U.S. patent application number 17/128454 was filed with the patent office on 2021-04-15 for analytics gathering of discovered and researched insights.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to AL CHAKRA, MICHAEL P. CLARKE, MATT R. HOGSTROM.
Application Number | 20210110279 17/128454 |
Document ID | / |
Family ID | 1000005293457 |
Filed Date | 2021-04-15 |
United States Patent
Application |
20210110279 |
Kind Code |
A1 |
CHAKRA; AL ; et al. |
April 15, 2021 |
ANALYTICS GATHERING OF DISCOVERED AND RESEARCHED INSIGHTS
Abstract
A computer-implemented method for optimizing research of an
abstracted issue with a plurality of analytics engines is
described. The method includes receiving a problem report at an
analytics engine controller. The problem report includes symptoms
of a problem in a computing system. The analytics engine forwards
the problem report to a research optimization engine that abstracts
one or more issues associated with the problem based on the
symptoms of the problem. The research optimization engine then
obtains anomaly research data for one or more of diagnosing the
problem and fixing the problem. The anomaly research data is based
on the one or more abstracted issues. The research optimization
engine associates the abstracted issues with corresponding portions
of the anomaly research data, then assigns the abstracted issues
and corresponding portions of the anomaly research data to at least
one of the plurality of analytics engines.
Inventors: |
CHAKRA; AL; (APEX, NC)
; CLARKE; MICHAEL P.; (ELLENBROOK, AU) ; HOGSTROM;
MATT R.; (CARY, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
1000005293457 |
Appl. No.: |
17/128454 |
Filed: |
December 21, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15595998 |
May 16, 2017 |
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17128454 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 5/04 20130101; G06N
5/022 20130101; G06F 11/00 20130101 |
International
Class: |
G06N 5/02 20060101
G06N005/02; G06N 5/04 20060101 G06N005/04; G06F 11/00 20060101
G06F011/00 |
Claims
1. A computer-implemented method for optimizing research of an
abstracted issue with a plurality of analytics engines, the method
comprising: providing a problem report comprising one or more
symptoms of a problem in a computing system to a research
optimization engine; abstracting, via the research optimization
engine, one or more issues associated with the problem based on the
one or more symptoms of the problem; obtaining, via the research
optimization engine, anomaly research data about an anomaly for one
or more of diagnosing the problem and fixing the problem;
associating, via the research optimization engine, the one or more
abstracted issues with one or more portions of the anomaly research
data; and assigning, via the research optimization engine, the one
or more abstracted issues and the one or more associated portions
of the anomaly research data to at least one of the plurality of
analytics engines.
2. The computer-implemented method according to claim 1, wherein
assigning the one or more abstracted issues and the one or more
associated portions of the anomaly research data to at least one of
the plurality of analytics engines comprises: determining, via the
research optimization engine, a characteristic of each of the one
or more abstracted issues; assigning an abstracted issue of the one
or more abstracted issue to an analytics engine configured to fix
problems associated with the one or more abstracted issue based on
the characteristic; and forwarding the abstracted issue and the one
or more associated portions of the anomaly research data to the
analytics engine configured to fix problems associated with the
abstracted issue.
3. The computer-implemented method according to claim 1, wherein
obtaining the anomaly research data comprises: receiving, via a
analytics engine controller, a plurality of data sources and data
types and data formats comprising one or more data lake in a
computing system; and obtaining system information, historical
operation data, and system response data.
4. The computer-implemented method according to claim 3, wherein
obtaining system information comprises: retrieving, via the
research optimization engine, host information comprising one or
more of: a host identification, a host location, cluster data
indicative of two or more mainframes acting together as a single
system image, and a hardware association between one or more of the
mainframes and the problem in the computing system.
5. The computer-implemented method according to claim 4, wherein
obtaining system information further comprises: retrieving, via the
research optimization engine, address space details comprising one
or more responses to a display command associated with the anomaly;
and querying an address space host for address space information
responsive to obtaining the one or more responses to the display
command.
6. The computer-implemented method according to claim 3, wherein
obtaining historical operation data comprises: identifying, via the
research optimization, the anomaly associated with the problem in
the computing system; and retrieving, via the research optimization
engine, one or more anomaly details from a knowledge base.
7. The computer-implemented method according to claim 6, wherein
obtaining historical operation data further comprises: retrieving,
via the research optimization engine, system log information,
wherein the system log information is cotemporaneous with an
occurrence of the anomaly.
8. The computer-implemented method according to claim 7, wherein
obtaining historical operation data further comprises: filtering,
via the research optimization engine, the system log information by
identifying and sequestering only data associated with the anomaly
and cotemporaneous with the occurrence of the anomaly; and
retrieving the filtered system log information.
9. The computer-implemented method according to claim 7, wherein
obtaining historical operation data further comprises: determining,
via the research optimization engine, whether a system abend
associated with the anomaly has occurred; and responsive to
determining that the system abend has occurred, obtaining
information associated with the system abend.
10. The computer-implemented method according to claim 9, wherein
the information associated with the abend comprises one or more of
a location of the system abend, and a fault analysis report.
11. The computer-implemented method according to claim 3, wherein
obtaining the system response data comprises: obtaining, via
research optimization engine, process information comprising one or
more of a process identification number, a job name, a primary
procedure, a start parameter, an application classification, an
application type, an application level, and a maintenance
level.
12. The computer-implemented method according to claim 11, wherein
obtaining the system response data comprises retrieving the process
information from an operating system via heuristics.
13. The computer-implemented method according to claim 12, wherein
obtaining the system response data comprises retrieving the process
information from an asset discovery engine, wherein the process
information comprises historical data associated with a system
experiencing the problem and a software running on the system
experiencing the problem.
14. The computer-implemented method according to claim 11, wherein
obtaining the system response data further comprises: retrieving,
via the research optimization engine, cross definition information
indicating one or more processes associated with the anomaly.
