Abstract: In some examples, a host name that is known to correspond to a first source component may be identified. The host name may be searched in a second source component. A topological linkage may be inferred between the first and the second source component in response to finding the host name in the second source component.

Abstract: According to an example, a batch processing system is to determine a cluster of related metrics. A stability of a baseline for a metric in the cluster is determined. A frequency of execution of batch jobs for calculating baselines for metrics is determined based on the stability.

Abstract: An example process for aligning time-series datasets includes receiving a first time-series dataset and a second time-series dataset. The first time-series dataset can include a first set of values associated with respective time stamps and the second time-series dataset can include a second set of values associated with respective time stamps. The process also includes determining degrees of variance of the first and second sets of values, and comparing each degree of variance with a threshold. The process also includes selecting among multiple time alignment processes based on the comparisons, and processing the time-series datasets according to the selected process to thereby generate an aligned time-series dataset.

Abstract: Examples relate to calculating normalize metrics. The examples disclosed herein calculate respective normalized first metric values for each of a plurality of first metric values that are on a time scale and respective normalized second metric values for each of the plurality of raw second metric values that are on the time scale, where the plurality of first metric values are associated with a first metric, and the plurality of second metric values are associated with a second metric. An extremum of the normalized first metric value and the normalized second metric value at each time of the time scale is averaged to calculate a plurality of extremum baseline values. Examples herein calculate a plurality of sleeve values of the plurality of extremum baseline values based on a standard deviation of the plurality of extremum baseline values.

Abstract: In some examples, a host name that is known to correspond to a first source component may be identified. The host name may be searched in a second source component. A topological linkage may be inferred between the first and the second source component in response to finding the host name in the second source component.

Abstract: Examples relate to calculating normalize metrics. The examples disclosed herein calculate respective normalized first metric values for each of a plurality of first metric values that are on a time scale and respective normalized second metric values for each of the plurality of raw second metric values that are on the time scale, where the plurality of first metric values are associated with a first metric, and the plurality of second metric values are associated with a second metric. An extremum of the normalized first metric value and the normalized second metric value at each time of the time scale is averaged to calculate a plurality of extremum baseline values. Examples herein calculate a plurality of sleeve values of the plurality of extremum baseline values based on a standard deviation of the plurality of extremum baseline values.

Abstract: An example process for selecting chart scalings for plotting two time-series datasets includes determining a first log difference between a logarithm of a maximum value of a first dataset and a logarithm of a minimum value of the first dataset. The process also includes determining a second log difference between a logarithm of a maximum value of a second dataset and a logarithm of a minimum value of the second dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the first dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the second dataset.

Abstract: According to an example, a batch processing system is to determine a cluster of related metrics. A stability of a baseline for a metric in the cluster is determined. A frequency of execution of batch jobs for calculating baselines for metrics is determined based on the stability.

Abstract: An example process for selecting chart scalings for plotting two time-series datasets includes determining a first log difference between a logarithm of a maximum value of a first dataset and a logarithm of a minimum value of the first dataset. The process also includes determining a second log difference between a logarithm of a maximum value of a second dataset and a logarithm of a minimum value of the second dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the first dataset. The process also includes selecting, based at least in part on the first and second log differences, between a linear chart scaling and a logarithmic chart scaling for the second dataset.

Abstract: An example process for aligning time-series datasets includes receiving a first time-series dataset and a second time-series dataset. The first time-series dataset can include a first set of values associated with respective time stamps and the second time-series dataset can include a second set of values associated with respective time stamps. The process also includes determining degrees of variance of the first and second sets of values, and comparing each degree of variance with a threshold. The process also includes selecting among multiple time alignment processes based on the comparisons, and processing the time-series datasets according to the selected process to thereby generate an aligned time-series dataset.