PATTERN-BASED COMPUTATIONAL HEALTH AND CONFIGURATION MONITORING

• US 20130124713A1
• Filed: 11/10/2011
• Published: 05/16/2013
• Est. Priority Date: 11/10/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Feinberg, Bonnie Nicole, Balasubramaniam, Vinay
• Granted Patent: US 8,918,501 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 11/0769 Readable error formats, e.g. cross-platform generic formats, human understandable formats
• CPC Class Codes: G06F 11/3055 Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction by redundancy in operation G06F11/14; error detection or correction by redundancy in hardware G06F11/16
• CPC Class Codes: G06F 11/3068 where the reporting involves data format conversion
• CPC Class Codes: G06F 11/3086 where the reporting involves the use of self describing data formats, i.e. metadata, markup languages, human readable formats

Abstract

Monitoring knowledge is distilled into platform-nonspecific patterns of high-level language elements compiled into management packs or other rule packs targeting specific platforms. A server health and/or configuration monitoring knowledge compiler accepts distillation document(s) and target-specific information, and generates target-specific rule packs to be consumed by monitoring tools to monitor specific target platforms consistent with the platform-nonspecific monitoring elements. Computational rule pack generation is qualitatively different from manual rule pack creation. Plug-ins tailor the compiler to generate on-premises or cloud-based rule packs. Distillation element examples include monitor alert types, instructional content types, target attributes and tags, monitored item types, event alert types, performance collection types, policy monitor types, and threshold monitor types. Tags on monitoring elements indicate relevant products, locations, product features, monitoring tool features, contributors, etc. Runtime code is shared by multiple rule packs for different target platforms.

Claims

1. A computer system comprising:

a logical processor;

a memory in operable communication with the logical processor;

an SHCMK distillation document residing in the memory and having SHCMK monitoring elements declared in a high-level computer language, the SHCMK monitoring elements providing at least one platform-nonspecific pattern for monitoring at least one of;

computational health, computational configuration;

a target identifier residing in the memory and specifying at least one target platform to be monitored; and

an SHCMK compiler residing in the memory and having code which upon execution will compile the SHCMK distillation document and the target identifier as a collective input, and which will produce as an output a target-specific SHCMK rule pack that is specifically arranged to monitor computational health and/or computational configuration of the target platform.

2. The system of claim 1, wherein the system further comprises a template residing in the memory and containing a mapping from the SHCMK distillation document to a particular identified target.

3. The system of claim 1, wherein the system further comprises the target-specific SHCMK rule pack produced by the SHCMK compiler, the target-specific SHCMK rule pack containing rules for monitoring the target platform.

4. The system of claim 3, wherein the target-specific SHCMK rule pack also includes at least one of the following rule pack components:

a health model;

a monitor code for monitoring a target;

a natural language instructional content for diagnosing health problems in a monitored target;

a natural language instructional content for diagnosing configuration problems in a monitored target;

a natural language instructional content for resolving health problems in a monitored target;

a natural language instructional content for resolving configuration problems in a monitored target;

5. The system of claim 1, wherein at least one of the following conditions is also satisfied:

the SHCMK distillation document monitoring elements are written at least partially in an XML schema language;

the target identifier identifies a SQL server installation;

the rule pack produced will be a database server management pack;

the rule pack produced will be an email server management pack;

the rule pack produced will be a calendaring server management pack;

the rule pack produced will be an intranet server management pack;

the rule pack produced will be an extranet server management pack;

the rule pack produced will be an enterprise software server management pack;

the rule pack produced will be a middleware server management pack.

6. The system of claim 1, further comprising a target definition document residing in the memory and including a target type and a target instance, the target instance being an instance of the target type, and the target instance identifying a target product and a target product version.

7. The system of claim 1, further comprising an SHCMK runtime which provides runtime support code that is shared by and invocable by multiple SHCMK rule packs for different target platforms.