15. The computer-implemented method according to claim 14, wherein
retrieving the cross definition information comprises: identifying
one or more actions taken by a system processor responsive to a
detection and a resolution of the anomaly; and identifying one or
more outcomes associated with the one or more actions taken by the
system processor.
16. The computer-implemented method according to claim 1, wherein
the problem report comprises one or more of: an anomaly
identification code; a timestamp of the anomaly; a host name
indicative of a name of a host associated with the anomaly; one or
more process identifications; and extracted data from one or more
data streams associated with the anomaly, wherein the one or more
symptoms of the problem are derivable from the problem report.
17. A system for optimizing research of an abstracted issue for a
plurality of analytics engines, the system comprising: a plurality
of analytics engines; and an processor running an analytics
optimization engine, the analytics optimization engine operatively
connected to the plurality of analytics engines, the processor
configured to: receive a problem report comprising one or more
symptoms of a problem in a computing system; receive a plurality of
data sources and data types and data formats comprising one or more
data lake in a computing system; abstract one or more issues
associated with the problem based on the one or more symptoms of
the problem; obtain anomaly research data about an anomaly for one
or more of diagnosing the problem and fixing the problem; associate
the one or more abstracted issues with one or more portions of the
anomaly research data; and assign the one or more abstracted issues
and the one or more associated portions of the anomaly research
data to at least one of the plurality of analytics engines.
18. The system according to claim 17, wherein, after obtaining the
anomaly research data based on the one or more abstracted issues,
the processor is configured to: determine a characteristic of each
of the one or more abstracted issues; associate the one or more
abstracted issues with one or more portions of the anomaly research
data; assign an abstracted issue of the one or more abstracted
issue to an analytics engine configured to fix problems associated
with the one or more abstracted issue; and forward the abstracted
issue and the one or more associated portions of the anomaly
research data to the analytics engine configured to fix problems
associated with the abstracted issue.
19. A computer program product for optimizing research of an
abstracted issue for a plurality of analytics engines, the computer
program product comprising a non-transitory computer readable
storage medium having program instructions embodied therewith, the
program instructions executable by a via an analytics engine
controller operating a research optimization engine to cause the
processor to perform a method comprising: providing a problem
report comprising one or more symptoms of a problem in a computing
system to a research optimization engine; abstracting one or more
issues associated with the problem based on the one or more
symptoms of the problem; obtaining information about an anomaly for
one or more of diagnosing the problem and fixing the problem;
associating the one or more abstracted issues with one or more
portions of the anomaly research data; and assigning the one or
more abstracted issues and the one or more associated portions of
the anomaly research data to at least one of the plurality of
analytics engines.
20. The computer program product according to claim 19, wherein
assigning the one or more abstracted issues and the one or more
associated portions of the anomaly research data to at least one of
the plurality of analytics engines comprises: determining a
characteristic of each of the one or more abstracted issues;
assigning an abstracted issue of the one or more abstracted issue
to an analytics engine configured to fix problems associated with
the one or more abstracted issue; and forwarding the abstracted
issue and the associated portion the one or more associated
portions of the anomaly research data to the analytics engine
configured to fix problems associated with the abstracted issue.
Description
DOMESTIC PRIORITY
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/595,998, filed May 16, 2017 and published
as U.S. 2018-0336475 on Nov. 22, 2018, the disclosure of which is
incorporated by reference herein in its entirety.
BACKGROUND
[0002] The present invention relates to a cognitive analytics
engine, and more specifically, to dynamic cognitive analytics
gathering of discovered and researched insights.
[0003] Analytics refers to the systematic analysis of data and is
increasingly used in a variety of areas to discern patterns and
gain insight into actions suggested by those patterns. For example,
analytics are increasingly used in the management of computer
systems to analyze and address issues arising in memory and other
operational areas. In this context, analytics engine is a term that
refers to the implementation of analysis tools that receive
information to facilitate the management of computer systems.
[0004] When multiple analytics engines are tasked with analysis of
different aspects of a problem, the analytics engines will most
likely request data from the same resources, and in some instances,
request the same data. The redundant requests and data
transmissions can increase computational time and time needed for
task responses. The inefficiencies caused by request and data
redundancy can diminish the ability of the data sources to respond
in a timely manner.
SUMMARY
[0005] According to an embodiment of the present invention, a
computer-implemented method for optimizing research is described.
The method optimizes research of an abstracted issue for
transmitting to a plurality of analytics engines. The method
includes receiving a problem report at an analytics engine
controller. The problem report includes symptoms of a problem in a
computing system. The analytics engine forwards the problem report
to a research optimization engine that abstracts one or more issues
associated with the problem based on the symptoms of the problem in
the problem report. The research optimization engine then obtains
anomaly research data for one or more of diagnosing the problem and
fixing the problem. The anomaly research data is based on the
abstracted issues. The research optimization engine associates the
abstracted issues with corresponding portions of the anomaly
research data, then assigns the abstracted issues and corresponding
portions of the anomaly research data to at least one of the
plurality of analytics engines.
[0006] According to another embodiment, a system for optimizing
research of an abstracted issue for a plurality of analytics
engines is described. The system includes a plurality of analytics
engines, and an analytics engine controller running an analytics
optimization engine. The processor is operatively connected to the
plurality of analytics engines. The processor is configured to
receive a problem report that includes symptoms of a problem in a
computing system. The processor is configured to abstract one or
more issues associated with the problem based on the symptoms of
the problem, and obtain research data that gives information about
an anomaly for one or more of diagnosing the problem and fixing the
problem. The anomaly research data is based on the one or more
abstracted issues. The processor associates the abstracted issues
with corresponding portions of the anomaly research data, then
assigns the abstracted issues and corresponding portions of the
anomaly research data to at least one of the plurality of analytics
engines.
