Kalman filtering for grid computing telemetry and workload management

• US 7,716,535 B2
• Filed: 06/08/2006
• Issued: 05/11/2010
• Est. Priority Date: 06/08/2006
• Status: Active Grant

• Current Assignee: Oracle America, Inc. (Oracle Corporation)
• Original Assignee: Oracle America, Inc. (Oracle Corporation)
• Inventors: Cunningham, Helen Ann, Demoff, Jeffrey Scott, Wolff, Alan Scott
• Primary Examiner(s): Iqbal; Nadeem
• Application Number:
  US11/450,723
• Publication Number:
  US 20070288626A1
• Time in Patent Office: 1,433 Days
• Field of Search: 714/48
• US Class Current: 714/48
• CPC Class Codes: G08C 25/00 Arrangements for preventing or correcting errors; Monitoring arrangements
• CPC Class Codes: H04Q 9/00 Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Abstract

In one embodiment, a method and apparatus for Kalman filtering for grid computing telemetry and workload management is disclosed. The method includes monitoring telemetry data at a node of a grid computing engine with a Kalman filter, determining whether the monitored telemetry data is outside of a bounds of a predictive model of the Kalman filter, signaling an error condition for the node if the monitored telemetry data is outside of the bounds, and addressing the error condition by correcting a problem experienced at the node corresponding to the error condition. Other embodiments are also disclosed.

Claims

1. A method comprising:

monitoring telemetry data at a node of a grid computing engine with a Kalman filter;

determining whether the monitored telemetry data is outside of a bounds of a predictive model of the Kalman filter;

signaling an error condition for the node if the monitored telemetry data is outside of the bounds; and

addressing the error condition by correcting a problem experienced at the node corresponding to the error condition.

2. The method of claim 1, wherein the Kalman filter recursively adjusts the predictive model based on the monitored telemetry data.

3. The method of claim 1, wherein the determining whether the monitored telemetry data is outside of the bounds is performed in a real-time manner by the Kalman filter.

4. The method of claim 1, wherein the Kalman filter does not save the monitored telemetry data.

5. The method of claim 1, wherein the telemetry data includes at least one of a disk space of the node, a temperature of the node, a central processing unit (CPU) usage of the node, a transaction load of the node, and a network input/output (I/O) of the node.

6. The method of claim 5, wherein more than one Kalman filter each monitors a specific type of the telemetry data at the node.

7. The method of claim 6, further comprising utilizing a monitoring console to depict states of each of the more than one Kalman filter.

8. The method of claim 1, wherein the Kalman filter is located at the node.

9. The method of claim 1, wherein the Kalman filter is located at a server.

10. An article of manufacture, comprising a machine-readable storage medium including data that, when accessed by a machine, cause the machine to perform operations comprising:

monitoring telemetry data at a node of a grid computing engine with a Kalman filter;

determining whether the monitored telemetry data is outside of a bounds of a predictive model of the Kalman filter;

signaling an error condition for the node if the monitored telemetry data is outside of the bounds; and

addressing the error condition by correcting a problem experienced at the node corresponding to the error condition.

11. The article of manufacture of claim 10, wherein the Kalman filter recursively adjusts the predictive model based on the monitored telemetry data.

12. The article of manufacture of claim 10, wherein the determining whether the monitored telemetry data is outside of the bounds is performed by the Kalman filter in a real-time manner.

13. The article of manufacture of claim 10, wherein the telemetry data includes at least one of a disk space of the node, a temperature of the node, a central processing unit (CPU) usage of the node, a transaction load of the node, and a network input/output (I/O) of the node.

14. The article of manufacture of claim 10, wherein the Kalman filter is located at the node.

15. The article of manufacture of claim 10, wherein the Kalman filter is located at a server.

16. An apparatus, comprising:

a grid computing engine having a node; and

a Kalman filter associated with the node to;

monitor telemetry data at the node;

determine whether the monitored telemetry data is outside of the bounds of a predictive model of the Kalman filter; and

signal an error condition for the node if the telemetry data is outside of the bounds.

17. The apparatus of claim 16, further comprising an automated response system to address the error condition by correcting a problem experienced at the node corresponding to the error condition.

18. The apparatus of claim 16, wherein the telemetry data includes at least one of a disk space of the node, a temperature of the node, a central processing unit (CPU) usage of the node, a transaction load of the node, and a network input/output (I/O) of the node.

19. The apparatus of claim 18, wherein the Kalman filter recursively adjusts the predictive model based on the monitored telemetry data.

20. The apparatus of claim 16, further comprising a monitoring console to depict a state of the Kalman filter.