U.S. patent application number 16/949667 was filed with the patent office on 2022-05-12 for generation of adaptive configuration files to satisfy compliance.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Charles E. Beller, Carlos A. Fonseca, Shikhar Kwatra, Abhishek Malvankar.
Application Number | 20220147333 16/949667 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-12 |
United States Patent
Application |
20220147333 |
Kind Code |
A1 |
Malvankar; Abhishek ; et
al. |
May 12, 2022 |
GENERATION OF ADAPTIVE CONFIGURATION FILES TO SATISFY
COMPLIANCE
Abstract
The present invention may include an embodiment that receives a
deployment declaration in a natural language. The embodiment may
detect one or more sequencing entities and one or more parameter
entities using trained natural language processing. The embodiment
may sequence a configuration file based on the one or more
sequencing entities. The embodiment may determine a plurality of
configuration parameters in the sequenced configuration file. The
embodiment may substitute a configuration parameter from the
plurality of configuration parameters of the sequenced
configuration file with the one or more parameter entities. The
embodiment may align the plurality of configuration parameters of
the sequenced configuration file based on organization compliance
data and deploys a tuned cloud service using the sequenced
configuration file.
Inventors: |
Malvankar; Abhishek; (White
Plains, NY) ; Kwatra; Shikhar; (San Jose, CA)
; Beller; Charles E.; (Baltimore, MD) ; Fonseca;
Carlos A.; (LaGrangeville, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Appl. No.: |
16/949667 |
Filed: |
November 10, 2020 |
International
Class: |
G06F 8/61 20060101
G06F008/61; G06F 8/71 20060101 G06F008/71; G06F 9/445 20060101
G06F009/445; G06K 9/62 20060101 G06K009/62; G06F 40/205 20060101
G06F040/205; H04L 29/08 20060101 H04L029/08 |
Claims
1. A processor-implemented method for configuration file
generation, the method comprising: receiving a deployment
declaration for an environment in a natural language; detecting one
or more sequencing entities and one or more parameter entities from
the deployment declaration using trained natural language
processing; sequencing a configuration file based on the one or
more sequencing entities; determining a plurality of configuration
parameters in the sequenced configuration file; substituting a
configuration parameter from the plurality of configuration
parameters of the sequenced configuration file with the one or more
parameter entities; aligning the plurality of configuration
parameters of the sequenced configuration file based on
organization compliance data by locating configuration parameter
values from configuration files located in the environment; and
deploying a cloud service using the sequenced configuration
file.
2. The processor-implemented method of claim 1, wherein aligning
the plurality of configuration parameters of the sequenced
configuration file based on the organization compliance data
further comprises tuning of the sequenced configuration file based
on the deployment declaration.
3. The processor-implemented method of claim 1, wherein sequencing
the configuration file based on the one or more sequencing entities
comprises: training a sequence model using an opensource
repository, wherein the opensource repository comprises one or more
configuration files; determining, using the sequence model, one or
more code blocks in the opensource repository associated with each
of the detected one or more sequencing entities; and generating the
configuration file from the one or more code blocks.
4. The processor-implemented method of claim 3, wherein determining
the plurality of configuration parameters in the sequenced
configuration file comprises: determining one or more parameter
entities of the sequenced configuration file; and flagging the one
or more parameter entities of the configuration file as the
plurality of the configuration parameters based on determining that
the one or more parameter entities of the sequenced configuration
file are on a list, wherein the list was generated during training
of the sequence model.
5. The processor-implemented method of claim 1, wherein the
organization compliance data comprises one or more configuration
files of an organization.
6. The processor-implemented method of claim 5, wherein the one or
more parameter entities comprise values, variables and numbers
associated with the organization.
7. The processor-implemented method of claim 1, wherein aligning
the plurality of configuration parameters of the sequenced
configuration file based on the organization compliance data
further comprises prioritizing a value of the configuration
parameter from the deployment declaration over the value of the
configuration parameter from the organization compliance data.