[0007] According to another embodiment, a computer program product
includes a computer-readable storage medium. The computer-readable
storage medium includes program instructions that are executable by
a processor to cause a computer to perform a method for optimizing
research. The method optimizes research of an abstracted issue for
transmitting to a plurality of analytics engines. The method
includes receiving a problem report at an analytics engine
controller. The problem report includes symptoms of a problem in a
computing system. The analytics engine forwards the problem report
to a research optimization engine that abstracts one or more issues
associated with the problem based on the symptoms of the problem.
The research optimization engine then obtains anomaly research data
for one or more of diagnosing the problem and fixing the problem.
The anomaly research data is based on the one or more abstracted
issues. The research optimization engine associates the abstracted
issues with corresponding portions of the anomaly research data,
then assigns the abstracted issues and corresponding portions of
the anomaly research data to at least one of the plurality of
analytics engines.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The forgoing and other
features, and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0009] FIG. 1 is a block diagram of the architecture that includes
the analytics engine controller and a research optimization engine
according to embodiments of the present invention;
[0010] FIG. 2A is a process flow of a computer-implemented method
for optimizing research of an abstracted issue for a plurality of
analytics engines according to embodiments of the present
invention;
[0011] FIG. 2B is a process flow of a computer-implemented method
for assigning one or more abstracted issues and associated data to
the analytics engines according to embodiments of the present
invention;
[0012] FIG. 3A is a block diagram of an exemplary problem report
according to embodiments of the present invention;
[0013] FIG. 3B is a block diagram of an exemplary problem report
after analysis by the research optimization engine of FIG. 1
according to embodiments of the present invention; and
[0014] FIG. 4 is a block diagram of an exemplary implementation of
a computer system performing the functionality of the research
optimization engine of FIG. 1 according to embodiments of the
present invention.
DETAILED DESCRIPTION
[0015] As previously noted, one implementation of analytics engines
involves receiving information and performing analytics to
facilitate management of computer systems. For example, in a data
center, one or more analytics engines monitor information
technology (IT) operations and can give insight to customers
regarding potential hacking based on a threshold number of invalid
password attempts being exceeded or a potential for a database
running out of memory based on monitoring usage, for example.
Analytics engines can also provide fixes or information related to
fixes for problems that arise in the computer systems. In prior
analytics engines, problems are detected, a problem report is
generated, and the problem report is passed to multiple analytics
engines for parallel assessment. For example, each analytics engine
may analyze a different aspect of the same detected problem. Each
of the cognitive engines drives its own research, queuing on the
same data providers (and sometimes for the same data). Each of the
cognitive engines waits in turn for the requested information and
provides proposed solutions to the problem(s) abstracted in the
problem report. The analytics engines may experience conflict when
queuing for the same resources and the same resource providers.
[0016] Problems in computing systems may have one cause or may have
multiple causes. The causes may be related, or they may be
contemporaneous and unrelated. In other aspects, the causes of a
problem may not be contemporaneous at all, but rather be have
several intervening factors separating the cause and the effect
(the anomaly). Stated in another way, a cause of the problem can be
indicated by an occurrence of the anomaly. It should be noted here
that associating some anomalies with the various causes of a
problem, and thus, a solution to the problem, may be a task that is
not currently suited (or even possible) for an unassisted human
actor. Because complex computing systems often include various
interconnecting systems and subsystems, aspects of the computing
system can, together, result in an unwanted artifact (an anomaly)
that may alone or in connection with other anomalies cause a
systematic problem. When the source or the root cause of the
anomaly is separated from the anomaly itself by multiple
intervening factors or steps, a human actor may not be able to make
a connection between the cause of the anomaly and the anomaly
itself, and even more remotely, make a connection between the
anomaly and the actual root causes. In other aspects, when multiple
unrelated causes of a problem are contemporaneous but completely
unrelated, a convergence of circumstances may result in multiple
anomalies which, any on their own, would not result in a systematic
problem, but together result in the manifestation of the
problem.
[0017] Analytics engines, as described herein, are suited for
(among other things) diagnosing and resolving problems of this
sort, because they can iteratively and systematically determine
minute patterns and/or associations between seemingly unrelated
causes, and use these associations to diagnose and resolve the
underlying issues. They are also suited for connecting multiple
intervening factors that would likely go undetected by a human
actor alone. Moreover, multiple analytics engines working in
conjunction (with each handling a specialized portion of the
detection and resolution) may each perform detection, analysis, and
resolution steps that are either impossible for human actors alone
because of the volume of data that is analyzed, or so complex and
cumbersome that the man hours required for diagnosis and
implementation of a solution is untenable. Accordingly, embodiments
of the present invention describe a technical problem and a
technical solution to the above-described technical problems that
are far more than mental abstraction or mathematical
relationships.
[0018] Turning now to an overview of the present invention, the
several embodiments detailed herein pertain to an analytics engine
controller. The analytics engine controller is an analytics engine
that communicates with the one or more other analytics engines that
manage a cluster of computers. The analytics engine controller
stores problems and corresponding solutions for future reference.
The analytics engine controller correlates related problems to
generate combination problems. Individual and correlated problems
are abstracted by the analytics engine controller in order to
generalize the specific previous problem and facilitate matching
future problems with the abstracted issues. The problems and
abstracted issues can be shared by the analytics engine controller
at different levels.
[0019] One or more embodiments of the systems and methods detailed
herein relate to recognizing interrelated issues. The analytics
engine controller includes a research assistant (also referred to
as a research optimization engine) configured with a that
hypothesizes a correlation between an anomaly associated with the
problem, and data associated with the anomaly. After determining
what information is needed for further analysis, research,
diagnosis, and resolution, the research optimization engine obtains
the information from various sources and makes a determination of
which connected analytics engines are best suited to resolve
various aspects of the problem. Each aspect of the problem has
associated with it research data that the optimization engine has
obtained. The research optimization engine forwards the abstracted
issue (or portion of the problem) and data associated with that
particular aspect of the problem, to multiple analytics engines
each suited to perform further research based on analysis made with
the research data, diagnose the source(s) of the problem, and
propose one or more resolutions to the problem. The match made by
the research optimization engine accounts for one or more
specialties of analysis for which each of the analytics engines is
suited.
