U.S. patent application number 17/101419 was filed with the patent office on 2022-05-26 for mobile kube-edge auto-configuration.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Anand Shantilal Borse, Vijay Ekambaram, Padmanabha Venkatagiri Seshadri, Gandhi Sivakumar.
Application Number | 20220166830 17/101419 |
Document ID | / |
Family ID | 1000006330501 |
Filed Date | 2022-05-26 |
United States Patent
Application |
20220166830 |
Kind Code |
A1 |
Sivakumar; Gandhi ; et
al. |
May 26, 2022 |
MOBILE KUBE-EDGE AUTO-CONFIGURATION
Abstract
A method, a computer program product, and a computer system
determine a kube-edge pod configuration. The method includes
determining mobile devices in a coverage area utilizing a cloud
service. The method includes receiving polling data from the mobile
devices that include bid data indicative of a respective cost and a
respective resource availability for each of the mobile devices
operating as an edge device for the cloud service. The method
comprises includes coupling information for the mobile devices
indicative of whether at least two of the mobile devices are to be
considered coupled such that the coupled mobile devices have a
coupled cost and a coupled resource availability. The method
includes determining a utility score of the coverage area based on
the bid data and the coupling information and selecting a data
storage deployment scheme for pods of the cloud service based on
the utility score.
Inventors: |
Sivakumar; Gandhi;
(Bentleigh, AU) ; Ekambaram; Vijay; (Chennai,
IN) ; Seshadri; Padmanabha Venkatagiri; (Mysore,
IN) ; Borse; Anand Shantilal; (Glen Huntly,
AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
1000006330501 |
Appl. No.: |
17/101419 |
Filed: |
November 23, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 43/10 20130101;
H04L 67/1097 20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; H04L 12/26 20060101 H04L012/26 |
Claims
1. A computer-implemented method for determining a kube-edge pod
configuration, the method comprising: determining mobile devices in
a coverage area of a mobile environment, the mobile devices
utilizing a cloud service; receiving polling data from the mobile
devices, the polling data including bid data indicative of a
respective cost and a respective resource availability for each of
the mobile devices operating as an edge device for the cloud
service; determining coupling information for the mobile devices,
the coupling information indicative of whether at least two of the
mobile devices are to be considered coupled such that the coupled
mobile devices have a coupled cost and a coupled resource
availability in providing a data storage functionality shared
across the coupled mobile devices by aggregating available storage
of the coupled mobile devices; as a result of the at least two of
the mobile devices being coupled, determining a utility score of
the coverage area based on the bid data and the coupling
information, the utility score incorporating the coupled cost
associated with the at least two of the mobile devices being
coupled; and determining a data storage deployment scheme for pods
of the cloud service based on the utility score where: as a result
of the utility score having a first result relative to a utility
threshold, selecting a stateless data storage deployment scheme
configuring the storage to be shared across the pods, and as a
result of the utility score having a second result relative to the
utility threshold, selecting a stateful data storage deployment
scheme configuring the storage to be individual for each of the
pods.
2. The computer-implemented method of claim 1, wherein the polling
data further includes characteristic information of the mobile
devices, the characteristic information including at least one of
availability information, location information, movement
information, and technical parameters for each of the mobile
devices.
3. The computer-implemented method of claim 1, wherein the bid data
includes information directed toward at least one of mobile
resource availability, cost in being the edge device, resources
required for the cloud service, and a mobile availability time.
4. The computer-implemented method of claim 1, wherein the coupling
information is based on spatio-temporal social network relation
data of at least one of the mobile devices and users associated
with the mobile devices.
5. The computer-implemented method of claim 1, wherein determining
the mobile devices in the coverage area comprises: broadcasting a
polling signal in the coverage area, wherein receiving the polling
data from the mobile devices is a result of the mobile devices
transmitting the polling data upon receiving the polling
signal.
6. The computer-implemented method of claim 1, wherein determining
the mobile devices in the coverage area comprises: selecting the
coverage area; and transmitting a polling signal to the mobile
devices in the coverage area, wherein the polling data is from the
mobile devices receiving the polling signal.
