METHOD AND APPARATUS FOR HARMONIZING THE GATHERING OF DATA AND ISSUING OF COMMANDS IN AN AUTONOMIC COMPUTING SYSTEM USING MODEL-BASED TRANSLATION

• US 20070288467A1
• Filed: 06/07/2006
• Published: 12/13/2007
• Est. Priority Date: 06/07/2006
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Strassner, John C., Menich, Barry J.
• Application Number:
  US11/422,642
• Publication Number:
  US 20070288467A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 41/0213 Standardised network management protocols, e.g. simple network management protocol [SNMP]
• CPC Class Codes: H04L 41/0226 Mapping or translating multiple network management protocols
• CPC Class Codes: H04L 41/0233 Object-oriented techniques, for representation of network management data, e.g. common object request broker architecture [CORBA]

Abstract

A system includes vendor specific converters [210, 215, 220, 225] to gather vendor-specific data from sensors [235, 240] and convert to normalized data. The vendor-specific data comprises at least a first input and a second input. The first input corresponds to a first language and the second input corresponds to a second language, where the first language is different from the second language. The vendor specific converters [210, 215, 220, 225] receive normalized commands and issue vendor-specific commands to a managed resource [165]. A converter module [230] receives the normalized data and converts to an output corresponding to a common language. The converter module [230] also receives and outputs normalized commands corresponding to the common language. Semantic processing logic [255] receives the normalized data and matches the normalized data against a predetermined set of knowledge bases and generates a new object to send to an autonomic manager [160].

Claims

1. A method, comprising:

accepting at least a first input and a second input from a plurality of sensors, the first input corresponding to a first language, and the second input corresponding to a second language, wherein the first language is different from the second language;

performing at least one computational function on the at least the first input and the second input according to at least one of installed policies and an input from an autonomic manager; and

harmonizing the at least the first input and the second input into an output corresponding to a common language for use by the autonomic manager.

2. The method of claim 1, wherein the common language comprises Extensible Markup Language (“

XML”

).

3. The method of claim 1, the at least one computational function comprising determining, for the at least the first input and the second input, at least one of:

a protocol delivering data, a vendor, an identity of an entity, and a version of a device operating system that is used to perform at least one of programming and controlling the entity.

4. The method of claim 1, the at least one computational function comprising comparing the first input and the second input to predetermined patterns corresponding to a particular vendor.

5. The method of claim 1, the accepting comprising receiving the at least a first input and a second input from a managed resource.

6. The method of claim 1, further comprising matching the output against an information model and constructing at least one new object based on a successful match.

7. The method of claim 6, wherein in response to determining that the at least one new object does not need semantic augmentation, matching the at least one new object against a current task being performed and in response to determining that the current task needs more data, defining queries to retrieve additional data for the at least one new object based on the content of the at least one new object.

8. The method of claim 6, wherein in response to determining that the at least one new object needs semantic augmentation, performing at least one semantic concept similarity matching function and a semantic markup on the at least one new object.

9. The method of claim 6, wherein the set of object construction and semantic concept similarity matching differentiates between an absolute match corresponding to at least one particular command, and a non-absolute match.

10. A system, comprising:

vendor specific converters to;

gather vendor-specific data from a plurality of sensors and convert the vendor-specific data to normalized data, the vendor-specific data comprising at least a first input and a second input, the first input corresponding to a first language and the second input corresponding to a second language, wherein the first language is different from the second language, and

receive normalized commands and issue vendor-specific commands to a managed resource;

a converter module to;

receive the normalized data and convert the normalized data to an output corresponding to a common language, and

receive normalized commands corresponding to the common language and output the normalized commands; and

semantic processing logic to receive the output and match the output against a predetermined set of knowledge bases and generate at least one new object to send to an autonomic manager.

11. The system of claim 10, wherein the set of knowledge bases comprises an information model, a least one ontology, and a semantic model.

12. The system of claim 10, wherein the common language is Extensible Markup Language (“

XML”

).

13. The system of claim 10, further comprising a managed resource to provide the first input and the second input.

14. The system of claim 10, further comprising object construction logic to match the output against an information model and construct the at least one new object based on a successful match.

15. The system of claim 14, the object construction logic comprising a processing element, wherein in response to determining that the at least one new object does not need semantic augmentation, the processing element matches the at least one new object against a current task being performed and in response to determining that the task needs more data, defines queries to retrieve additional data for the at least one new object.

16. The system of claim 14, the object construction logic comprising a processing element, wherein in response to determining that the at least one new object needs semantic augmentation, the processing element performs at least one semantic concept similarity matching function and a semantic markup on the new object.

17. The system of claim 16, wherein the processing element determines at least one concept that is similar to at least one of the first input and the second input, to determine additional meaning and context of the at least one of the first input and the second input.

18. An apparatus, comprising:

vendor specific converters to;

gather vendor-specific data from a plurality of sensors and convert the vendor-specific data to normalized data, the vendor-specific data comprising at least a first input and a second input, the first input corresponding to a first language and the second input corresponding to a second language, wherein the first language is different from the second language, and

receive normalized commands and issue vendor-specific commands to a managed resource; and

a semantic model converter to;

receive the normalized data,

output the normalized commands, and

match the normalized data against a predetermined set of knowledge bases and generate a new object to send to an autonomic manager.

19. The apparatus of claim 18, wherein the set of knowledge bases comprises an information model, a least one ontology, and a semantic model.

20. The apparatus of claim 18, further comprising object construction logic to match the normalized data against an information model and construct the new object based on a successful match.