[0020] While hypothesizing and verifying correlations among
problems related to the management of a cluster of computer systems
is discussed specifically for explanatory purposes, the analytics
engine controller according to the one or more embodiments
described herein can be applied in any area to find correlations
among problems and events.
[0021] Additional embodiments of the systems and methods detailed
herein relate to abstracting issues. Previously encountered
problems are generalized or abstracted by the analytics engine
controller such that they can be identified based on subsequent
symptoms even when those symptoms are not identical to ones that
were previously encountered. A searchable archive of abstracted
issues is generated. The archive can be organized into different
levels such that a hierarchy of search is established for searching
subsequent symptoms to identify an issue. The search facilitates
failure prediction and insight generation for issue resolution.
[0022] Further embodiments of the systems and methods detailed
herein relate to the analytics engine controller determining
whether and with whom to share abstracted issues. Different
hierarchical levels are defined for the different sharing partners
(e.g., other systems of the same enterprise as that of the
analytics engine controller, global database). A variety of
considerations such as contracts, service agreements, and
confidentiality agreements can be used to make a determination of
which abstracted issues can be shared and at which hierarchical
levels. Once an anomaly has been detected, it will be passed to a
research assistant (e.g., a research optimizing engine) to be
researched before it is passed to one or more cognitive engines for
assessment. This can provide a faster response from the cognitive
engines because they will have been provided with enough contextual
data for them to make a good analysis of the problem without
redundant and competing requests for information from the same
sources. Embodiments of the present invention should reduce overall
processing costs and computational overhead.
[0023] FIG. 1 is a block diagram of the architecture that includes
the analytics engine controller 110 according to one or more
embodiments. A cluster of computer systems 130-1 through 130-m
(generally referred to as 130) communicate over a bus 120 with
analytics engines 105-1 through 105-n (generally referred to as
105), an operator 101, and the analytics engine controller 110. The
analytics engine controller 110 monitors data traffic on the bus
120.
[0024] FIG. 2 is a process flow of a computer-implemented method
for optimizing research of an abstracted issue for a plurality of
analytics engines 105, according to one or more embodiments. The
processes shown in FIG. 1 can be performed continuously or
periodically. In alternate embodiments, the processes at blocks 204
through 218 can be based on a problem report being received at
block 202. The processes are performed by analytics engine
controller 110. As previously noted, analytics engine controller
110 is an analytics engine itself.
[0025] As a prior step to block 202, one or more analytics engines
105 has declared the presence of one or more problems in one or
more computer systems 130. Analytics engine controller 110 may have
searched a local database 115 to determine if the problems or
abstracted issues, which are generalized descriptions of the
problems that are generated by one or more of the analytics engines
105 regarding the problems, have a match in the database 115. A
match can facilitate expedited problem resolution or insight into
an impending failure. Prior to searching the database 115, the
analytics engine controller 110 may obtain information about the
computer software that is run by the cluster of computer systems
130 from a resource database 140. This information can narrow the
search or provide an indication of which matches are most
relevant.
[0026] By way of a general background of capability of engine
controller 110, and although a full description of which is outside
the scope of the present description, it is notable that the
analytics engine controller 110 is configured to hypothesize a
correlation among two or more problems and determine a correlation
score upon one or more of those problems being resolved. Correlated
problems can be addressed in the future as a new, combination
problem. The correlation facilitates further insight into problems
that would previously have been addressed individually. In some
aspects, engine controller 110 may have hypothesized a correlation
between two or more problems received in previous problem
reports.
[0027] Accordingly, analytics engine controller 110 can share
problems and abstracted issues, along with corresponding solutions,
for storage in other databases 150-1 through 150-x (generally
referred to as 150). The other databases 150 can be associated with
different levels sharing. For example, the other database 150-1 can
be associated with a different enterprise than the one that
operates the cluster of computer systems 130. The other database
150-2 can be a service database associated with organizations that
produce software products that are run in the cluster of computer
systems 130. The other database 150-x can be a shared, public
global database.
[0028] Research optimization engine 112 (hereafter "optimization
engine 112") is shown in FIG. 1 to be part of analytics engine
controller 110. In other embodiments, optimization engine 112
operates as a separate entity to analytics engine controller 110.
For example, optimization engine 112 may be a resource operating as
part of a cloud computing network. In other aspects, optimization
engine 112 may be a separate appliance operatively connected to
analytics engine controller. In another embodiment, analytics
engine controller may function as part of any one or more of
analytics engines 105.
[0029] FIG. 2A illustrates a process flow of a computer-implemented
method 200 for optimizing research of an abstracted issue for a
plurality of analytics engines 105, according to embodiments of the
present invention. Referring now to FIG. 2A, at block 202,
analytics engine controller 110 receives a problem report.
Receiving a problem report can include, for example, receiving a
problem report that includes information that can be used for
further research, diagnosis, and resolution of a computing
problem.
[0030] FIG. 3A depicts an exemplary problem report typically
received by an analytics engine 105. Referring briefly to FIG. 3, a
problem report 300 is illustrated according to one embodiment.
Problem report 300 can include various portions of information that
describe a computing problem in a computing system. Problem report
300 can be illustrative of the type received by analytics engine
controller 110.
[0031] As shown in FIG. 3A, problem report 300 includes one or more
symptoms of the problem. For example, problem report can include
anomaly identification (ID) 302 indicating a known anomaly that
causes a problem in a computing system. In other aspects, the
symptoms can include a timestamp 304 of the anomaly, a host name
306 of the system experiencing the anomaly and/or the one or more
process ID(s) 308 that were running on the host system during an
occurrence of the anomaly. In other aspects, the symptoms may be
embodied in the extracted data 310 that may be included in problem
report 300. The problem report in FIG. 3 is exemplary only.
