U.S. patent application number 15/709508 was filed with the patent office on 2017-12-28 for eliminating execution of jobs-based operational costs of related reports.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Michael Fankhauser, Marc N. Haber, Benjamin Halberstadt, Roger K. Hecker.
Application Number | 20170371711 15/709508 |
Document ID | / |
Family ID | 53183825 |
Filed Date | 2017-12-28 |
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United States Patent
Application |
20170371711 |
Kind Code |
A1 |
Fankhauser; Michael ; et
al. |
December 28, 2017 |
ELIMINATING EXECUTION OF JOBS-BASED OPERATIONAL COSTS OF RELATED
REPORTS
Abstract
Optimizing operational costs in a computing environment includes
identifying high-cost jobs that are executed to generate one or
more reports in the computing environment, identifying one or more
reports the generation of which is dependent on the execution of
the high-cost jobs, and culling at least a first job from among the
high-cost jobs, in response to determining that a benefit achieved
from the reports that depend on the first job does not justify
costs associated with generating the reports.
Inventors: |
Fankhauser; Michael; (DN
Karkur, IL) ; Haber; Marc N.; (Efrat, IL) ;
Halberstadt; Benjamin; (Jerusalem, IL) ; Hecker;
Roger K.; (Efrat, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
53183825 |
Appl. No.: |
15/709508 |
Filed: |
September 20, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15246577 |
Aug 25, 2016 |
9811382 |
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15709508 |
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15062712 |
Mar 7, 2016 |
9483309 |
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15246577 |
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14088501 |
Nov 25, 2013 |
9336504 |
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15062712 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/254 20190101;
G06F 9/4881 20130101; G06Q 10/0631 20130101; G06F 9/5011 20130101;
G06Q 10/0639 20130101; G06Q 10/0637 20130101 |
International
Class: |
G06F 9/50 20060101
G06F009/50; G06F 9/48 20060101 G06F009/48; G06F 17/30 20060101
G06F017/30; G06Q 10/06 20120101 G06Q010/06 |
Claims
1. A computer program product comprising a non-transitory computer
readable storage medium having a computer readable program, wherein
the computer readable program when executed on a computer causes
the computer to: identify high-cost jobs from among a plurality of
jobs that are executed by one or more computing devices in the
computing environment to generate one or more reports in the
computing environment, the high-cost jobs determined as a function
of resource usage of resources in the computing environment,
wherein the resource usage comprises at least one of CPU usage,
data storage usage, memory usage, and network bandwidth usage;
identify one or more reports the generation of which is dependent
on the execution of the high-cost jobs; and cull at least a first
job from among the high-cost jobs, in response to determining that
a benefit achieved from the reports that depend on the first job
does not justify costs associated with generating the reports,
wherein costs associated with a report are calculated based on
aggregated cost of jobs on which the report depends, and wherein
the cost of the individual job is determined by: a) if the job is
not executing during the highest peak of resource usage, then its
individual cost is zero; b) if the job is executing during the
highest peak of resource usage but is needed by other reports too,
then its individual cost is zero; and c) if the job is executing
during the highest peak of resource usage and is only needed by a
particular report, then the job cost is added to the report's
individual job cost.
Description
PRIORITY
[0001] This application is a continuation of and claims priority to
U.S. patent application Ser. No. 15/246,577, filed on Aug. 25,
2016, entitled "ELIMINATING EXECUTION OF JOBS-BASED OPERATIONAL
COSTS OF RELATED REPORTS"; which is a continuation of Ser. No.
15/062,712 filed Mar. 7, 2016 and issued as U.S. Pat. No.
9,483,309B2 on Nov. 1, 2016 entitled "ELIMINATING EXECUTION OF
JOBS-BASED OPERATIONAL COSTS OF RELATED REPORTS; which is a
continuation of U.S. patent application Ser. No. 14/088,501, filed
on Nov. 25, 2013 and issued as U.S. Pat. No. 9,336,504B2 on May 10,
2016 entitled "ELIMINATING EXECUTION OF JOBS-BASED OPERATIONAL
COSTS OF RELATED REPORTS; the entire contents of which are
incorporated herein by reference.
BACKGROUND
[0002] The disclosed subject matter relates generally to optimizing
execution of jobs in a computing environment and, more
particularly, to strategically eliminating jobs according to the
costs for generating reports associated with the jobs.