8. A computer system for configuration file generation, the
computer system comprising: one or more processors, one or more
computer-readable memories, one or more computer-readable tangible
storage medium, and program instructions stored on at least one of
the one or more tangible storage medium for execution by at least
one of the one or more processors via at least one of the one or
more memories, wherein the computer system is capable of performing
a method comprising: receiving a deployment declaration for an
environment in a natural language; detecting one or more sequencing
entities and one or more parameter entities from the deployment
declaration using trained natural language processing; sequencing a
configuration file based on the one or more sequencing entities;
determining a plurality of configuration parameters in the
sequenced configuration file; substituting a configuration
parameter from the plurality of configuration parameters of the
sequenced configuration file with the one or more parameter
entities; aligning the plurality of configuration parameters of the
sequenced configuration file based on organization compliance data
by locating configuration parameter values from configuration files
located in the environment; and deploying a cloud service using the
sequenced configuration file.
9. The computer system of claim 8, wherein aligning the plurality
of configuration parameters of the sequenced configuration file
based on the organization compliance data further comprises tuning
of the sequenced configuration file based on the deployment
declaration.
10. The computer system of claim 8, wherein sequencing the
configuration file based on the one or more sequencing entities
comprises: training a sequence model using an opensource
repository, wherein the opensource repository comprises one or more
configuration files; determining, using the sequence model, one or
more code blocks in the opensource repository associated with each
of the detected one or more sequencing entities; and generating the
configuration file from the one or more code blocks.
11. The computer system of claim 10, wherein determining the
plurality of configuration parameters in the sequenced
configuration file comprises: determining one or more parameter
entities of the sequenced configuration file; and flagging the one
or more parameter entities of the configuration file as the
plurality of the configuration parameters based on determining that
the one or more parameter entities of the sequenced configuration
file are on a list, wherein the list was generated during training
of the sequence model.
12. The computer system of claim 8, wherein the organization
compliance data comprises one or more configuration files of an
organization.
13. The computer system of claim 12, wherein the one or more
parameter entities comprise values, variables and numbers
associated with the organization.
14. The computer system of claim 8, wherein aligning the plurality
of configuration parameters of the sequenced configuration file
based on the organization compliance data further comprises
prioritizing a value of the configuration parameter from the
deployment declaration over the value of the configuration
parameter from the organization compliance data.
15. A computer program product for configuration file generation,
the computer program product comprising: one or more
computer-readable tangible storage medium and program instructions
stored on at least one of the one or more tangible storage medium,
the program instructions executable by a processor, the program
instructions comprising: program instructions to receive a
deployment declaration for an environment in a natural language;
program instructions to detect one or more sequencing entities from
the deployment declaration and one or more parameter entities using
trained natural language processing; program instructions to
sequence a configuration file based on the one or more sequencing
entities; program instructions to determine a plurality of
configuration parameters in the sequenced configuration file;
program instructions to substitute a configuration parameter from
the plurality of configuration parameters of the sequenced
configuration file with the one or more parameter entities; program
instructions to align the plurality of configuration parameters of
the sequenced configuration file based on organization compliance
data by program instructions to locate configuration parameter
values from configuration files located in the environment; and
program instructions to deploy a cloud service using the sequenced
configuration file.
16. The computer program product of claim 15, wherein program
instructions to align the plurality of configuration parameters of
the sequenced configuration file based on the organization
compliance data further comprises program instructions to tune the
sequenced configuration file based on the deployment
declaration.
17. The computer program product of claim 15, wherein program
instructions to sequence the configuration file based on the one or
more sequencing entities comprises: program instructions to train a
sequence model using an opensource repository, wherein the
opensource repository comprises one or more configuration files;
program instructions to determine, using the sequence model, one or
more code blocks in the opensource repository associated with each
of the detected one or more sequencing entities; and program
instructions to generate the configuration file from the one or
more code blocks.
18. The computer program product of claim 17, wherein program
instructions to determine the plurality of configuration parameters
in the sequenced configuration file comprises: program instructions
to determine one or more parameter entities of the sequenced
configuration file; and program instructions to flag the one or
more parameter entities of the configuration file as the plurality
of the configuration parameters based on determining that the one
or more parameter entities of the sequenced configuration file are
on a list, wherein the list was generated during training of the
sequence model.
19. The computer program product of claim 15, wherein the
organization compliance data comprises one or more configuration
files of an organization.
20. The computer program product of claim 19, wherein the one or
more parameter entities comprise values, variables and numbers
associated with the organization.