Embodiments of the present invention may include some, all, and/or
additional information indicative of symptoms.
[0032] Receiving problem report 300 at block 202 refers to
receiving real-time problem logs rather than historical data. The
problem descriptions can be in the form of traffic on the bus 120
that is generated by one of the analytics engines 105. The problem
descriptions can be error logs output by one of the computer
systems 130 in the cluster being managed by the analytics engine
controller 110 (embodied as extracted data 322).
[0033] In one embodiment, analytics engine controller 110 receives
information indicative of a problem (block 202), and instead of
broadcasting the problem report 300 to analytics engines 105 for
their independent analyses of the problem, analytics engine
controller 110 forwards it to optimization engine 112, as shown in
block 204. Optimization engine 112, which may be operating on
analytics engine controller 110, can assess the symptoms of the
problem, abstract the issues from problem report 300, and do
independent research to obtain anomaly research data that indicates
(or gives clues to) the source(s) of the problem. The research data
may also give clues to areas of additional research to be explored
by one or more assigned analytics engines 105. According to one or
more embodiments, having research performed in advance by
optimization engine 112 can provide a faster response from the
cognitive engines (analytics engines 105) as compared to
broadcasting problem report 300 to all of analytics engines 105 for
parallel research, because the analytics engines 105 will have been
provided with enough contextual data for them to make a thorough
analysis of the situation. This method can reduce overall
processing costs and computational overhead across all of the
analytics engines 105 when comparing the processing costs in the
aggregate using previous methods.
[0034] Referring again to FIG. 2, in one aspect, after analytics
engine controller 110 receives problem report 300 at block 202,
analytics engine controller 110 forwards problem report 300 to
optimization engine 112, as shown in block 204.
[0035] At block 206, optimization engine 112 abstracts one or more
issues associated with the problem based on the symptoms of the
problem included in problem report 300. Abstracting the issues
includes processing the problems that are curated, correlated, or
archived individually, according to one or more embodiments. The
curation, correlation, and archiving may be performed as prior
steps not considered in detail herein. Curated problems can be
stored in a local database 115 or in a different area of memory
accessible to optimization engine 112.
[0036] The process of abstracting includes generalizing each
problem. One way of generalizing problems in an error log, for
example, is by removing incident-specific information from the
error log or other report detailing the problem. Incident-specific
information includes the job number, job name, or other identifying
information.
[0037] Abstraction can be performed at different hierarchical
levels and abstracted issues can be stored at different
hierarchical levels. For example, at one hierarchical level, an
abstracted issue can include the type of address space that is
experiencing the problem while stripping out the address space
identifier (ASID). At another hierarchical level, a more
generalized abstracted issue can have the address space information
removed, as well.
[0038] Table 1 gives examples of problems and their abstractions.
Simplified problem records are used to indicate the types of
information that can be retained or discarded to achieve
abstraction at different levels. The problem and abstracted issued
also includes anomaly research data. Anomaly research data
indicates (or gives clues too) the source(s) of the problem.
Although not specifically identified as such, anomaly research data
may be, for example, a particular transaction in a "Transaction
Failure" symptom, a particular threshold in a "critical Threshold
Exceeded" symptom, etc.
TABLE-US-00001 TABLE 1 Exemplary problems and corresponding
abstracted issues. Problem Abstracted Issue A1 Address Space:
{jobname: CI35TLXR, ASID: Address Space: {Type CICS, 4567, Type
CICS, Subtype: AOR, HasParent: Subtype: AOR} Symptoms: "MQM2T1XR"}
Symptoms: ["Multiple ["Multiple Transaction Transaction Failures",
"Critical Threshold Failures", "Critical Threshold Exceeded"]
Exceeded"] Action: ["Resolve issue with parent"] Resolution:
[Outcome: successful, Time 5 minutes] A2 Address Space: {jobname:
CI35TLXR, ASID: Address Space: {Type CICS} 4567, Type CICS,
Subtype: AOR, HasParent: Symptoms: ["Multiple "MQM2T1XR"} Symptoms:
["Multiple Transaction Failures", Transaction Failures", "Critical
Threshold "Critical Threshold Exceeded"] Exceeded"] Action:
["Resolve issue with parent"] Resolution: [Outcome: successful,
Time 5 minutes] B (1) Address Space: {jobname CI35TLXR, (1) Address
Space: {Type: ASID: 4567, Type: CICS, Subtype: AOR, CICS, Subtype:
AOR, HasParent: "MQM2T1XR"} Symptoms: HasParent: {Type: MQ,
["Multiple Transaction Failures", "Critical Subtype QueueManager}}
Threshold Exceeded"] Symptoms: ["Multiple (2) Address Space:
{jobname: MQM2TLXR, Transaction Failures", ASID: 5678, Type: MQ,
Subtype: "Critical Threshold QueueManager, HasChild: "CI35TLXR"}
Exceeded"] Symptoms: ["Unexpected messages", (2) Address Space:
{Type: "MQM234E", "Critical Threshold Exceeded"] MQ, Subtype:
QueueManager, HasChild: {Type: CICS, Subtype: AOR}} Symptoms:
["Unexpected messages", "MQM234E", "Critical Threshold Exceeded"] C
Address Space: {jobname: M23RC45X, ASID: Symptoms: ["unusually high
1234} Symptoms: ["unusually high CPU CPU usage", "no IO usage", "no
IO activity"] Action: ["Monitor for activity"] Action: ["Monitor 15
mins", "Cancel"] Resolution: {Outcome: for 15 mins", "Cancel"]
Successful, Time: 16 minutes} Resolution: {Outcome: Successful,
Time: 16 minutes}, {Outcome: Reoccurred_After_Restart, Time: 7
minutes}, {Outcome: Successful, Time: 16 minutes}
[0039] In Table 1, Rows A1 and A2 show the same problem resulting
in two different hierarchical levels of abstracted issues.