7. (canceled)
8. A computer program product for determining a kube-edge pod
configuration, the computer program product comprising: one or more
non-transitory computer-readable storage media and program
instructions stored on the one or more non-transitory
computer-readable storage media capable of performing a method, the
method comprising: determining mobile devices in a coverage area of
a mobile environment, the mobile devices utilizing a cloud service;
receiving polling data from the mobile devices, the polling data
including bid data indicative of a respective cost and a respective
resource availability for each of the mobile devices operating as
an edge device for the cloud service; determining coupling
information for the mobile devices, the coupling information
indicative of whether at least two of the mobile devices are to be
considered coupled such that the coupled mobile devices have a
coupled cost and a coupled resource availability in providing a
data storage functionality shared across the coupled mobile devices
by aggregating available storage of the coupled mobile devices; as
a result of the at least two of the mobile devices being coupled,
determining a utility score of the coverage area based on the bid
data and the coupling information, the utility score incorporating
the coupled cost associated with the at least two of the mobile
devices being coupled; and determining a data storage deployment
scheme for pods of the cloud service based on the utility score
where: as a result of the utility score having a first result
relative to a utility threshold, selecting a stateless data storage
deployment scheme configuring the storage to be shared across the
pods, and as a result of the utility score having a second result
relative to the utility threshold, selecting a stateful data
storage deployment scheme configuring the storage to be individual
for each of the pods.
9. The computer program product of claim 8, wherein the polling
data further includes characteristic information of the mobile
devices, the characteristic information including at least one of
availability information, location information, movement
information, and technical parameters for each of the mobile
devices.
10. The computer program product of claim 8, wherein the bid data
includes information directed toward at least one of mobile
resource availability, cost in being the edge device, resources
required for the cloud service, and a mobile availability time.
11. The computer program product of claim 8, wherein the coupling
information is based on spatio-temporal social network relation
data of at least one of the mobile devices and users associated
with the mobile devices.
12. The computer program product of claim 8, wherein determining
the mobile devices in the coverage area comprises: broadcasting a
polling signal in the coverage area, wherein receiving the polling
data from the mobile devices is a result of the mobile devices
transmitting the polling data upon receiving the polling
signal.
13. The computer program product of claim 8, wherein determining
the mobile devices in the coverage area comprises: selecting the
coverage area; and transmitting a polling signal to the mobile
devices in the coverage area, wherein the polling data is from the
mobile devices receiving the polling signal.
14. (canceled)
15. A computer system for determining a kube-edge pod
configuration, the computer system comprising: one or more computer
processors, one or more computer-readable storage media, and
program instructions stored on the one or more of the
computer-readable storage media for execution by at least one of
the one or more processors capable of performing a method, the
method comprising: determining mobile devices in a coverage area of
a mobile environment, the mobile devices utilizing a cloud service;
receiving polling data from the mobile devices, the polling data
including bid data indicative of a respective cost and a respective
resource availability for each of the mobile devices operating as
an edge device for the cloud service; determining coupling
information for the mobile devices, the coupling information
indicative of whether at least two of the mobile devices are to be
considered coupled such that the coupled mobile devices have a
coupled cost and a coupled resource availability in providing a
data storage functionality shared across the coupled mobile devices
by aggregating available storage of the coupled mobile devices; as
a result of the at least two of the mobile devices being coupled,
determining a utility score of the coverage area based on the bid
data and the coupling information, the utility score incorporating
the coupled cost associated with the at least two of the mobile
devices being coupled; and determining a data storage deployment
scheme for pods of the cloud service based on the utility score
where: as a result of the utility score having a first result
relative to a utility threshold, selecting a stateless data storage
deployment scheme configuring the storage to be shared across the
pods, and as a result of the utility score having a second result
relative to the utility threshold, selecting a stateful data
storage deployment scheme configuring the storage to be individual
for each of the pods.
16. The computer system of claim 15, wherein the polling data
further includes characteristic information of the mobile devices,
the characteristic information including at least one of
availability information, location information, movement
information, and technical parameters for each of the mobile
devices.
17. The computer system of claim 15, wherein the bid data includes
information directed toward at least one of mobile resource
availability, cost in being the edge device, resources required for
the cloud service, and a mobile availability time.
18. The computer system of claim 15, wherein the coupling
information is based on spatio-temporal social network relation
data of at least one of the mobile devices and users associated
with the mobile devices.
19. The computer system of claim 15, wherein determining the mobile
devices in the coverage area comprises: broadcasting a polling
signal in the coverage area, wherein receiving the polling data
from the mobile devices is a result of the mobile devices
transmitting the polling data upon receiving the polling
signal.
20. The computer system of claim 15, wherein determining the mobile
devices in the coverage area comprises: selecting the coverage
area; and transmitting a polling signal to the mobile devices in
the coverage area, wherein the polling data is from the mobile
devices receiving the polling signal.
Description
BACKGROUND
[0001] The exemplary embodiments relate generally to kube-edge
computing, and more particularly to determining a kube-edge pod
configuration to enable high coverage and availability with minimal
incentives.