[0003] Many enterprises rely on business intelligence (BI) reports
for day-to-day decision making. BI reports are generated by way of
extract, transform, and load (ETL) jobs, which require computing
resources (e.g., CPU, memory, etc.) to run. Thus, each ETL job has
an operational cost that can be calculated based on the cost of
processing resources allocated to that job.
[0004] In general, the number of jobs proportionally grows with the
number of BI reports generated. If sufficient capacity and proper
planning are not present, the computing resources may become
overloaded and fail to properly support the jobs. It is desirable
to maintain sufficient capacity to support the important jobs,
while culling the less important jobs or reports to lower
operational costs.
[0005] Typically, a human operator examines each report to see if
the report meets certain cost-benefit criteria based on which the
report and the supporting jobs may be decommissioned. This process
is a manual task and expensive. Further, the process typically does
not result in substantial savings, due to lack of insight about
operational costs of individual reports.
SUMMARY
[0006] In accordance with embodiments, methods, systems and
computer program products for optimizing operational costs in a
computing environment are provided. A method includes identifying
high-cost jobs that are executed to generate one or more reports in
the computing environment, identifying one or more reports the
generation of which is dependent on the execution of the high-cost
jobs, and culling at least a first job from among the high-cost
jobs, in response to determining that a benefit achieved from the
reports that depend on the first job does not justify costs
associated with generating the reports.
[0007] A system comprising one or more logic units is provided. The
one or more logic units are configured to perform the functions and
operations associated with the above-disclosed methods. In yet
another embodiment, a computer program product comprising a
computer readable storage medium having a computer readable program
is provided. The computer readable program when executed on a
computer causes the computer to perform the functions and
operations associated with the above-disclosed methods.
[0008] One or more of the above-disclosed embodiments in addition
to certain alternatives are provided in further detail below with
reference to the attached figures. The disclosed subject matter is
not, however, limited to any particular embodiment disclosed.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0009] The disclosed embodiments may be better understood by
referring to the figures in the attached drawings, as provided
below.
[0010] FIG. 1 illustrates an exemplary computing environment in
accordance with one or more embodiments, wherein a data warehouse
and a data mart are implemented to aggregate data for the
production of BI reports.
[0011] FIG. 2 is a flow diagram of an exemplary method for reducing
the costs associated with the generation of reports in accordance
with one embodiment.
[0012] FIGS. 3 and 4 are block diagrams of hardware and software
environments in which the disclosed systems and methods may
operate, in accordance with one or more embodiments.
[0013] Features, elements, and aspects that are referenced by the
same numerals in different figures represent the same, equivalent,
or similar features, elements, or aspects, in accordance with one
or more embodiments.
DETAILED DESCRIPTION
[0014] A portion of this disclosure may contain material, which is
subject to copyright protection. The owner has no objection to the
facsimile reproduction by any one of the patent document or the
patent disclosure, as it appears in the Patent and Trademark Office
patent file or records, but otherwise reserves all copyrights
whatsoever.
[0015] Certain marks referenced herein may be common law or
registered trademarks of the applicant, the assignee or third
parties affiliated or unaffiliated with the applicant or the
assignee. Use of these marks is for providing an enabling
disclosure by way of example and shall not be construed to
exclusively limit the scope of the disclosed subject matter to
material associated with such marks.
[0016] In the following, numerous specific details are set forth to
provide a thorough description of various embodiments. Certain
embodiments may be practiced without these specific details or with
some variations in detail. In some instances, certain features are
described in less detail so as not to obscure other aspects. The
level of detail associated with each of the elements or features
should not be construed to qualify the novelty or importance of one
feature over the others.
[0017] Referring to FIG. 1, in the exemplary operating environment
100, one or more server systems 190 may be utilized to process
day-to-day transactions in a business enterprise, or other
computing environments. Server systems 190 may provide relevant
data to a data warehouse 110 by way of scheduling and executing
computer implemented jobs. An ETL tool may be used to move selected
data to domain-specific data marts 120 to facilitate reporting,
analysis and decision making in the operational environment
100.
[0018] Cubes 150 may be utilized for the purpose of filtering or
aggregating data from a data warehouse 110 or a data mart 120 to
support the efficient generation of reports 170. For example, a
cube 150 may be implemented to filter out information included in a
data mart 120 or data warehouse 110 to facilitate the generation of
a report 170 that includes sales information for a product on a
quarterly basis, where the timing data stored in the data warehouse
110 or the data mart 120 may provide the sales data on a daily or
weekly basis, for example.