[0040] Row A2 shows a more generalized abstracted issue that does
not include the address space subtype. The abstracted issue
includes insight that refers to one or more actions taken to
resolve the problem along with information about the resolution
(e.g., success, the length of time to resolve).
[0041] Row B shows two problems that are correlated. The resulting
abstracted issue indicates that the two address spaces involved are
related as parent and child. The action and resolution are not
shown in row B for simplicity, but the insights associated with the
combination of the problems can be included in the local database
115.
[0042] Row C shows another exemplary problem that is abstracted.
Once again, the insight is omitted in Table 1. As row C indicates,
the resolution record for the abstracted issue is augmented based
on subsequent occurrences of the problem. As Table 1 indicates, an
abstracted issue includes the symptoms of the associated problem at
a minimum. At different levels of abstraction, the source or
location of the symptoms (e.g., address space type) can also be
included.
[0043] Abstracting the issues at block 206 can include curating
previously identified problems, and/or identifying and curating
previously encountered problems that are discussed in the public
sphere. This aspect of the curating can include performing internet
searches and obtaining publications using natural language
processing, for example.
[0044] Abstracting the issues, at block 206, can also include
storing the abstracted issues in the searchable local database 115.
As previously noted, the abstracted issues can be stored at
different hierarchical levels of abstraction. Along with the
abstracted issues, the non-abstracted problems can also be stored
in the local database 115. Abstracted problems can be added to the
abstract issues in the local database 115. Optionally, the received
problem can be added as a non-abstracted problem, as well. When the
problem is resolved, the resolution can be added to the local
database 115 in correspondence with the abstracted (and
non-abstracted) problem for subsequent search. In addition to
building the local database 115, abstracting issues facilitates
sharing the abstracted issues with analytics engines 105.
[0045] Once relevant information is identified by optimization
engine 112, optimization engine 112 may curate hierarchical and
indexed categories of problems. For example, in the exemplary case
of the analytics engine controller 110 being involved in the
management of a cluster of computer systems 130, the problems can
relate to any components (e.g., computer programs, memory managers)
and can relate to topics that include hardware, software, operating
systems, address spaces, subsystems, jobs, and error codes. When
the optimization engine 112 relates to another type of management,
the problems that are identified and curated can be modified to
that type of management. In some aspects, non-abstracted problems
are also stored in the local database 115.
[0046] At block 208, optimization engine 112 obtains anomaly
research data based on the abstracted issues, and in some
embodiments, the non abstracted issues as well. Accordingly, the
research assistant (research optimization engine 112) directs
queries to a number of different entities, such as the operating
system, other monitoring and automation software, other
repositories of system knowledge and other elements of the
analytics and cognitive network to help identify the entities
involved in the anomaly and the environment in which the anomaly
occurred.
[0047] In one embodiment, obtaining the anomaly research data can
include receiving a plurality of data sources, data types, and/or
data formats that include one or more data lake in a computing
system. A data lake, as used herein, refers to an aggregation of
data. Such data could be stored or streamed, and may be
heterogeneous in nature. For example, a data type or a data format
coming from a data lake may come from any number of a plurality of
data sources, and may be destined for any number of a plurality of
destinations.
[0048] In one aspect, obtaining the anomaly research data includes
obtaining system information, historical operation data, and system
response data. Each category of anomaly research data is separately
discussed below.
[0049] In one embodiment, obtaining system information includes
retrieving one or more of a host identification, a host location,
cluster data indicative of two or more mainframes acting together
as a single system image, and a hardware association between one or
more of the mainframes and the problem in the computing system.
[0050] In another embodiment, obtaining system information can also
include retrieving, via the research optimization engine, address
space details that include one or more responses to a display
command associated with the anomaly, and querying an address space
host for address space information in response to obtaining the one
or more responses to the display command.
[0051] In yet another embodiment, identifying historical operation
data includes identifying the anomaly associated with the problem
in the computing system, and retrieving, via research optimization
engine 112, one or more anomaly details from a knowledge base. In
some aspects, analytics engine controller 110 may provide
information to one or more analytics engines 105 about the anomaly
details, and/or any resolution(s) stored in correspondence with the
identified anomaly.
[0052] In another embodiment, retrieving historical operation data
can further include retrieving system log information. In one
aspect, the system log information is contemporaneous with an
occurrence of the anomaly. In another aspect, optimization engine
112 can filter the system log information to identify and sequester
only data associated with the anomaly and contemporaneous with the
occurrence of the anomaly. Optimization engine 112 then retrieves
only the filtered system log information.
[0053] Optimization engine 112 may also determine whether a system
abend (i.e., a system dump) associated with the anomaly has
occurred. If an abend has occurred, optimization engine 112 may
obtain information associated with the system abend. In some
aspects, the information associated with the abend can include a
location of the system abend, and/or a fault analysis report.
[0054] According to another embodiment, the optimization engine 112
obtains system response data. This can include obtaining one or
more of a process identification number, a job name, a primary
procedure, a start parameter, an application classification, an
application type, an application level, and a maintenance level.
Optimization engine 112 may obtain the system response data by
retrieving the process information from an operating system via
heuristics.
[0055] In another aspect, optimization engine 112 retrieves the
process information from an asset discovery engine. The process
information includes historical data associated with a system
experiencing the problem and a software running on the system
experiencing the problem. According to some embodiments of the
present invention, the historical data includes one or more
particular operation system settings, and/or a software associated
with that particular operating system setting. In one aspect, the
historical data can provide one or more recorded system responses
associated with the particular operating system setting or software
over a predetermined period of time. A predetermined period of time
may be, for example, an hour, a day, a week, a month, etc.
[0056] In another exemplary embodiment, optimization engine 112
obtains the system response data by retrieving cross definition
information that indicates one or more processes associated with
the anomaly. Optimization engine 112 may also identify one or more
actions taken by a system processor responsive to a detection and a
resolution of the anomaly, and identify one or more outcomes
associated with the respective actions.