[0002] In a cloud environment, edge computing enables processing
and/or storage of data to be provided closer to the devices where
such operations are being performed. Accordingly, edge computing
eliminates the need for data that is to be processed and/or stored
to be transmitted to a central location (e.g., a central cloud
server) which may be physically located a significant distance away
from the devices. Although this configuration may not provide a
substantial change to the services being provided on an individual
device perspective, the explosion of Internet of Things (IoT) and
use of such devices exponentially increases requirements in
utilizing the cloud services (e.g., increase in latency resulting
in lower quality, bandwidth costs, etc.). Therefore, edge computing
may be provided to alleviate such issues.
[0003] Multi-access edge computing (MEC) provides an approach where
cloud-computing capabilities and an IT service environment are
provided at the edge of the network. MEC provides an ecosystem in
which applications and services towards devices may be flexibly and
rapidly deployed. 5G is the next generation of broadband cellular
networks that purportedly allows for increased communication rates.
MEC has implementations for various networks and 5G has been
expanding as service providers adopt the most current and
technologically advanced system for their customers. However, MEC
and 5G are considered disrupting technologies on their own but,
when combined, will become a powerful force in the world of
computing. The emergence of 5G networking capabilities will
increase the number of connected devices on a network, which spurs
the need for edge computing to help distribute networking demands.
Applications that rely heavily on a consistent network connection,
rapid deployment, and low latency include burgeoning technologies
such as artificial intelligence (AI), IoT, virtual reality (VR),
augmented reality (AR), etc. MEC and 5G networking together allow
for the simultaneous usage of a massive number of connected
technologies without incurring network outages due to traffic
bottlenecks. However, current edge deployments may not be properly
configured to dynamically address providing the edge services in
various coverage areas.
SUMMARY
[0004] The exemplary embodiments disclose a method, a computer
program product, and a computer system for determining a kube-edge
pod configuration. The method comprises determining mobile devices
in a coverage area of a mobile environment, the mobile devices
utilizing a cloud service. The method comprises receiving polling
data from the mobile devices. The polling data includes bid data
indicative of a respective cost and a respective resource
availability for each of the mobile devices operating as an edge
device for the cloud service. The method comprises determining
coupling information for the mobile devices. The coupling
information is indicative of whether at least two of the mobile
devices are to be considered coupled such that the coupled mobile
devices have a coupled cost and a coupled resource availability.
The method comprises determining a utility score of the coverage
area based on the bid data and the coupling information. The method
comprises selecting a data storage deployment scheme for pods of
the cloud service based on the utility score.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0005] The following detailed description, given by way of example
and not intended to limit the exemplary embodiments solely thereto,
will best be appreciated in conjunction with the accompanying
drawings, in which:
[0006] FIG. 1 depicts an exemplary schematic diagram of a kube-edge
configuration system 100, in accordance with the exemplary
embodiments.
[0007] FIG. 2 depicts an exemplary flowchart of a method 200
illustrating the operations of the configuration server 130 of the
kube-edge configuration system 100 in determining a kube-edge pod
configuration, in accordance with the exemplary embodiments.
[0008] FIG. 3 depicts an exemplary block diagram depicting the
hardware components of the kube-edge configuration system 100 of
FIG. 1, in accordance with the exemplary embodiments.
[0009] FIG. 4 depicts a cloud computing environment, in accordance
with the exemplary embodiments.
[0010] FIG. 5 depicts abstraction model layers, in accordance with
the exemplary embodiments.
[0011] The drawings are not necessarily to scale. The drawings are
merely schematic representations, not intended to portray specific
parameters of the exemplary embodiments. The drawings are intended
to depict only typical exemplary embodiments.
[0012] In the drawings, like numbering represents like
elements.
DETAILED DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[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. The
exemplary embodiments are only illustrative and may, however, be
embodied in many different forms and should not be construed as
limited to the exemplary embodiments set forth herein. Rather,
these exemplary embodiments are provided so that this disclosure
will be thorough and complete, and will fully convey the scope to
be covered by the exemplary embodiments to those skilled in the
art. In the description, details of well-known features and
techniques may be omitted to avoid unnecessarily obscuring the
presented embodiments.
[0014] References in the specification to "one embodiment", "an
embodiment", "an exemplary embodiment", etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may not necessarily include
the particular feature, structure, or characteristic. Moreover,
such phrases are not necessarily referring to the same embodiment.
Further, when a particular feature, structure, or characteristic is
described in connection with an embodiment, it is submitted that it
is within the knowledge of one skilled in the art to implement such
feature, structure, or characteristic in connection with other
embodiments whether or not explicitly described.
