Distributed tool for detecting states and state transitions in remote network management platforms

Patent metadata

Field	Value
Publication number	US-10826766-B2
Application number	US-202016828587-A
Country	US
Kind code	B2
Filing date	Mar 24, 2020
Priority date	Sep 22, 2017
Publication date	Nov 3, 2020
Grant date	Nov 3, 2020

Abstract

A computing system may involve a time-series server device and computing devices. The time-series server device may be configured to: receive and store pre-defined trigger configurations; receive and store time-series data, wherein the pre-defined trigger configurations define states and/or state transitions for the received time-series data; apply, by way of a trigger engine, the pre-defined trigger configurations to the received time-series data to determine observed states and/or state transitions in the time-series data; and store, in transition storage, representations of the observed states and/or state transitions. One or more applications operating on computing devices may be configured to: transmit the pre-defined trigger configurations to the time-series server; transmit a stream of the time-series data to the time-series server; and repeatedly poll and receive, by way of a plurality of worker threads, the representations of the observed states and/or state transitions from the transition storage.

First claim

What is claimed is:

1. A computing system comprising:
a processor; and
a memory, accessible by the processor, the memory storing instructions that, when executed by the processor, cause the processor to perform operations comprising:
receiving a set of trigger configurations that define a plurality of states of a process utilization metric for a computing device and one or more transitions between the plurality of states, wherein the one or more transitions comprises a particular transition in which the process utilization metric crosses a threshold value;
receiving a set of time-series data comprising measurements of the process utilization metric for the computing device;
applying the set of trigger configurations to the received set of time-series data to identify observed states and observed transitions in the received set of time-series data, wherein the observed transitions includes the particular transition in which the process utilization metric crosses the threshold value; and
storing, in the memory, representations of the observed states and observed transitions.

2. The computing system of claim 1 , wherein the computing device is one or a plurality of computing devices within a managed network, and wherein the managed network if managed by a remote network management platform.

3. The computing system of claim 1 , wherein applying the set of trigger configurations to the received set of time-series data to identify observed states and observed transitions in the received set of time-series data comprises determining the particular transition has occurred when the process utilization metric is on one side of the threshold value for m measurements out of a previous n consecutive measurements.

4. The computing system of claim 1 , the operations comprising:
receiving a second set of trigger configurations that define a second plurality of states of a memory utilization metric for the computing device and one or more second transitions between the second plurality of states, wherein the one or more second transitions comprises a second particular transition in which the memory utilization metric crosses a second threshold value.

5. The computing system of claim 4 , wherein applying the set of trigger configurations to the received set of time-series data comprises:
synchronously applying the set of trigger configurations, wherein the set of trigger configurations are applied to a plurality of respective individual measurements in the received set of time-series data; and
asynchronously applying the second set of the trigger configurations, wherein the second set of trigger configurations are applied to a second plurality of respective individual measurements in the received set of time-series data.

6. The computing system of claim 1 , wherein applying the set of trigger configurations to the received set of time-series data comprises:
in response to the process utilization metric being below the threshold value, synchronously applying the set of trigger configurations to the received set of time-series data to identify the observed states and the observed transitions.

7. The computing system of claim 1 , wherein applying the set of trigger configurations to the received set of time-series data comprises:
in response to the process utilization metric being above the threshold value, asynchronously applying the set of trigger configurations to the received set of time-series data to identify the observed states and the observed transitions.

8. The computing system of claim 1 , wherein each of the set of trigger configurations is respectively associated with a callback function, and wherein applying the set of trigger configurations to the received set of time-series data comprises calling the callback function associated with a particular trigger configuration of the set of trigger configurations as a result of a particular state or the particular transition of the particular trigger configuration being observed.

9. The computing system of claim 8 , wherein the particular trigger configuration of the set of trigger configurations is based on a linear prediction of a trend in the received set of time-series data estimating a future time at which the process utilization metric is expected to cross the threshold value.

10. A method comprising:
receiving a set of trigger configurations that define a plurality of states of a process utilization metric for a computing device and one or more transitions between the plurality of states, wherein the one or more transitions comprises a particular transition in which the process utilization metric crosses a threshold value;
receiving a set of time-series data comprising measurements of the process utilization metric for the computing device;
applying the set of trigger configurations to the received set of time-series data to identify observed states and observed transitions in the received set of time-series data, wherein the observed transitions includes the particular transition in which the process utilization metric crosses the threshold value; and
storing, in the memory, representations of the observed states and observed transitions.

11. The method of claim 10 , wherein applying the set of trigger configurations to the received set of time-series data to identify observed states and observed transitions in the received set of time-series data comprises determining the particular transition has occurred when the process utilization metric is on one side of the threshold value for m measurements out of a previous n consecutive measurements.

12. The method of claim 10 , wherein applying the set of trigger configurations to the received set of time-series data comprises:
in response to the process utilization metric being below the threshold value, synchronously applying the set of trigger configurations to the received set of time-series data to identify the observed states and the observed transitions.

13. The method of claim 10 , wherein applying the set of trigger configurations to the received set of time-series data comprises:
in response to the process utilization metric being above the threshold value, asynchronously applying the set of trigger configurations to the received set of time-series data to identify the observed states and the observed transitions.

14. The method of claim 10 , comprising:
receiving a second set of trigger configurations that define a second plurality of states of a memory utilization metric for the computing device and one or more second transitions between the second plurality of states, wherein the one or more second transitions comprises a second particular transition in which the memory utilization metric crosses a second threshold value;
wherein applying the set of trigger configurations to the received set of time-series data comprises:
synchronously applying the set of trigger configurations, wherein the set of trigger configurations are applied to a plurality of respective individual measurements in the received set of time-series data; and
asynchronously applying the second set of the trigger configurations, wherein the second set of trigger configurations are applied to a second plurality of respective individual measurements in the received set of time-series data.

15. The method of claim 10 , wherein each of the set of trigger configurations is respectively associated with a callback function, and wherein applying the set of trigger configurations to the received set of time-series data comprises calling the callback function associated with a particular trigger configuration of the set of trigger confi

Assignees

• Servicenow Inc

Inventors

• Baxa Olivier
• Wang Paul
• Seguin Vincent

Classifications

• H04L41/0895

  Configuration of virtualised networks or elements, e.g. virtualised network function or OpenFlow elements · CPC title

• H04L41/0894

  Policy-based network configuration management · CPC title

• H04L41/0816 Primary

  the condition being an adaptation, e.g. in response to network events · CPC title

• H04L43/0817 Primary

  by checking functioning · CPC title

• H04L41/0681

  Configuration of triggering conditions · CPC title