Description
BACKGROUND
[0001] The present invention relates, generally, to the field of
computing, and more particularly to automatic generation of
adaptive configuration files to satisfy compliance in a cloud
infrastructure.
[0002] Cloud infrastructure or cloud computing is an on-demand
availability of computer system resources, such as data storage and
computing power, that does not require active management by the
user. Typically, cloud infrastructure comprises data centers that
are available to many users over the internet. Cloud computing
relies on sharing of resources to achieve coherence and economies
of scale. Cloud infrastructure may have various deployment models
such as private cloud, public cloud and hybrid cloud.
[0003] Hybrid cloud is typically a cloud computing environment that
uses a combination of on-premises, private cloud and third-party,
public cloud services by synthesizing between the two platforms.
The synthesizing is defined as allowing workloads to move between
private and public clouds as computing needs and costs change. The
hybrid cloud solutions give businesses greater flexibility and more
data deployment options.
[0004] Deploying software components in the cloud require service
specific configuration files, such as YAML, a human-readable data
serialization language. YAML is commonly used for configuration
files and in applications where data is being stored or
transmitted. YAML targets many of the same communications
applications as Extensible Markup Language (XML) but has a minimal
syntax which intentionally differs from Standard Generalized Markup
Language (SGML). YAML files may be analyzed using different
techniques such as by natural language processing (NLP).
[0005] NLP is a field of computer science, artificial intelligence,
and computational linguistics related to the interactions between
computers and human natural languages such as programming computers
to process large natural language corpora.
SUMMARY
[0006] According to one embodiment, a method, computer system, and
computer program product for automatic generation of adaptive
configuration files to satisfy compliance in a cloud infrastructure
is provided. The present invention may include an embodiment that
receives a deployment declaration in a natural language. The
embodiment may detect one or more sequencing entities and one or
more parameter entities using trained natural language processing.
The embodiment may sequence a configuration file based on the one
or more sequencing entities. The embodiment may determine a
plurality of configuration parameters in the sequenced
configuration file. The embodiment may substitute a configuration
parameter from the plurality of configuration parameters of the
sequenced configuration file with the one or more parameter
entities. The embodiment may align the plurality of configuration
parameters of the sequenced configuration file based on
organization compliance data and deploys a cloud service using the
sequenced configuration file.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0007] These and other objects, features and advantages of the
present invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings. The various
features of the drawings are not to scale as the illustrations are
for clarity in facilitating one skilled in the art in understanding
the invention in conjunction with the detailed description. In the
drawings:
[0008] FIG. 1 illustrates an exemplary networked computer
environment according to at least one embodiment;
[0009] FIG. 2 is an operational flowchart illustrating a
configuration file generation process according to at least one
embodiment;
[0010] FIG. 3 is a block diagram of internal and external
components of computers and servers depicted in FIG. 1 according to
at least one embodiment;
[0011] FIG. 4 depicts a cloud computing environment according to an
embodiment of the present invention; and
[0012] FIG. 5 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0013] Detailed embodiments of the claimed structures and methods
are disclosed herein; however, it can be understood that the
disclosed embodiments are merely illustrative of the claimed
structures and methods that may be embodied in various forms. This
invention may, however, be embodied in many different forms and
should not be construed as limited to the exemplary embodiments set
forth herein. In the description, details of well-known features
and techniques may be omitted to avoid unnecessarily obscuring the
presented embodiments.
[0014] Embodiments of the present invention relate to the field of
computing, and more particularly to generation of adaptive
configuration files to satisfy compliance in a cloud
infrastructure. The following described exemplary embodiments
provide a system, method, and program product to, among other
things, to receive a deployment declaration in a natural language
and generate a configuration file that is based on analyzing the
query and the existing compliance parameters of the infrastructure.
Therefore, the present embodiment has the capacity to improve the
technical field of configuration file generation that satisfies
compliance by converting the deployment declaration from a natural
language into the configuration file for deployment by using a
trained machine learning component.
[0015] As previously described, deploying software components in
the cloud require service specific configuration files, such as
YAML, a human-readable data serialization language. YAML is
commonly used for configuration files and in applications where
data is being stored or transmitted. YAML targets many of the same
communications applications as Extensible Markup Language (XML) but
has a minimal syntax which intentionally differs from Standard
Generalized Markup Language (SGML). YAML files may be analyzed
using different techniques such as by natural language processing
(NLP).
