppose that when a single client connects to the system, it takes a speedy 10 ms of server-side time to process this request Note that network latency and other client or network issues affecting the delivery of the response will increase the end-to-end response time; for example, maybe end-to-end response time will be 1,000 ms for a single client But, to keep our example simple, let's consider just server-side time Next, suppose that 50 users simultaneously want to view their recent transactions, and that it takes an average of 500 ms of server-side time to process each of these 50 concurrent requests Obviously, our server-side response time has slowed because of the concurrency of demands That is to be expected Our next question might be: How well does our application scale To answer this, we need some scalability metrics, such as the following:



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Calculation options include basic aggregation functions (SUM, COUNT, AVERAGE, etc), as well as % of, Running total, and several other options Sort settings: Choose to sort rows or columns based on either headers or values Filter: Individual header values can be selected, Label lters can be de ned (eg, StateProvince does not contain Wales ), or Value lters can be de ned (eg, show only periods with more than 100 orders) Properties: Analysis Services data sources associate properties with many of the values listed in the header Some of these values may not be available directly in the Field List Properties may be exposed either directly as columns in the spreadsheet or as a tooltip when the cursor hovers over a header Additional Actions: Analysis Services can associate actions, such as running reports, with header values.

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Throughput the rate at which transactions are processed by the system Resource usage the usage levels for the various resources involved (CPU, memory, disk, bandwidth) Cost the price per transaction

Because PivotTables display summary data, it is often useful to drill into the details behind a sum or count Double-clicking on any value will create a new worksheet with the associated detail rows By default, Analysis Services data sources limit the rows returned by a drill-through to 1,000, but this maximum is con gurable via the Connection Properties dialog After a bit of practice, generating a desired view in this environment is extremely time ef cient, limited mostly by the speed of the underlying data source Insights into data can be gained at a surprising rate..

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self-canceling installment notes (SCIN), 329 transfers in trust, 330 Investment returns measures, 143 146 after-tax return, 144 after-tax yield, 145 146 annualized return, 143 current yield, 145 expected rate of return, 144 geometric return, 146 holding period return, 144 internal rate of return (IRR), 144 real (in ation-adjusted) return, 143 realized compound rate, 146 required rate of return, 143 risk-adjusted return, 143 yield-to-call, 145 yield-to-maturity, 144 145 Investment risk measures 139 143 beta, 142 143 coef cient of determination (R2), 139 140 coef cient of variation, 142 correlation coef cient, 140 covariance, 140 semi-variance, 140 standard deviation, 140 142 variance, 140 Investment risk types, 138 139 business risk, 139 unsystematic risk, 139 exchange rate risk, 139 exchange risk, 139 in ation risk, 138 interest rate risk, 138 139 liquidity risk, 139 political risk, 139 reinvestment risk, 139 systematic risk, 139 Investment strategies, 159 162 buy and hold, 160 fundamental analysis, 159 market timing (active investing), 159 passive investing (indexing), 159 portfolio immunization, 160 swaps, 160 technical analysis, 161 Con dence Index (CI), 161 contrarians, 161 market indicators, 161 162 breadth of the market, 162 Dow theory, 161 162 bear market, 162 bull market, l62 importance of volume, 162 moving average lines, 162 relative strength ratio, 162 short interest ration, 162 support and resistance levels, 162 Investment strategies in tax-advantaged accounts, 172 appropriate assets for tax-advantaged versus taxable accounts, 172 capital gain versus ordinary income, 172 net unrealized appreciation (NUA), 172 tax advantages, 172 Investment vehicles types and use, 122 137 American depositary receipts (ADR), 137 asset-backed securities, 136 callable, 126 interest rate risk, 126 reinvestment risk, 126 certi cates of deposit and cash equivalents, 122 banker s acceptances, 122 certi cates of deposit, 122 commercial paper, 122 money market mutual funds, 122 repurchase agreements, 122 US Treasury bills, 122 collateralized mortgage obligations (CMOs), 136 137 convertible, 125 126 conversion ratio, 126 corporate bonds, 125 investment grade, 125 debentures, 125 secured versus unsecured bonds, 125 zero coupon bonds, 125 derivatives, 130 131 futures, 131 132 options, 130 131 embedded option types, 127 accelerated sinking fund provision, 127 call provision 127 conversion rights, 127 prepayment provision, 127 put provision, 127 exchange traded funds (ETFs), 132 high-yield, 125 index securities, 132 133 insurance-based investments annuities, 127 xed, 127 variable, 127 guaranteed investment contracts (GICs), 127.

A more detailed discussion of these and other metrics can be found in Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning (Menasce and Almeida, 2000) Measuring resource use is fairly easy; measuring throughput and cost requires a bit more explanation What is the throughput in both of the cases described, with one user and with 50 users To calculate this, we can take advantage of something called Little's law, a simple but very useful measure that can be applied very broadly Consider the simple black box shown in Figure 1-3 Little's law says that if this box contains an average of N users, and the average user spends R seconds in that box, then the throughput X of that box is roughly

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Little's law can be applied to almost any device: a server, a disk, a system, or a Web application Indeed, any system that employs a notion of input and output and that can be considered a black box is a candidate for this kind of analysis Armed with this knowledge, we can now apply it to our example Specifically, we can calculate application throughput for different numbers of concurrent users Our N will be transactions, and since R is in seconds, we will measure throughput in terms of transactions per second (tps) At the same time, let's add some data to our banking example Table 1-3 summarizes what we might observe, along with throughputs calculated using Little's law Again, keep in mind that this is just an example; I pulled these response times from thin air Even so, they are not unreasonable Based on these numbers, how well does our application scale It's still hard to say We can quote numbers, but do they mean anything Not really The problem here is that we need a comparison something to hold up against our mythical application so we can judge how well or how poorly our example scales

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