[0019] Data in data marts 120 or cubes 150 may be processed by way
of one or more BI tools (e.g., IBM Cognos Enterprise.RTM.) to
generate one or more reports 170. To maintain an efficient
reporting process, a three-stage approach may be implemented in
accordance with one embodiment. In a first stage, high-cost jobs
that are scheduled for execution to generate one or more reports
170 are identified. The cost of a job may be determined based on
processor usage at a certain time interval (e.g., during peak
hours). Once the high-cost jobs are identified, in a second stage,
reports associated with those jobs are identified. In a third
stage, certain jobs are selected for elimination to the extent that
the reports depending on the selected jobs do not justify the cost
associated with those jobs.
[0020] In eliminating the unwanted reports, in one embodiment, the
interdependencies between multiple jobs and reports are taken into
consideration so that, for example, if one job contributes to more
than one report, a job associated with a low value report is not
eliminated if that job is also needed for a high value report. As
provided in further detail below, reports may be grouped together
as a function of the set of common reports that are dependent on a
job, such that aggregated cost savings may be achieved by removing
a group of reports that depend on a common set of jobs.
[0021] To determine the operational costs associated with a job, in
one embodiment, the number of CPUs deployed to run the job is
considered. This is because each additional CPU deployed may incur
hardware costs and software licensing costs associated with the
corresponding database, application server and ETL engine that are
utilized to facilitate job execution. In one implementation, since
the number of CPUs deployed increases during a peak usage window,
narrowing the peak usage window helps reduce the number of CPUs
required to run the jobs and therefore the operational costs.
[0022] Referring to FIG. 2, in one embodiment, usage metrics for a
job execution environment may be determined by identifying the peak
CPU usage time range (S210), identifying the jobs running during
that time range (S220), and identifying the CPU usage or usage
contribution factor for a job (S230). Once the above metrics are
calculated, the job costs by CPU usage may be calculated (S240).
Below, a detailed example process (e.g., algorithm) is provided
that facilitates the translation of CPU usage metrics to a more
meaningful measure to indicate the associated job costs.
[0023] In one implementation, a data lineage tool may be used to
identify the dependency of a report on one or more jobs and
determine the cost for each report (S250, S260). In this manner,
the reports that contribute to peak CPU usage (which contribute
disproportionately to the operational costs) may be detected. These
reports may be the proper candidates for culling. Thus, in one
implementation, the benefits (e.g., business need, importance,
etc.) of a candidate report is examined and weighed against the
report's cost (S270) by way of a cost-benefit analysis, for
example. The jobs or the dependent reports that do not meet a
certain criteria are eliminated (S280).
[0024] In one example scenario, InfoSphere Information Server that
includes a DataStage ETL engine may be used to schedule and run the
jobs in an ETL environment. In this example, a DataStage
operational console may be used to provide CPU metrics for each job
run. A metadata asset manager my be utilized for integration with
Cognos BI reports that includes Metadata Workbench which deduces a
data lineage graph from the DataStage jobs to the Cognos BI
reports. Without limitation, the following exemplary process may be
implemented in one embodiment:
a) The DataStage Operational Console may be used to: [0025]
Identify the peak CPU usage time range. [0026] Identify the
DataStage jobs running during that time range. [0027] For a job,
find its CPU usage and determine the individual cost of the job
based on its CPU usage. b) Using Metadata Workbench invoke a data
lineage tool for a job to identify the downstream BI reports that
are using the job data either directly or indirectly. c) Determine
the individual cost of a report based on the costs of the jobs
which provide the report's data. d) Determine the cost for a report
based the analysis above calculated metrics and eliminate the
reports whose business needs do not justify their cost together
with the jobs that provide their data.
[0028] Employing the above process to cull jobs and reports will
reduce the peak CPU usage. The above process may be repeated
iteratively to identify eliminate additional jobs and reports until
a threshold condition is reached. For the purpose of providing a
clarifying detailed example, in the following, a specific exemplary
implementation is introduced. It is noteworthy, however, that the
specific features or scenarios provided are not to be construed to
limit the scope of the claimed subject matter to such
limitations.
[0029] In one example implementation, the unit of report cost may
not be represented as currency, but rather as a comparison to one
another report in terms of relative CPU usage. For example, if
there are 5 reports that are being compared, it may be adequate to
provide information that indicates: report_X uses 40% of the
resources while the other reports use 15% of the resources each. In
this example, report_X is the most expensive and therefore would be
the first candidate for the cost-benefit analysis and elimination.
