Abstract: The real time availability of a group of network elements is determined based upon both a real time availability value for each of the network elements and cooperation relationships between the network elements. The cooperation relationships reflect both the topological relationships between the network elements, i.e., how the network elements are connected, and the extent to which network elements interact with each other effectively. For relatively small groups of network elements, where the cooperation relationships are not overly complex, the real time availability is determined directly from the real time availability value for each of the network elements and cooperation relationships between the network elements. Decomposition and recombination are used to determine the real time availability of large groups of network elements based on specific formulas for basic network element topology models.

Abstract: An approach is provided for characterizing the dynamic availability behavior of network elements using metrics that indicate patterns of availability of a network element over time. The metrics provide an indication of stability and instability of a network element and include one stability metric and three instability metrics. The stability metric is a stability order that indicates how long an operational state holds with respect to a prior operational state. The instability metrics include an instability order, a repeatability order and a multiplicity order. The instability order indicates how quickly a network element changes operational states. The repeatability order indicates short-term instability within a vicinity of a change in operational state. The multiplicity order indicates long-term instability. The metrics may be used alone, or in various combinations, by network management applications to better manage network elements.

Abstract: Approaches are described for capturing illegal and undesired behavior for network components and for interactions between components. One approach comprise specifying one or more states and state transitions for one or more components or interactions between components, wherein at least one composite state transition is specified, and generating a notification corresponding to a specified state or state transition when the state or state transition occurs. These approaches provide a holistic view of the entire state space of a network or component or component interactions, and allow undesired or illegal states to be captured in a timely manner.

Abstract: Methods and mechanisms for preventing deadlock in a policy-based computer system are disclosed. In one embodiment, the method includes various machine-implemented steps, such as a step of receiving policy information based upon decisions about system configurations. At least one policy includes at least one action that has a post condition. Satisfaction of the post condition triggers at least one subsequent action or decision. The policy includes an indication whether the at least one subsequent action or decision should proceed if the action fails to satisfy the post condition. The at least one action is executed. Then, if the action fails to satisfy the post condition, a determination is made whether the at least one subsequent action or decision can proceed based upon the indication.

Abstract: A method is disclosed for network knowledge-based diagnosis comprising the machine-implemented steps of creating and storing one or more symptoms, wherein each symptom comprises a set of information elements that represent one or more network events that may be potentially received from a computer network; associating a weight value with each information element of the one or more symptoms; associating a confidence time interval value with each of the one or more symptoms; receiving one or more network events from elements in the computer network; and determining a set of one or more candidate diagnoses of a problem indicated by the received network events, by (a) selecting one or more symptoms that include at least one of the received network events and (b) using functions that map the selected symptoms to one or more candidate diagnoses, based on all weight values of events in the selected symptoms and the confidence time interval values of the selected symptoms.