[0016] In order to deploy a software system in the cloud, a user is
required to set different knobs in one or more configuration files
such as YAML configuration files. Typically, the user modifies one
of the YAML files available on the opensource repositories by
updating the values in the already existing configuration files,
such as by updating storage mount points, storage type input
directory, output directory, memory requirement, key location in
case of an SSL deployment, and adds commands and keys from his
experience. Typically, the build of the target environment is
unknown to the user and, thus, the user executes the newly
configured YAML file in order to validate that it may lead to
generation of processes or containers that expose more information
than required, or leaks sensitive information especially in hybrid
cloud infrastructures.
[0017] As such, it may be advantageous to, among other things,
implement a system that parses a query in a natural language that
contains a deployment declaration to identify entities, and
generates a configuration file based on the identified entities and
their corresponding sequence using a machine learning-based trained
sequencing model. In addition, the system may further perform an
alignment of parameter values of the generated configuration file
that is based on the compliance needs of the organization and
deploy the service in the cloud using the generated configuration
file.
[0018] According to one embodiment, a sequence model is trained
based on the existing configuration files available online and
generates a configuration file in a YAML format from a natural
language query. According to the embodiment, the sequence model may
be a deep neural network that is trained using existing data that
comprises multiple configuration files. This sequence model is
configured to detect, from the existing configuration files, an
associated to each sequencing entity block of code. For example,
the sequencing entity name may be determined by analyzing an
annotation of each block of code that is typically left by the
developer. The generated configuration file is then updated based
on the organization compliance data of the organization and
deployed in the cloud.
[0019] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. 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.
[0020] 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.
[0021] 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.
[0022] 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, configuration data for integrated
circuitry, 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 Smalltalk, C++, or the
like, and 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.
[0023] 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.
[0024] 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.
[0025] 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.
[0026] 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 blocks 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.
[0027] The following described exemplary embodiments provide a
system, method, and program product to generate a configuration
file from a query in a natural language using NLP and trained
sequencing model.
[0028] Referring to FIG. 1, an exemplary networked computer
environment 100 is depicted, according to at least one embodiment.
The networked computer environment 100 may include client computing
device 102, a server 112 and an opensource repository 122
interconnected via a communication network 114. According to at
least one implementation, the networked computer environment 100
may include a plurality of client computing devices 102, servers
112 and opensource repositories 122, of which only one of each is
shown for illustrative brevity.
[0029] The communication network 114 may include various types of
communication networks, such as a wide area network (WAN), local
area network (LAN), a telecommunication network, a wireless
network, a public switched network and/or a satellite network. The
communication network 114 may include connections, such as wire,
wireless communication links, or fiber optic cables. It may be
appreciated that FIG. 1 provides only an illustration of one
implementation and does not imply any limitations with regard to
the environments in which different embodiments may be implemented.
Many modifications to the depicted environments may be made based
on design and implementation requirements.
[0030] Client computing device 102 may include a processor 104 and
a data storage device 106 that is enabled to host and run a
software program 108 and a configuration file generation (CFG)
program 110A and communicate with the server 112 via the
communication network 114, in accordance with one embodiment of the
invention. Client computing device 102 may be, for example, a
mobile device, a telephone, a personal digital assistant, a
netbook, a laptop computer, a tablet computer, a desktop computer,
or any type of computing device capable of running a program and
accessing a network. As will be discussed with reference to FIG. 3,
the client computing device 102 may include internal components
302a and external components 304a, respectively.
[0031] Opensource repository 122 may be one or more servers such as
server 112 that enables hosting and access to one or more
configuration files, such as YAML configuration files, that are
used for configuring a software application or service to operate
in the cloud. For example, Opendatahub may be used as an opensource
repository 122.
[0032] The server computer 112 may be a laptop computer, netbook
computer, personal computer (PC), a desktop computer, or any
programmable electronic device or any network of programmable
electronic devices capable of hosting and running a configuration
file generation (CFG) program 110B and a storage device 116 and
communicating with the client computing device 102 via the
communication network 114, in accordance with embodiments of the
invention. As will be discussed with reference to FIG. 3, the
server computer 112 may include internal components 302b and
external components 304b, respectively. The server 112 may also
operate in a cloud computing service model, such as Software as a
Service (SaaS), Platform as a Service (PaaS), or Infrastructure as
a Service (IaaS). The server 112 may also be located in a cloud
computing deployment model, such as a private cloud, community
cloud, public cloud, or hybrid cloud.