That is, if measuring the report's business value against its cost
does not make sense, then report_X and jobs on which that report
depends may be eliminated, provided that those jobs are not needed
for generating the other reports.
[0030] Job costs may be calculated based on a DataStage operational
console indication of the percentage of CPU usage for a job that is
run during a peak window. This percentage indicates the cost number
that is associated with the job. Jobs that do not run during the
peak window do not impact the peak usage if culled. Therefore, in
one implementation cost associated with such jobs may be considered
zero. Once the job costs are determined, then the cost associated
with the corresponding reports may be calculated.
[0031] By way of example, consider a scenario where there are two
reports (R1, R2) and three jobs (J1, J2, J3) running during the
peak usage window such that: [0032] J1 moves data that is needed
for R1. The cost of J1 is 5. [0033] J2 moves data that is needed
for R2. The cost of J2 is 6. [0034] J3 moves data that is needed
for R1 and R2. The cost of J3 is 9.
[0035] In the above example, if R1 is deemed unnecessary and
removed then J1 can be removed since it is providing data only to
R1. However, J2 and J3 cannot be removed, since J2 and J3 are
providing data also to R2. As such, removing R1 will save running
J1 and therefore will save the cost of 5.
[0036] On the other hand, in the above example, if R2 is deemed
unnecessary and removed, then J2 may be removed, since it is
providing data only to R2. J1 and J3 may not be removed, since J1
and J3 are providing data also to R1. As such, removing R2 will
save running J2 and therefore will save the cost of 6
[0037] In a different scenario, if R1 and R2 are both deemed
unnecessary and removed then J1, J2 and J3 may all be removed since
they are providing data only to R1 and R2. As such, removing R1 and
R2 will save the cost of 5+6+9, which is 20.
[0038] As provided in the above example scenarios, the cost savings
achieved by removing two reports is more than the aggregate savings
achieved by removing each report independently. As shown above, J3
could be removed and its cost saved if both R1 and R2 were
removed.
[0039] In accordance with one implementation, to determine the
operational cost of a report, it may be important to distinguish
between the cost savings achieved by removing an individual report
vs. the cost savings achieved by removing a collection of reports,
where such collection of reports represents the full set of reports
that are dependent on a single set of jobs.
[0040] In other words, the individual cost of a report may be
determined by the cost saved by removing only the report. In one
implementation, data lineage results may be used to find the jobs
contributing data to a particular report. In one example
implementation, the related operating costs for a job may be
assigned according to the following, for example: [0041] If the job
is not executing during the highest peak of CPU usage, then its
individual cost is zero. [0042] If the job is executing during the
highest peak of CPU usage but is needed by other reports too, then
its individual cost is zero. [0043] If the job is executing during
the highest peak of CPU usage and is needed only by a particular
report, then the job cost is added to the reports individual
cost.
[0044] Based on the above, the cost of a report may be calculated
as the aggregate cost of all jobs that contribute to the report and
the collective cost of a "report group" may be determined as the
cost saved by removing a group of reports. As noted earlier, data
lineage results may be used to determine the jobs contributing data
to a group of reports. For a job the associated costs with that job
may be assigned based on the following, for example: [0045] If the
job is not executing during the highest peak of CPU usage then its
cost is zero. [0046] If the job is executing during the highest
peak of CPU usage but it is needed by reports not in this group
then its cost is zero. [0047] If the job is executing during the
highest peak of CPU usage and it is needed by only this report
group then its cost is added to the report group's cost.
[0048] In one implementation, the cost of the report group is the
aggregate cost of jobs that contribute to the report group. In the
following, another detailed example is provided, without
limitation, to help better understand the methodology discussed
above.
Non-limiting Example:
[0049] Reviewing a DataStage CPU utilization graph for an ETL
operational environment may indicate peak usage on the 21st of Nov
between 01:00 am and 03:30 am. The jobs that were executing during
this peak may be the jobs that have the highest real cost. Removing
these jobs real savings may be achieved. In this example, an
operation console may be accessed to find the jobs that were
running on the 21st of Nov between 01:00 am and 03:30 am. For
example, the operation console may indicate that a job "loadDW
EMEA" was executing during the peak and the related CPU usage was
15%. Further investigation may further indicate that there were
four jobs executing during the peak usage window and the job costs
are 8, 12, 9 and 15, for example.
