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A process moves into the running state when it is chosen for execution. The process's instructions are executed by one of the CPUs (or cores) of the system. There is at most one running process per CPU or core. A process can run in either of the two modes, namely kernel mode or user mode. [1] [2]
The context switch loads the process into the processor and changes the state to "running" while the previously "running" process is stored in a "waiting" state. If a process in the "running" state needs to wait for a resource (wait for user input or file to open, for example), it is assigned the "blocked" state.
Figure 7: State roles in a state transition. In UML, a state transition can directly connect any two states. These two states, which may be composite, are designated as the main source and the main target of a transition. Figure 7 shows a simple transition example and explains the state roles in that transition.
State transitions in executable operational architectural models provide for descriptions of conditions that control the behavior of process events in responding to inputs and in producing outputs. A state specifies the response of a process to events. The response may vary depending on the current state and the rule set or conditions.
Start state q 0: (not shown in the examples below). The start state q 0 ∈ Q is usually represented by an arrow with no origin pointing to the state. In older texts, [2] [4] the start state is not shown and must be inferred from the text. Accepting state(s) F: If used, for example for accepting automata, F ∈ Q is the accepting state. It is ...
The three-state process management model is designed to overcome this problem, by introducing a new state called the BLOCKED state. This state describes any process which is waiting for an I/O event to take place. In this case, an I/O event can mean the use of some device or a signal from another process. The three states in this model are:
Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]
In the state-transition table, all possible inputs to the finite-state machine are enumerated across the columns of the table, while all possible states are enumerated across the rows. If the machine is in the state S 1 (the first row) and receives an input of 1 (second column), the machine will stay in the state S 1.