[0033] The storage device 116 may store a trained sequence model
118 and an organization compliance data 120. The trained sequence
model 118 may be a trained neural network or other trained machine
learning model that generates a configuration file from one or more
entities by accessing the corresponding to the entity code block
from the opensource repository 122. For example, the trained
sequence model 118 may generate a table having a list of available
entities and a corresponding vector that points to the code in the
opensource repository 122. The organization compliance data 120 may
be one or more configuration files or other structured or
unstructured compliance data used by the organization of the user
to enforce compliance of the services. For example, environment
compliance data 120 may store YAML files of the organization that
includes parameters and compliance requirements of the organization
such as SSL and other security requirements.
[0034] According to the present embodiment, the CFG program 110A,
110B may be a program capable of receiving a deployment declaration
in a natural language, extracting entities from the declaration and
generating a configuration file based on the extracted declaration
using a trained sequence model 118. The configuration file
generation method is explained in further detail below with respect
to FIG. 2.
[0035] Referring now to FIG. 2, an operational flowchart
illustrating a configuration file generation process 200 is
depicted according to at least one embodiment. At 202, the
configuration file generation (CFG) program 110A, 110B receives a
deployment declaration in a natural language. The deployment
declaration may be a short sentence in a natural language that
defines the user-required cloud service, such as a name of the
service, a computing framework, a deployment environment, and a
type of distributed database. According to an example embodiment,
CFG program 110A, 110B may receive the deployment declaration as a
query in a natural language by accessing an input device that is
configured to receive an input in a natural language such as a
microphone or a keyboard. In another embodiment, CFG program 110A,
110B may receive a media file that contains an input in a natural
language such as a voice or a text file. For example, a deployment
declaration may be "deploy a 3-node spark cluster in pre-prod env
with 2 node Cassandra database."
[0036] Next, at 204, the CFG program 110A, 110B detects entities
from the deployment declaration. According to an example
embodiment, the CFG program 110A, 110B may convert the received
query in a natural language into text by using a general NLP
algorithm. Then, the CFG program 110A, 110B may analyze the text to
extract two types of entities such as sequence entities and
parameter entities. The sequence entities are keywords that
correspond to a specific block of code in the configuration file
while parameter entities are values, variables and numbers used to
configure variables and define the number of components. According
to an example embodiment, the CFG program 110A, 110B may use a
trained machine learning model such as trained sequence model 118
to analyze the text and extract the entities from the text based on
previously ingested examples of configuration files from the
opensource repository 122. According to an example embodiment, the
CFG program 110A, 110B may detect numbers as parameter entities and
associated phrases as sequence entities. To continue the previous
example, the CFG program 110A, 110B may detect the "node spark
cluster", "pre prod", and "node Cassandra" as sequence entities and
"3" and "2" as parameter entities from the deployment
declaration.
[0037] Then, at 206, the CFG program 110A, 110B sequences a
configuration file based on the entities. According to an example
embodiment, the CFG program 110A, 110B may generate the
configuration file, such as a YAML file, by substituting each
detected sequence entity with an associated block of code using
trained sequence model 118. For example, the CFG program 110A, 110B
may use trained sequence model 118 and the environment compliance
data 120 to copy corresponding to the sequence entities blocks of
the code located in the opensource repository 118 and the
environment compliance data 120.
[0038] Next, at 208, the CFG program 110A, 110B determines
configuration parameters in the sequenced configuration file.
According to an example embodiment, the CFG program 110A, 110B may
search and flag all the parameters that are typically updated in
the sequenced file such as values, indexes, and paths in order to
adjust them according to the requirements of the organization. For
example, during the training period, the CFG program 110A, 110B may
maintain a list of all the parameter entities determined while
analyzing opensource repository 122. Then, when any of the entities
of the sequenced configuration file match one of the parameter
entities in the list, the CFG program 110A, 110B may flag them as
one of the configuration parameters.