[0057] The research process described with respect to block 208 is
iterative nature such that, with each level of discovery,
optimization engine 112 opens up more options for further
discovery. Accordingly, optimization engine 112 is driven by an
opportunistic data driven mechanism, and not a sequential
procedural mechanism.
[0058] In some embodiments of the present invention, optimization
engine 112 operates under a predetermined time limit. A
predetermined time may be, for example, from 1 to 3 seconds. In
other aspects, the predetermined time limit for research may be
less (e.g., 1/2 second to 2 seconds), or it may be more (e.g., 2
seconds to five seconds).
[0059] After describing the various ways optimization engine 112
can obtain anomaly research data as shown in block 208, we next
consider how optimization engine 112 associates abstracted issues
with the anomaly research data, as represented in block 210. At
block 210, optimization engine 112 associates the abstracted issues
with anomaly research data. In some aspects, optimization engine
112 may hypothesize a correlation. This can include the
optimization engine 112 postulating a connection between two or
more problems that are reported individually. This hypothesizing
can use the curated problems recorded in local database 115, as
well as the received problems abstracted in block 106. Accordingly,
in previous iterations of the present method, optimization engine
112 may have archived the problem (during a previous iteration at
block 260) and, when a solution was implemented, the solution was
stored in correspondence with the problem in the local database
115. Accordingly, optimization engine 112 may postulate a
connection two or more reported problems, any one or more problems,
and one or more abstracted issues, and/or the one or abstracted
issues correlated with the associated solutions.
[0060] Optimization engine 112 can hypothesize a correlation based
on several factors in addition to temporal coincidence. Even
temporal coincidence alone can suggest a high correlation if it
occurs repeatedly and consistently. That is, for example, if two
problems occur and are resolved at the same time, each occurrence
of the coincidence can raise the correlation score or level
associated with the correlation of those two problems. While a
single occurrence of a temporal coincidence of the problems can
suggest a low level or score of correlation, other factors can
suggest a stronger correlation.
[0061] For example, when two problems are associated with two
computer programs and one of the computer programs is dependent on
the other computer program, a hypothesis can be made that the two
problems are correlated. In addition to a direct interaction
between two or more components (e.g., computer programs, address
spaces, etc.) that are experiencing a problem, there can be a
competition for the same resource among the two or more components
that are experiencing a problem. As the examples indicate, the
hypothesis of a correlation among problems can require knowledge
(e.g., the interaction between components, dependence among
components, resources required by each of the components) that is
additional to the knowledge included in the problem report. Thus,
in order to hypothesize a correlation, optimization engine 112 can
obtain additional information from the resource database 140. This
information can include relationships between the sources or
locations (e.g., computer programs, memory addresses) of the
problems and resources requested by the sources of the problems.
The source of a problem is understood to be the computer program,
memory address, or other component that is experiencing the problem
and which is identified in the problem description.
[0062] Depending on the number of problem descriptions that are
received and the additional information that is obtained from
optimization engine 112, more than one hypothesis may be generated.
For example, two problems can exhibit a dependence that leads to a
hypothesis of a strong correlation while a third problem can be
hypothesized to be weakly correlated with the two problems because
it temporally coincides with the occurrence of the two
problems.
[0063] At block 212, optimization engine 112 may assign one or more
abstracted issues and the corresponding anomaly research data
retrieved that is associated with that abstracted issue, to at
least one of the plurality of analytics engines. The assignments
may be driven, for example, by the one or more hypotheses discussed
above.
[0064] In other aspects, optimization engine 112 also assigns
abstracted issues to particular analytics engines that are
predisposed for computing that particular type of problem. FIG. 2B
depicts a process flow of a computer-implemented method for
assigning one or more abstracted issues to the analytics engines,
according to embodiments of the present invention.
[0065] Referring now to FIG. 2B, in block 216, optimization engine
212 determines a characteristic of each of the one or more
abstracted issues. Accordingly, as shown in block 216, optimization
engine may determine a characteristic of each of the abstracted
issues, then assign an abstracted issue to one or more analytics
engines configured to fix problems associated with the one or more
abstracted issue.
[0066] Assigning abstracted issues and associated anomaly research
data to analytics engines for analysis can include determining,
with optimization engine 112, a characteristic of each of the
abstracted issues. A characteristic of an abstracted issue can
include, for example, a field of business (e.g., banking, data
analysis, etc.), a type of problem (e.g., memory allocation issues,
key generation and data encryption, etc.), and/or a particular
solution, etc. Accordingly, optimization engine 112 may assign an
analytics engine (any of analytics engines 105) configured to fix
problems associated with the characteristic of the abstracted
issue.
[0067] Optimization engine 112 also has knowledge of a
predetermined specialty of analysis associated with each of the
analytics engines 105. For example, one analytics engine may be
configured to diagnose, detect, and/or fix problems concerning an
address space for mixed language application servers providing
online transaction management and connectivity for applications. In
some aspects, optimization engine 112 assigns an abstracted issue
of that sort to the analytics engine configured to analyze such a
problem. Optimization engine 112 may make matches with the
abstracted issue based on the characteristic of the issue.
[0068] At block 220 optimization engine forwards the abstracted
issue and the one or more associated portions of the anomaly
research data to the analytics engine configured to fix problems
associated with the abstracted issue. The anomaly research data
forwarded includes a problem report (depicted in FIG. 3B) that
describes the abstracted issue after analysis by the research
optimization engine 112.