[0050] A data lineage tool may be used for the target jobs and the
reports that depend on the target jobs may be found. For example,
the data lineage tool may provide the following lineage summary:
[0051] The job "loadDW EMEA" is feeding only R1 its cost is 15.
[0052] The job J1 is feeding reports R2 and R3 its cost is 12.
[0053] The job J2 is feeding reports R1, R2 and R3 its cost is 9.
[0054] The job J3 is feeding reports R1, R2 and R3 its cost is
8.
[0055] Consider a scenario with a single job that costs 15 and
contributes to two different reports: R1 and R2. We would like to
determine how does the job costs are attributed to R1 separately,
because it is not fair or correct to say that R1 cost is 15, since
the cost 15 is also needed for R2. To resolve this issue, the
following costs are considered: [0056] R1 individual cost is 0
[0057] R2 individual cost is 0 [0058] R1 and R2 collective cost is
15
[0059] Based upon the lineage summary and the calculated job costs,
the reports costs may be determined as follows:
[0060] Individual costs: [0061] R1=15--The job "loadDW EMEA" is
only required for R1 and the cost of this job is 15. No other jobs
running during the peak contribute solely to this report [0062]
R2=0--The jobs (J1, J2, J3) running during the CPU peak that
contribute to this report also contribute to other reports [0063]
R3=0--The jobs (J1, J2, J3) running during the CPU peak that
contribute to this report also contribute to other reports
[0064] Collective costs: [0065] The cost of collection (R2, R3) is
12--Job J1 is executing during the peak CPU usage and contributes
solely to these reports, the cost of J1 is 12 and therefore the
cost of this collection is 12 [0066] The cost of collection (R1,
R2, R3) is 44--Jobs J1, J2, J3 and "loadDW EMEA" are executing
during the peak CPU usage and contribute solely to these reports,
their costs are 12, 9, 8, 15 respectively and therefore the cost of
this collection is 44
[0067] Based on the above, a human operator or a computer
implemented system may be able to understand that culling
individual reports R2 or R3 will not result in costs saving.
However, the following actions may provide substantial cost
savings: [0068] Cull R1 and save 15 [0069] Cull R2 and R3 and save
12 [0070] Cull R1, R2 and R3 and save 44
[0071] As shown, culling R1 and the corresponding dependent jobs is
more impactful than culling both R2 and R3 and all their dependent
jobs. According to the above implantations, the individual and
collective costs of one or more jobs and the reports that depend on
such jobs may be calculated, so that the jobs and reports that do
not meet a cost benefit analysis may be properly eliminated.
[0072] Advantageously, using the above methodology, costs
associated with adding additional hardware or software licensing
for different system components may be avoided or reduced. Further,
the need for hiring additional human operators, and human-related
errors or shortcomings in removing non-impactful reports due to
lack of insight into the operational cost of each report may be
also eliminated.
[0073] References in this specification to "an embodiment", "one
embodiment", "one or more embodiments" or the like, mean that the
particular element, feature, structure or characteristic being
described is included in at least one embodiment of the disclosed
subject matter. Occurrences of such phrases in this specification
should not be particularly construed as referring to the same
embodiment, nor should such phrases be interpreted as referring to
embodiments that are mutually exclusive with respect to the
discussed features or elements.
[0074] In different embodiments, the claimed subject matter may be
implemented as a combination of both hardware and software
elements, or alternatively either entirely in the form of hardware
or entirely in the form of software. Further, computing systems and
program software disclosed herein may comprise a controlled
computing environment that may be presented in terms of hardware
components or logic code executed to perform methods and processes
that achieve the results contemplated herein. The methods and
processes, when performed by a general purpose computing system or
machine, convert the general purpose machine to a specific purpose
machine.
[0075] Referring to FIGS. 3 and 4, a computing system environment
in accordance with an exemplary embodiment may be composed of a
hardware environment 1110 and a software environment 1120. The
hardware environment 1110 may comprise logic units, circuits or
other machinery and equipments that provide an execution
environment for the components of software environment 1120. In
turn, the software environment 1120 may provide the execution
instructions, including the underlying operational settings and
configurations, for the various components of hardware environment
1110.