[0039] Then, at 210, the CFG program 110A, 110B substitutes the
determined configuration parameters with the detected parameter
entities. According to an example embodiment, the CFG program 110A,
110B may substitute each determined configuration parameter in the
sequenced configuration file with the available corresponding
parameter entity from the deployment declaration. To continue
previous example, the sequence entity "node cassandra" may be
substituted with the following code block:
[0040] cluster name: `Test Cluster`
[0041] num_tokens: 256
[0042] hinted_handoff_enabled: true
[0043] max_hint_window_in_ms: 10800000
[0044] hinted_handoff_throttle_in_kb: 1024
[0045] max_hints_delivery_threads: 2
[0046] hints_directory: /cassandra_data/hints
[0047] hints_flush_period_in_ms: 10000
[0048] max_hints_file_size_in_mb: 128
[0049] batchlog_replay_throttle_in_kb: 1024
[0050] dynamic_snitch_badness_threshold: 0.1
[0051] request_scheduler:
org.apache.cassandra.scheduler.NoScheduler
[0052] server_encryption_options:
[0053] internode_encryption: none
[0054] keystore: conf/.keystore
[0055] keystore_password: cassandra
[0056] truststore: conf/.truststore
[0057] truststore_password: cassandra
[0058] client_encryption_options:
[0059] enabled: false
[0060] inter_dc_tcp_nodelay: false
[0061] tracetype_query_ttl: 86400
[0062] tracetype_repair_ttl: 604800
[0063] gc_warn_threshold_in_ms: 1000
[0064] enable_user_defined_functions: false
[0065] enable_scripted_user_defined_functions: false
[0066] windows_timer_interval: 1
[0067] transparent_data_encryption_options:
[0068] enabled: false
[0069] chunk_length_kb: 64
[0070] cipher: AES/CBC/PKCS5Padding
[0071] key_alias: testing:1
[0072] key_provider: [0073] class_name:
org.apache.cassandra.security.JKSKeyProvider
[0074] parameters: [0075] keystore: conf/.keystore
[0076] keystore_password: cassandra
[0077] store_type: JCEKS
[0078] key_password: cassandra
[0079] Then, at 212, the CFG program 110A, 110B aligns the
configuration parameters based on the compliance needs. According
to an example embodiment, the CFG program 110A, 110B may locate
similar configuration parameter values from the available
configuration files located in the environment compliance data 120.
For example, configuration parameters may be a path, an IP number,
memory allocation, SSL server location, login etc. In another
embodiment, the CFG program 110A, 110B may ask a user to verify the
determined parameter in case the deployment declaration parameter
value deviates from the values for the same sequencing entity in
the environment compliance data 120. In further embodiment, the CFG
program 110A, 110B may prioritize the value of the deployment
declaration over environment compliance data 120 or vise versa.
Alternatively, the CFG program 110A, 110B may perform performance
tuning of the configuration file by optimizing parameter entities
and other parameters in the configuration file so that the
application or the service will run optimally. Performance tuning
of the configuration file may improve execution speed, memory, CPU
or any other operation of the hardware. The performance tuning may
be based on the user request in the deployment declaration. For
example, a deployment declaration of "provide distributed Cassandra
DB with max storage and minimum latency", is analyzed by the CFG
program 110A, 110B that may update the "storage" and "latency"
parameters with the maximum storage and minimum latency values
found in an opensource repository.
[0080] Next, at 214, the CFG program 110A, 110B displays the
configuration file for validation. According to an example
embodiment, the CFG program 110A, 110B may run a YAML editor on the
client computing device 102 and allow the user to edit the
sequenced configuration file and, after the editing, request user
confirmation.
[0081] Then, at 216, the CFG program 110A, 110B deploys the
configuration file. According to an example embodiment, the CFG
program 110A, 110B may execute the confirmed configuration file to
deploy the service in the cloud. For example, a user may execute
the configuration file using Docker.RTM. Compose (Docker.RTM. and
all Docker.RTM.-based trademarks and logos are trademarks of
Docker, Inc. or registered trademarks of Docker, Inc. and/or its
affiliates).
[0082] It may be appreciated that FIG. 2 provides only an
illustration of one implementation and does not imply any
limitations with regard to how different embodiments may be
implemented. Many modifications to the depicted environments may be
made based on design and implementation requirements.
