Abstract: Processes, computer-readable media, and machines are disclosed for reducing a likelihood that active functional components fail in a computing system. An active monitoring component receives metrics associated with different active functional components of a computing system. The different active functional components contribute to different functionalities of the system. Based at least in part on the metrics associated with a particular active functional component, the active monitoring component determines that the particular active functional component has reached a likelihood of failure but has not failed. In response to determining that the particular active functional component has reached the likelihood of failure but has not failed, the active monitoring component causes a set of actions that are predicted to reduce the likelihood of failure.

Abstract: Techniques for adaptive trace logging include, in one embodiment, obtaining input data on trace logging behavior and computing resources used by trace logging. Based on the obtained input data, an adaptive trace logging module automatically takes action at runtime to reduce the amount of computing resources consumed by tracing logging. For example, the action taken may include decreasing a trace logging level of an executing software program to reduce the number of trace logging messages added to a trace log. In another embodiment, the techniques include detecting a condition of an executing software program that warrants a change to a trace logging level of the executing program. The adaptive trace logging module automatically changes the trace logging level of the executing program as-needed for the detected condition. For example, the adaptive trace logging module may increase the trace logging level of an executing program upon detecting a deadlock or other abnormal condition of the executing program.

Abstract: Processes, computer-readable media, and machines are disclosed for reducing a likelihood that active functional components fail in a computing system. An active monitoring component receives metrics associated with different active functional components of a computing system. The different active functional components contribute to different functionalities of the system. Based at least in part on the metrics associated with a particular active functional component, the active monitoring component determines that the particular active functional component has reached a likelihood of failure but has not failed. In response to determining that the particular active functional component has reached the likelihood of failure but has not failed, the active monitoring component causes a set of actions that are predicted to reduce the likelihood of failure.

Abstract: Processes, computer-readable media, and machines are disclosed for reducing a likelihood that active functional components fail in a computing system. An active monitoring component receives metrics associated with different active functional components of a computing system. The different active functional components contribute to different functionalities of the system. Based at least in part on the metrics associated with a particular active functional component, the active monitoring component determines that the particular active functional component has reached a likelihood of failure but has not failed. In response to determining that the particular active functional component has reached the likelihood of failure but has not failed, the active monitoring component causes a set of actions that are predicted to reduce the likelihood of failure.

Abstract: Processes, computer-readable media, and machines are disclosed for reducing a likelihood that active functional components fail in a computing system. An active monitoring component receives metrics associated with different active functional components of a computing system. The different active functional components contribute to different functionalities of the system. Based at least in part on the metrics associated with a particular active functional component, the active monitoring component determines that the particular active functional component has reached a likelihood of failure but has not failed. In response to determining that the particular active functional component has reached the likelihood of failure but has not failed, the active monitoring component causes a set of actions that are predicted to reduce the likelihood of failure.

Abstract: Described herein are techniques for adaptively managing timers that are used in various layers of a node. In many cases, the number of timers that occur in the system is reduced by proactively and reactively adjusting values of the timers based on conditions affecting the system, thereby making such a system to perform significantly better and more resiliently than otherwise.

Abstract: Techniques for adaptive trace logging include, in one embodiment, obtaining input data on trace logging behavior and computing resources used by trace logging. Based on the obtained input data, an adaptive trace logging module automatically takes action at runtime to reduce the amount of computing resources consumed by tracing logging. For example, the action taken may include decreasing a trace logging level of an executing software program to reduce the number of trace logging messages added to a trace log. In another embodiment, the techniques include detecting a condition of an executing software program that warrants a change to a trace logging level of the executing program. The adaptive trace logging module automatically changes the trace logging level of the executing program as-needed for the detected condition. For example, the adaptive trace logging module may increase the trace logging level of an executing program upon detecting a deadlock or other abnormal condition of the executing program.

Abstract: Described herein are techniques for adaptively managing timers that are used in various layers of a node. In many cases, the number of timers that occur in the system is reduced by proactively and reactively adjusting values of the timers based on conditions affecting the system, thereby making such a system to perform significantly better and more resiliently than otherwise.