8. A process for use by a user to generate a rule pack, the process comprising the steps of:

indicating by electronic input to an SHCMK compiler machine an SHCMK distillation document configuring a computer-readable memory, the SHCMK distillation document having monitoring elements declared in a high-level computer language, the monitoring elements providing at least one platform-nonspecific pattern for monitoring at least one of;

computational health, computational configuration;

denoting by electronic input to the SHCMK compiler machine a target definition document which identifies at least one target platform to be monitored; and

acquiring an electronic digital target-specific SHCMK rule pack generated computationally by the SHCMK compiler machine from the SHCMK distillation document and the target definition document, the rule pack being specific to a target platform that is identified in the target definition document, the rule pack also including an implementation of a monitoring element of the SHCMK distillation document.

9. The process of claim 8, further comprising at least one of the following tailoring steps prior to the acquiring step:

tailoring the SHCMK compiler machine to generate on-premises rule packs, and wherein the acquired rule pack is an on-premises rule pack;

or

tailoring the SHCMK compiler machine to generate cloud-based rule packs, and wherein the acquired rule pack is a cloud-based rule pack.

10. The process of claim 8, further comprising at least one of the following:

causing the SHCMK compiler machine to generate, from the same monitoring element(s), at least two different rule packs for respective different versions of a particular target product that is identified in the target definition document;

causing the SHCMK compiler machine to generate, from the same monitoring element(s), at least two different rule packs for respective different target products that are identified in the target definition document.

11. The process of claim 8, wherein the implementation of the monitoring element in the acquired rule pack comprises at least one of the following:

server management pack XML code, server management pack script code.

12. A computer-readable non-transitory storage medium configured with data and with instructions that when executed by at least one processor causes the processor(s) to perform a computational process for generating a rule pack, the process comprising the steps of:

obtaining a distillation document configuring a computer-readable memory, the distillation document having monitoring elements declared in a high-level computer language, the monitoring elements providing at least one platform-nonspecific pattern for generating at least one of;

computational rules directed to health, computational rules directed to configuration;

getting a digital target identifier, the target identifier identifying at least one target platform, namely, at least one of the following;

a database server, an email server, a calendaring server, an intranet server, an extranet server, an enterprise software server, a middleware server; and

computationally generating an electronic digital rule pack from the distillation document and the target identifier, the generated rule pack containing human-readable machine-parsable computational rules specific to the target platform.

13. The configured medium of claim 12, wherein the distillation document declares at least one of the following monitoring elements:

a monitor alert type which defaults to autoresolution;

a monitor alert type which defaults to alert on warning;

a textual instructional content type;

a target attributes group;

a tag and version attributes group;

a monitored item type;

an event alert type;

a performance collection type;

a policy monitor type;

a threshold monitor type.

14. The configured medium of claim 13, wherein the distillation document declares at least three of the listed monitoring elements.

15. The configured medium of claim 13, wherein the distillation document declares at least six of the listed monitoring elements.

16. The configured medium of claim 12, wherein the process further comprises using a platform-specific target template to map at least one monitoring element to a particular target platform while generating the rule pack.

17. The configured medium of claim 12, wherein the process further comprises associating monitoring elements with target sets by tagging the monitoring elements.

18. The configured medium of claim 12, wherein the process comprises obtaining monitoring elements provided by at least two of the following contributors:

a target product vendor developer, a target product customer support technician, an independent consultant, an independent software vendor, an administrator of a target product.

19. The configured medium of claim 12, wherein computational rule pack generation satisfies at least one of the following conditions:

generated rule packs show greater consistency between rule packs than manually written rule packs;

generated rule packs are more concise than functionally interchangeable manually written rule packs;

generated rule packs are produced more quickly than functionally interchangeable manually written rule packs.

20. The configured medium of claim 12, wherein the process further comprises at least one of the following:

generating, from the same monitoring element(s), at least two different rule packs for respective different versions of a particular target product that is identified in the target identification;

generating, from the same monitoring element(s), at least two different rule packs for respective different target products that are identified in the target identification.