SUPPORT ACTION BASED SELF LEARNING AND ANALYTICS FOR DATACENTER DEVICE HARDWARE/FIRMARE FAULT MANAGEMENT

• US 20170039105A1
• Filed: 04/30/2014
• Published: 02/09/2017
• Est. Priority Date: 04/30/2014
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company), Suhas Shivanna
• Inventors: Polackal, Bino Joseph, Ahmed, Afreen, Shivanna, Suhas
• Granted Patent: US 10,157,100 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/142 Reconfiguring to eliminate the error group management mechanisms in a peer-to-peer network H04L67/1044
• CPC Class Codes: G06F 2201/805 Real-time
• CPC Class Codes: G06F 2201/82 Solving problems relating to consistency ensuring consistency in mirrored systems G06F11/2064
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 50/00 Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism healthcare informatics G16H
• CPC Class Codes: G06Q 50/205 Education administration or guidance

Abstract

Techniques for support activity based self learning and analytics for datacenter device hardware/firmware fault management are described. In one example, a service event including a unique service event ID, a set of prioritized cause and support engineer actions/recommendations and associated unique support engineer action codes are generated for the support engineer upon detecting a hardware/firmware failure event. Support engineer actions taken by the support engineer upon completing the service event are then received. The support engineer actions are then analyzed using the set of prioritized cause and support engineer actions/recommendations and FRU configuration information before and after servicing the hardware/firmware failure. Any potential errors resulting from the support engineer actions are then determined and notified at real-time to the support engineer based on the outcome of the analysis. Any needed updates to the set of prioritized cause and support engineer actions/recommendations are then recommended.

Claims

1. A system, comprising:

a support engineer'"'"'s computing device;

a centralized event and action code repository (CEACR); and

a managed device coupled to the support engineer'"'"'s computing device and the CEASR, wherein the managed device, comprising;

a self learning manager;

an event and action code repository snap shot (EACRS);

an error analysis engine; and

a hardware/firmware layer coupled to the error analysis engine, wherein the self learning manager and the error analysis engine are to;

generate a service event including a unique service event ID, and associated set of prioritized cause and support engineer'"'"'s actions/recommendations and unique support engineer action codes for a support engineer;

receive support engineer actions taken by the support engineer upon completing the service event;

analyze support engineer actions taken by the support engineer;

determine any potential errors resulting from the support engineer actions and notifying the support engineer based on the outcome of the analysis; and

dynamically recommend any needed updates to the set of prioritized cause and support engineer actions/recommendations based on the outcome of the analysis.

2. The system of claim 1, wherein the self learning manager and the error analysis engine are further to:

store the support engineer actions taken by the support engineer in a service action history repository (SAHR).

3. The system of claim 1, wherein the self learning manager along with the error analysis engine are further configured to:

send the unique service event ID and the support engineer actions taken by the support engineer along with a confirmation of completion of the service event to a remote datacenter to enable automatic closure of a service event.

4. The system of claim 1, wherein the self learning manager and the error analysis engine are further to:

define a unique support engineer'"'"'s action code for each possible support engineer'"'"'s actions associated with hardware/firmware failure events; and

store the mapping table including each of support engineer'"'"'s actions and associated unique support engineer'"'"'s action codes in EACRS and/or a central event and action code repository (CEACR) residing in a service provider cloud.

5. The system of claim 1, wherein the self learning manager and the error analysis engine are to analyze support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations, comprising the step of:

analyzing support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations using statistical techniques.

6. The system of claim 1, wherein the set of prioritized cause and support engineer actions/recommendations, comprises:

a combination of field replaceable unit (FRU) replacements and/or non FRU replacement procedures, and further the order of FRU replacement cause/recommendations are based on an annual failure rate of associated FRUs.

7. The system of claim 6, wherein the self learning manager and the error analysis engine are to generate the service event including a unique service event ID, a set of prioritized cause/support engineer actions/recommendations, associated unique support engineer action codes, location identifier as to where the hardware/firmware failure event occurred or detected, FRU vital information for the support engineer using the mapping table, wherein the vital information includes FRU serial number, FRU port number and/or FRU time of failure.

8. A non-transitory computer readable storage medium comprising a set of instructions executable by at least one processor resource to:

generating a service event including a unique service event ID, a set of prioritized cause and support engineer actions/recommendations and associated unique support engineer action codes for a support engineer;

receiving support engineer actions taken by the support engineer upon completing the service event;

analyzing support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations;

determining any potential errors resulting from the support engineer actions taken by the support engineer and notifying the support engineer based on the outcome of the analysis by the self learning manager; and

dynamically recommending any needed updates to the set of prioritized cause and support engineer actions/recommendations based on the outcome of the analysis by the self learning manager.

9. The non-transitory computer readable storage medium of claim 8, comprising:

storing the support engineer actions taken by the support engineer in a non-volatile memory for off-line analytics.

10. The non-transitory computer readable storage medium of claim 8, comprising:

sending the service event ID and the support engineer actions taken by the support engineer along with a confirmation of completion of the service event to a remote datacenter.

11. A method for support activity based self learning and analytics for datacenter device hardware/firmware fault management, comprising:

generating a service event including a unique service event ID, a set of prioritized cause and support engineer actions/recommendations and associated unique support engineer action codes for a support engineer using a mapping table including each of support engineers actions and associated unique support engineer action codes by an error analysis engine upon detecting a hardware/firmware failure event via a hardware/firmware layer associated with a managed device in the datacenter;

receiving support engineer actions taken by the support engineer upon completing the service event via a support engineer'"'"'s user interface (UI) by a self learning manager;

analyzing support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations and field replaceable unit (FRU) configuration information before and after servicing the hardware/firmware failure by the self learning manager;

determining any potential errors resulting from the support engineer actions taken by the support engineer and notifying the support engineer based on the outcome of the analysis by the self learning manager; and

dynamically recommending any needed updates to the set of prioritized cause and support engineer actions/recommendations based on the outcome of the analysis by the self learning manager.

12. The method of claim 11, further comprising:

storing the support engineer actions taken by the support engineer in a non-volatile memory for off-line analytics.

13. The method of claim 11, further comprising:

sending the service event ID and the support engineer actions taken by the support engineer along with a confirmation of completion of the service event to a remote datacenter.

14. The method of claim 11, further comprising:

defining a unique support engineer'"'"'s action code for each possible support engineers actions/recommendations associated with hardware/firmware failure events;

maintaining the mapping table including each of support engineer'"'"'s actions and associated unique support engineer'"'"'s action codes in the EACSR and/or in the central event and action code repository (CEACR) residing in a service provider cloud.

15. The method of claim 11, wherein analyzing support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations, comprises:

analyzing support engineer actions taken by the support engineer with the set of prioritized cause and support engineer actions/recommendations using statistical techniques.