[0076] Referring to FIG. 3, the application software and logic code
disclosed herein may be implemented in the form of machine readable
code executed over one or more computing systems represented by the
exemplary hardware environment 1110. As illustrated, hardware
environment 110 may comprise a processor 1101 coupled to one or
more storage elements by way of a system bus 1100. The storage
elements, for example, may comprise local memory 1102, storage
media 1106, cache memory 1104 or other machine-usable or computer
readable media. Within the context of this disclosure, a machine
usable or computer readable storage medium may include any
recordable article that may be utilized to contain, store,
communicate, propagate or transport program code.
[0077] A computer readable storage medium may be an electronic,
magnetic, optical, electromagnetic, infrared, or semiconductor
medium, system, apparatus or device. The computer readable storage
medium may also be implemented in a propagation medium, without
limitation, to the extent that such implementation is deemed
statutory subject matter. Examples of a computer readable storage
medium may include a semiconductor or solid-state memory, magnetic
tape, a removable computer diskette, a random access memory (RAM),
a read-only memory (ROM), a rigid magnetic disk, an optical disk,
or a carrier wave, where appropriate. Current examples of optical
disks include compact disk, read only memory (CD-ROM), compact disk
read/write (CD-R/W), digital video disk (DVD), high definition
video disk (HD-DVD) or Blue-ray.TM. disk.
[0078] In one embodiment, processor 1101 loads executable code from
storage media 1106 to local memory 1102. Cache memory 1104
optimizes processing time by providing temporary storage that helps
reduce the number of times code is loaded for execution. One or
more user interface devices 1105 (e.g., keyboard, pointing device,
etc.) and a display screen 1107 may be coupled to the other
elements in the hardware environment 1110 either directly or
through an intervening I/O controller 1103, for example. A
communication interface unit 1108, such as a network adapter, may
be provided to enable the hardware environment 1110 to communicate
with local or remotely located computing systems, printers and
storage devices via intervening private or public networks (e.g.,
the Internet). Wired or wireless modems and Ethernet cards are a
few of the exemplary types of network adapters.
[0079] It is noteworthy that hardware environment 1110, in certain
implementations, may not include some or all the above components,
or may comprise additional components to provide supplemental
functionality or utility. Depending on the contemplated use and
configuration, hardware environment 1110 may be a machine such as a
desktop or a laptop computer, or other computing device optionally
embodied in an embedded system such as a set-top box, a personal
digital assistant (PDA), a personal media player, a mobile
communication unit (e.g., a wireless phone), or other similar
hardware platforms that have information processing or data storage
capabilities.
[0080] In some embodiments, communication interface 1108 acts as a
data communication port to provide means of communication with one
or more computing systems by sending and receiving digital,
electrical, electromagnetic or optical signals that carry analog or
digital data streams representing various types of information,
including program code. The communication may be established by way
of a local or a remote network, or alternatively by way of
transmission over the air or other medium, including without
limitation propagation over a carrier wave.
[0081] As provided here, the disclosed software elements that are
executed on the illustrated hardware elements are defined according
to logical or functional relationships that are exemplary in
nature. It should be noted, however, that the respective methods
that are implemented by way of the exemplary software elements may
be also encoded in the hardware elements by way of configured and
programmed processors, application specific integrated circuits
(ASICs), field programmable gate arrays (FPGAs) and digital signal
processors (DSPs), for example.
[0082] Referring to FIG. 4, software environment 1120 may be
generally divided into two classes comprising system software 1121
and application software 1122 as executed on one or more hardware
environments 1110. In one embodiment, the methods and processes
disclosed here may be implemented as system software 1121,
application software 1122, or a combination thereof. System
software 1121 may comprise control programs, such as an operating
system (OS) or an information management system, that instruct one
or more processors 1101 (e.g., microcontrollers) in the hardware
environment 1110 on how to function and process information.
Application software 1122 may comprise but is not limited to
program code, data structures, firmware, resident software,
microcode or any other form of information or routine that may be
read, analyzed or executed by a processor 1101.
[0083] In other words, application software 1122 may be implemented
as program code embedded in a computer program product in form of a
machine-usable or computer readable storage medium that provides
program code for use by, or in connection with, a machine, a
computer or any instruction execution system. Moreover, application
software 1122 may comprise one or more computer programs that are
executed on top of system software 1121 after being loaded from
storage media 1106 into local memory 1102. In a client-server
architecture, application software 1122 may comprise client
software and server software. For example, in one embodiment,
client software may be executed on a client computing system that
is distinct and separable from a server computing system on which
server software is executed.