[0083] FIG. 3 is a block diagram 300 of internal and external
components of the client computing device 102, the server 112 and
depicted in FIG. 1 in accordance with an embodiment of the present
invention. It should be appreciated that FIG. 3 provides only an
illustration of one implementation and does not imply any
limitations with regard to the environments in which different
embodiments may be implemented. Many modifications to the depicted
environments may be made based on design and implementation
requirements.
[0084] The data processing system 302, 304 is representative of any
electronic device capable of executing machine-readable program
instructions. The data processing system 302, 304 may be
representative of a smart phone, a computer system, PDA, or other
electronic devices. Examples of computing systems, environments,
and/or configurations that may represented by the data processing
system 302, 304 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, network PCs, minicomputer systems,
and distributed cloud computing environments that include any of
the above systems or devices.
[0085] The client computing device 102 and the server 112 may
include respective sets of internal components 302 a,b and external
components 304 a,b illustrated in FIG. 3. Each of the sets of
internal components 302 include one or more processors 320, one or
more computer-readable RAMs 322, and one or more computer-readable
ROMs 324 on one or more buses 326, and one or more operating
systems 328 and one or more computer-readable tangible storage
devices 330. The one or more operating systems 328, the software
program 108 and the CFG program 110A in the client computing device
102, and the CFG program 110B in the server 112 are stored on one
or more of the respective computer-readable tangible storage
devices 330 for execution by one or more of the respective
processors 320 via one or more of the respective RAMs 322 (which
typically include cache memory). In the embodiment illustrated in
FIG. 3, each of the computer-readable tangible storage devices 330
is a magnetic disk storage device of an internal hard drive.
Alternatively, each of the computer-readable tangible storage
devices 330 is a semiconductor storage device such as ROM 324,
EPROM, flash memory or any other computer-readable tangible storage
device that can store a computer program and digital
information.
[0086] Each set of internal components 302 a,b also includes a RAY
drive or interface 332 to read from and write to one or more
portable computer-readable tangible storage devices 338 such as a
CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical
disk or semiconductor storage device. A software program, such as
the cognitive screen protection program 110A, 110B, can be stored
on one or more of the respective portable computer-readable
tangible storage devices 338, read via the respective R/W drive or
interface 332, and loaded into the respective hard drive 330.
[0087] Each set of internal components 302 a,b also includes
network adapters or interfaces 336 such as a TCP/IP adapter cards,
wireless Wi-Fi interface cards, or 3G or 4G wireless interface
cards or other wired or wireless communication links. The software
program 108 and the CFG program 110A in the client computing device
102 and the CFG program 110B in the server 112 can be downloaded to
the client computing device 102 and the server 112 from an external
computer via a network (for example, the Internet, a local area
network or other, wide area network) and respective network
adapters or interfaces 336. From the network adapters or interfaces
336, the software program 108 and the CFG program 110A in the
client computing device 102 and the CFG program 110B in the server
112 are loaded into the respective hard drive 330. The network may
comprise copper wires, optical fibers, wireless transmission,
routers, firewalls, switches, gateway computers and/or edge
servers.
[0088] Each of the sets of external components 304 a,b can include
a computer display monitor 344, a keyboard 342, and a computer
mouse 334. External components 304 a,b can also include touch
screens, virtual keyboards, touch pads, pointing devices, and other
human interface devices. Each of the sets of internal components
302 a,b also includes device drivers 340 to interface to computer
display monitor 344, keyboard 342, and computer mouse 334. The
device drivers 340, RAY drive or interface 332, and network adapter
or interface 336 comprise hardware and software (stored in storage
device 330 and/or ROM 324).
[0089] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0090] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0091] Characteristics are as Follows:
[0092] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0093] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0094] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0095] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0096] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0097] Service Models are as Follows:
[0098] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0099] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0100] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0101] Deployment Models are as Follows:
[0102] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0103] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0104] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0105] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0106] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0107] Referring now to FIG. 4, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 100 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 100 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 4 are intended to be illustrative only and that computing
nodes 100 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0108] Referring now to FIG. 5, a set of functional abstraction
layers 500 provided by cloud computing environment 50 is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 5 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
[0109] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0110] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0111] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0112] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
configuration file generation 96. Configuration file generation 96
may relate to automatically sequencing a configuration file based
on a deployment declaration in a natural language using trained
sequencing model.
[0113] 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
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.
* * * * *