| In an autonomic computing architecture, the basic management element is a control loop, depicted in Figure 8.1. This acts as manager of the resource through monitoring, analysis, and actions taken on a set of predefined system policies. These control loops, or managers, can communicate and eventually will negotiate with each other and other types of resources within and outside of the autonomic computing architecture. Figure 8.1. An example of a basic autonomic control loop. 
This collects information from the system and makes decisions based on that data and then issues instructions to make adjustments to the system. An intelligent control loop can provide functionality of autonomous computing, such as the following: -
Requesting additional processing cycles when needed. -
Installing software and upgrades. -
Restarting a system after a failure. -
Initiating backups after daily processing. -
Shutting down systems after detection of an intrusion. These are many of the self-managing functions that we have been discussing so far. They will be available in embedded software or system tools. An alternative approach is to install control loops in runtime environments for faster responses and actions. When fully operational, control loops will hide complexity from end-users and IT professionals. A more detailed picture of the structure and components of the autonomous control loop is shown in Figure 8.2. Figure 8.2. A diagram of an intelligent control loop, which facilitates the self-management functions of the autonomous system architecture. 
In Figure 8.2, we see the control loop is divided into two basic subelements: -
The Managed Element— This can be any component in the autonomous system, such as a server, a database, or a file, or it can be numerous related larger elements, such as a cluster of servers, a complete software application, or even a business unit. This means that managed elements are highly scalable. The sensors and effectors control the managed element. -
The Autonomic Manager— This manages the collection, filtering, and reports of the data collected from the element from the sensors. It also analyzes, models if necessary, and learns about the element, gaining knowledge. With this knowledge, it can predict future situations. The planning part provides the structure the mechanism needs for the actions it takes to achieve the desired goals and objectives of the autonomous system. The planning part also uses the predefined policies that establish the goals and objectives. These policies are described in the system. The execute part of the autonomic manager provides control of the commands being accomplished. It will establish whether the commands completed their required actions. The sensors provide the mechanisms to collect data on the state of the element. To trigger the sensors will require a "get" instruction—for example, "get the information of the customer database"—or for the element to change in a material fashion, such as volume or time. An example of the last trigger would be "get the transaction information when the database completes the daily update." The effectors are the mechanisms that change the state of an element. In other words, they act or alter the configuration of the element from the data provided from the sensors. The effectors are a set of software commands, or application programming interfaces (APIs), that alter the element's configuration. |