[0084] Software environment 1120 may also comprise browser software
1126 for accessing data available over local or remote computing
networks. Further, software environment 1120 may comprise a user
interface 1124 (e.g., a graphical user interface (GUI)) for
receiving user commands and data. It is worthy to repeat that the
hardware and software architectures and environments described
above are for purposes of example. As such, one or more embodiments
may be implemented over any type of system architecture, functional
or logical platform or processing environment.
[0085] It should also be understood that the logic code, programs,
modules, processes, methods and the order in which the respective
processes of each method are performed are purely exemplary.
Depending on implementation, the processes or any underlying
sub-processes and methods may be performed in any order or
concurrently, unless indicated otherwise in the present disclosure.
Further, unless stated otherwise with specificity, the definition
of logic code within the context of this disclosure is not related
or limited to any particular programming language, and may comprise
one or more modules that may be executed on one or more processors
in distributed, non-distributed, single or multiprocessing
environments.
[0086] As will be appreciated by one skilled in the art, a software
embodiment may include firmware, resident software, micro-code,
etc. Certain components including software or hardware or combining
software and hardware aspects may generally be referred to herein
as a "circuit," "module" or "system." Furthermore, the subject
matter disclosed may be implemented as a computer program product
embodied in one or more computer readable storage medium(s) having
computer readable program code embodied thereon. Any combination of
one or more computer readable storage medium(s) may be utilized.
The computer readable storage medium may be a computer readable
signal medium or a computer readable storage medium. A computer
readable storage medium may be, for example, but not limited to, an
electronic, magnetic, optical, electromagnetic, infrared, or
semiconductor system, apparatus, or device, or any suitable
combination of the foregoing.
[0087] In the context of this document, a computer readable storage
medium may be any tangible medium that can contain, or store a
program for use by or in connection with an instruction execution
system, apparatus, or device. A computer readable signal medium may
include a propagated data signal with computer readable program
code embodied therein, for example, in baseband or as part of a
carrier wave. Such a propagated signal may take any of a variety of
forms, including, but not limited to, electro-magnetic, optical, or
any suitable combination thereof. A computer readable signal medium
may be any computer readable medium that is not a computer readable
storage medium and that can communicate, propagate, or transport a
program for use by or in connection with an instruction execution
system, apparatus, or device.
[0088] Program code embodied on a computer readable storage medium
may be transmitted using any appropriate medium, including but not
limited to wireless, wireline, optical fiber cable, RF, etc., or
any suitable combination of the foregoing. Computer program code
for carrying out the disclosed operations may be 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.
[0089] The program code 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).
[0090] Certain embodiments are disclosed with reference to
flowchart illustrations or block diagrams of methods, apparatus
(systems) and computer program products according to embodiments.
It will be understood that each block of the flowchart
illustrations or block diagrams, and combinations of blocks in the
flowchart illustrations and/or block diagrams, can be implemented
by computer program instructions. These computer program
instructions may be provided to a processor of a general purpose
computer, a special purpose machinery, 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 or acts specified in the flowchart or
block diagram block or blocks.
[0091] These computer program instructions may also be stored in a
computer readable storage medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable storage medium produce an article of
manufacture including instructions which implement the function or
act specified in the flowchart or block diagram block or
blocks.
[0092] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer or machine implemented process such that the
instructions which execute on the computer or other programmable
apparatus provide processes for implementing the functions or acts
specified in the flowchart or block diagram block or blocks.
[0093] 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. In this regard, each block in the
flowchart or block diagrams may represent a module, segment, or
portion of code, which comprises one or more executable
instructions for implementing the specified logical functions. It
should also be noted that, in some alternative implementations, the
functions noted in the block may occur in any order or out of the
order noted in the figures.
[0094] 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 or flowchart illustration, and combinations of blocks in
the block diagrams or flowchart illustration, may be implemented by
special purpose hardware-based systems that perform the specified
functions or acts, or combinations of special purpose hardware and
computer instructions.
[0095] The claimed subject matter has been provided here with
reference to one or more features or embodiments. Those skilled in
the art will recognize and appreciate that, despite of the detailed
nature of the exemplary embodiments provided here, changes and
modifications may be applied to the embodiments without limiting or
departing from the generally intended scope. These and various
other adaptations and combinations of the embodiments provided here
are within the scope of the disclosed subject matter as defined by
the claims and their full set of equivalents.
* * * * *