Web server type - 324 CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY
324 CHAPTER 12 TRENDING, FORECASTING, AND CAPACITY PLANNING In addition, you should include any other significant resource in your environment. For example, several of my larger IBM WebSphere clients use the MQSeries messaging infrastructure to communicate with a mainframe. In this case, the mainframe performance and the ability of MQSeries to transfer messages between the application server and the mainframe are both key to the performance of the application. MQSeries includes performance metrics such as message time in a queue, queue depth, and thread pool utilization. The methodology is to construct a model for each of these metrics that describes its current and historical behavior and then review the resource s history to discern any patterns. The difficult part is that you need to build daily, weekly, monthly, and annual models, and then look back at the historical data to determine if any trends exist against those models. For example, consider the following scenario, identified by analyzing the CPU utilization of an application server: every nonholiday business day over the past month, the CPU has been running at 65 percent utilization between 8:00 AM and 9:00 AM (part of the daily model). If this is the case, then how was the CPU performing three or six months ago? Furthermore, what is the pattern of behavior between six months ago and today? Does this pattern present a valid trend, or are your observations part of a monthly or annual model? Assume that this example is an intranet application in which users log in between 8:00 AM and 9:00 AM, and for which the only annual patterns that affect user utilization are user vacations, so the fluctuations are minor. Looking back six months ago, the CPU was at 30 percent for the time period in question, and during the last six months, the staff increased by 10 percent. This type of utilization increase will have an impact on the CPU, but it should not cause CPU utilization to nearly double. The CPU utilization growth pattern was relatively flat until four months ago, when it mysteriously increased by 25 percent and gradually increased another 5 percent, until it reached its current 65 percent utilization. Looking back at changes that occurred four months ago, we learn that the authentication mechanism was upgraded from a basic, file-based authentication to an LDAP server. Furthermore, the entire staff increase occurred between four months ago and today, accounting for the additional 5 percent increase. By defining an annual CPU utilization model, we knew that this change in behavior was not because of seasonal changes throughout the year. By defining a weekly model, we learned that CPU utilization was slightly higher on Monday and slightly lower on Friday, but the changes were global and did not change any weekly pattern. The daily model demonstrated conclusively that the 8:00 AM to 9:00 AM CPU utilization increased consistently, regardless of all other trends. And finally, the trend analysis pointed to the time frame that caused the problem. Define the following models for each resource: Daily Weekly Monthly Annual Daily models examine resource utilization behaviors during your standard business days. If you run an e-commerce site, then every day is a business day, but if you maintain an intranet application, then your business days might be Monday through Friday. The goals of defining this model and analyzing its historical behavior are to identify peak daily usage times and detect large-scale and global changes that affect the performance of your environment. Daily models and trends need to be carefully analyzed against business environmental factors, such
Note: If you are looking for cheap and reliable webhost to host and run your mysql application check mysql web server services.