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Manageability and Flexibility
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Manageability refers to the set of services that ensures the continued integrity, or correctness, of the component application. It includes security, concurrency control, and server management. A metric example of manageability would be the number
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2: Common Architectures and Protocols
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of staff hours per month required to perform normal upgrades. Server management refers to the set of system facilities used for starting and stopping the server, installing new components, managing security permissions, and performing other tasks. These services can be implemented through a best of breed third-party product approach, integrated in a middle-tier server product, or implemented through operating system facilities. Flexibility is the key to an available, reliable, and scalable application. Flexibility can be improved through location independence of application code. An example of flexibility would be a JEE system that uses internationalization code and property files to allow changes in the presentation language (for example, English to German). Regarding metrics, there is no standard way of measuring flexibility. The business measure is the cost of change in time and money, but this depends on what types of change can be anticipated. As flexibility, reliability, and availability are increased, manageability can suffer. Flexibility is also essential for keeping pace with rapid change. It s enhanced when the middle-tier technology is a component-based solution that easily accommodates the integration of multiple technologies. Independence from hardware, operating system, and language creates the most adaptable and portable solutions. The connectivity mechanisms to multiple data sources also increase adaptability. Fortunately, this area is one in which several solutions are available, including the database connection standards (ODBC and JDBC), native database drivers, messaging, remote procedure calls (to database stored procedures), object request brokers, and database gateways.
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Response time and response ratio are important to an application. The most important task resulting in good performance is to identify and control expensive calls. The architect should state target performance criteria before implementing within a production environment. For example, the first visible response in any application browser view when the application is under maximum specified load must occur in less than 3 seconds, 95 percent of the time. Measurement is made at the enterprise s external firewall. Today, when measuring performance, the architect must consider and attempt to quantify the cost of an operation (data or computational) which can involve a myriad of servers across a sea of network connections before finally returning a response view to the user requestor. Today, performance is the ability to execute functions fast enough to meet goals. Response time and response ratio (the time it takes to respond/time it takes to perform the function) are important to an application. Both figures should be as
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low as possible, but a ratio of 1 is the target. For example, suppose a user requests functionality requiring a great deal of processing or database searching and it takes a minute to process. The user will not see a result for a minute seemingly a long time to wait, but if the result can be viewed in 1 minute plus 20 seconds (a response ratio of 1.3333), that is still good performance. Alternatively, suppose that the processing takes only 1 second but the user does not see the result for 20 seconds (response ratio of 20); that is not good performance.
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Capacity is a measure of the extent or ability of the computer hardware, software, and connection infrastructure resources over some period of time. A typical capacity concern of many enterprises is whether resources will be in place to handle an increasing number of requests as the number of users or interactions increases. The aim of the capacity planner is to plan so well that new capacity is added just in time to meet the anticipated need but not so early that resources go unused for a long period. The successful capacity planner is one that makes the trade-offs between the present and the future that overall prove to be the most cost efficient. No benchmark can predict the performance of every application. It is easy to find two applications and two computers with opposite rankings, depending on the application; therefore, any benchmark that produces a performance ranking must be wrong on at least one of the applications. However, memory references dominate most applications. For example, there is considerable difference between a kernel-like information retrieval product and one that performs complex business rules of a heuristic trading system that does a matrix multiply. Most kernels are code excerpts. The work measure is typically something like the number of iterations in the loop structure, or an operation count (ignoring precision or differing weights for differing operations). It accomplishes a petty but useful calculation and defines its work measure strictly in terms of the quality of the answer instead of what was done to get there. Although each iteration is simple, it still involves more than 100 instructions on a typical serial computer and includes decisions and variety that make it unlikely to be improved by a hardware engineer.
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