[0069] FIG. 3B is a block diagram of a forwarded problem report 312
after analysis by research optimization engine 112, according to
embodiments of the present invention. Referring now to FIG. 3B,
problem report 312 can include, for example, various portions of
information that describe a computing problem in a computing system
(similar to those depicted in FIG. 3A). As shown in FIG. 3B,
problem report 312 includes an anomaly identification (ID) 314
indicating a known anomaly that causes a particular problem in a
computing system. In other aspects, the symptoms can include a
timestamp 316 of the anomaly, a host name 318 of the system
experiencing the anomaly and/or the one or more process ID(s) 320
that were running on the host system during an occurrence of the
anomaly. In other aspects, the symptoms may be embodied in the
extracted data 322 that may be included in problem report 312. In
addition, problem report 312 can include one or more of system
information 324, historical operation data 326, and system response
data 328, which were the result of one or more operations described
with respect to blocks 206-218. The problem report in FIG. 3B is
exemplary only. Embodiments of the present invention may include
some, all, and/or additional information.
[0070] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, element components, and/or groups thereof.
[0071] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
invention has been presented for purposes of illustration and
description but is not intended to be exhaustive or limited to the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the invention. The
embodiment was chosen and described in order to best explain the
principles of the invention and the practical application and to
enable others of ordinary skill in the art to understand the
invention for various embodiments with various modifications as are
suited to the particular use contemplated.
[0072] The flow diagrams depicted herein are just one example.
There may be many variations to this diagram or the steps (or
operations) described therein without departing from the spirit of
the invention. For instance, the steps may be performed in a
differing order or steps may be added, deleted or modified. All of
these variations are considered a part of the claimed
invention.
[0073] While an embodiment of the invention had been described, it
will be understood that those skilled in the art, both now and in
the future, may make various improvements and enhancements which
fall within the scope of the claims which follow. These claims
should be construed to maintain the proper protection for the
invention first described.
[0074] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0075] The analytics engines 105 and analytics engine controller
110 can be part of one or more of the computer systems 130 that
carry out the functionality of the enterprise applications. One or
more analytics engines 105 and the analytics engine controller 110
can, instead, be part of a separate computer system 130. In some
embodiments, as shown in FIG. 4, the computer system 130 includes a
processor 405, memory 410 coupled to a memory controller 415, and
one or more input devices 445 and/or output devices 440, such as
peripherals, that are communicatively coupled via a local I/O
controller 435. The input devices 445 and output devices 440 can
facilitate communication with the other databases 150, for example.
These devices 440 and 445 can include, for example, a printer, a
scanner, a microphone, and the like. Input devices such as a
conventional keyboard 450 and mouse 455 can be coupled to the I/O
controller 435. The I/O controller 435 can be, for example, one or
more buses or other wired or wireless connections, as are known in
the art. The I/O controller 435 can have additional elements, which
are omitted for simplicity, such as controllers, buffers (caches),
drivers, repeaters, and receivers, to enable communications.
[0076] The I/O devices 440, 445 can further include devices that
communicate both inputs and outputs, for instance disk and tape
storage, a network interface card (NIC) or modulator/demodulator
(for accessing other files, devices, systems, or a network), a
radio frequency (RF) or other transceiver, a telephonic interface,
a bridge, a router, and the like.
[0077] The processor 405 is a hardware device for executing
hardware instructions or software, particularly those stored in
memory 410. The processor 405 can be a custom made or commercially
available processor, a central processing unit (CPU), an auxiliary
processor among several processors associated with the computer
system 130, a semiconductor based microprocessor (in the form of a
microchip or chip set), a macroprocessor, or other device for
executing instructions. The processor 405 includes a cache 470,
which can include, but is not limited to, an instruction cache to
speed up executable instruction fetch, a data cache to speed up
data fetch and store, and a translation lookaside buffer (TLB) used
to speed up virtual-to-physical address translation for both
executable instructions and data. The cache 470 can be organized as
a hierarchy of more cache levels (L1, L2, etc.).
[0078] The memory 410 can include one or combinations of volatile
memory elements (e.g., random access memory, RAM, such as DRAM,
SRAM, SDRAM, etc.) and nonvolatile memory elements (e.g., ROM,
erasable programmable read only memory (EPROM), electronically
erasable programmable read only memory (EEPROM), programmable read
only memory (PROM), tape, compact disc read only memory (CD-ROM),
disk, diskette, cartridge, cassette or the like, etc.). Moreover,
the memory 410 can incorporate electronic, magnetic, optical, or
other types of storage media. Note that the memory 410 can have a
distributed architecture, where various components are situated
remote from one another but can be accessed by the processor
405.
[0079] The instructions in memory 410 can include one or more
separate programs, each of which comprises an ordered listing of
executable instructions for implementing logical functions. In the
example of FIG. 4, the instructions in the memory 410 include a
suitable operating system (OS) 411. The operating system 411
essentially can control the execution of other computer programs
and provides scheduling, input-output control, file and data
management, memory management, and communication control and
related services.
[0080] Additional data, including, for example, instructions for
the processor 405 or other retrievable information, can be stored
in storage 420, which can be a storage device such as a hard disk
drive or solid state drive. The stored instructions in memory 410
or in storage 420 can include those enabling the processor to
execute one or more aspects of the analytics engine controller 110
and methods of this detailed description.
[0081] The computer system 130 can further include a display
controller 425 coupled to a monitor 430. In some embodiments, the
computer system 130 can further include a network interface 460 for
coupling to a network 465. The network 465 can be an IP-based
network for communication between the computer system 130 and an
external server, client and the like via a broadband connection.
The network 465 transmits and receives data between the computer
system 130 and external systems. In some embodiments, the network
465 can be a managed IP network administered by a service provider.
The network 465 can be implemented in a wireless fashion, e.g.,
using wireless protocols and technologies, such as WiFi, WiMax,
etc. The network 465 can also be a packet-switched network such as
a local area network, wide area network, metropolitan area network,
the Internet, or other similar type of network environment. The
network 465 can be a fixed wireless network, a wireless local area
network (LAN), a wireless wide area network (WAN) a personal area
network (PAN), a virtual private network (VPN), intranet or other
suitable network system and can include equipment for receiving and
transmitting signals.
[0082] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0083] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0084] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0085] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0086] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0087] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0088] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0089] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *