Measuring Scalability in Java

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Measuring Scalability
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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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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
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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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Figure 1-3 Little's law
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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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Table 1-3 Sample Application Response and Throughput Times Concurrent Users 1 50 100 150 200 Average Response Time (ms) 10 500 1200 2200 4000 Throughput (tps) 100 100 83333 68182 50
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One good comparison is against a "linearly scalable" version of our application, by which I mean an application that continues to do exactly the same amount of work per second no matter how many clients use it This is not to say the average response time will remain constant no way In fact, it will increase, but in a perfectly predictable manner However, our throughput will remain constant Linearly scalable applications are perfectly scalable in that their performance degrades at a constant rate directly proportional to their demands If our application is indeed linearly scalable, we'll see the numbers shown in Table 1-4 Notice that our performance degrades in a constant manner: The average response time is ten times the number of concurrent users However, our throughput is constant at 100 tps To understand this data better, and how we can use it in a comparison with our original mythical application results, let's view their trends in graph form Figure 1-4 illustrates average response time as a function of the number of concurrent users; Figure 1-5 shows throughput as a function of the number of users These graphs also compare our results with results for an idealized system whose response time increases linearly with the number of concurrent users Figure 1-4 Scalability from the client's point of view
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