[0015] In the interest of not obscuring the presentation of the
exemplary embodiments, in the following detailed description, some
processing steps or operations that are known in the art may have
been combined together for presentation and for illustration
purposes and in some instances may have not been described in
detail. In other instances, some processing steps or operations
that are known in the art may not be described at all. It should be
understood that the following description is focused on the
distinctive features or elements according to the various exemplary
embodiments.
[0016] The exemplary embodiments are directed to a method, computer
program product, and system for determining a kube-edge
configuration. As will be described in further detail below, the
exemplary embodiments may provide a mechanism to auto-configure a
kube-edge pod configuration in a coverage area of a mobile
environment where mobile devices may be present. The exemplary
embodiments may determine the kube-edge pod configuration according
to a storage deployment scheme such as between a stateful
deployment or a stateless deployment dynamically by considering the
varying and dynamic conditions due to the mobile nature of the
devices in the coverage area. Key benefits of the exemplary
embodiments may include dynamically determining the configuration
and departing from a static form of kube-edge deployment that may
operate seamlessly in mobile environments. Detailed implementation
of the exemplary embodiments follows.
[0017] Kube-edge deployments in a cloud environment are generally
static in nature. Specifically, with regard to a pod configuration,
the kube-edge deployment may have a configuration that is fixed and
used for the pods of the Kubernetes. For example, the storage
across pods may be selected to be shared. In another example, the
storage across pods may be selected to be individual for pods. The
kube-edge deployment in conventional systems substantially fixes
one of these configurations to be used by the Kubernetes. However,
developments in, for example, Internet of Things (IoT) has raised a
need for mobile based deployments that enable kube-edge based
application program interfaces (APIs) in movable vehicles that lead
to faster 5G networks in vehicular networks or enable better
coverage to remote areas through vehicular networks. Accordingly,
the fixed or static approach to kube-edge deployments have
drawbacks and do not provide a seamless operation in mobile
environments.
[0018] Conventional approaches in deployment kube-edge systems
relate to various mechanisms that purportedly improve how kube-edge
operates. For example, a conventional approach describes how mobile
edge computing (MEC) may be integrated with 5G technology. In
another example, a conventional approach describes fusion
management of an edge cloud where a kube-edge program meets the
needs of resource limited edge nodes being managed into a central
Kubernetes system. In a further example, a conventional approach
describes a Kubernetes native infrastructure and operator framework
for 5G edge cloud computing. In yet another example, conventional
approaches describe extending cloud or a Kubernetes ecosystem from
cloud to edge. However, these conventional approaches do not
provide any mechanism to leverage mobility patterns of mobile edge
nodes and a communication interaction with peer edge nodes to
generate a service-level-objective conforming kube-edge pod
configurations that span a spectrum of stateless-to-stateful
options. These conventional approaches further do not consider
standard parameters with respect to resource and cost. Accordingly,
the conventional approaches do not understand the coupling between
edge devices from a spatio-temporal social network analysis and
leverage this information to provide a better trade-off between
stateful vs stateless deployments in kube-edges leading to better
trade-off decisions.
[0019] The exemplary embodiments are configured to address mobile
environments and coverage areas involving mobile environments that
have varying resources based on the mobile edge device availability
and respective configurations. Therefore, the exemplary embodiments
are directed to providing an approach in which a same kube-edge pod
configuration that does not apply generically to add mobile
environments. In mobile environments, for a given location or
coverage area, different mobile devices may be available. Different
devices may have different costs and resources associated
therewith. Furthermore, availability time for these devices may
constantly be changing as a mobile device that is present in a
coverage area at one moment may be absent in another moment.
Therefore, a plain static selection of devices do not always
provide an optimal solution. The exemplary embodiments utilize a
dynamic approach in which a more sophisticated analysis is used.
The exemplary embodiments therefore provide a selection between
stateful and stateless deployments that ordinarily include complex
decisions due to the mobile nature of the mobile devices. In this
manner, the exemplary embodiments are configured to discover the
optimal kube-edge pod configuration for a given mobile environment
to enable high coverage and availability with minimal incentives.
The exemplary embodiments are also configured to discover the
optimal kube-edge pod configuration when multiple devices are
polling for at the same time.
[0020] The exemplary embodiments are described with particular
reference to kube-edge and pod configurations that are associated
with Kubernetes in a cloud environment. However, the exemplary
embodiments being directed to such an arrangement is only for
illustrative purposes. The exemplary embodiments may be utilized
and/or modified to be used in any networking environment in which
an edge system may be utilized and the features of the exemplary
embodiments may be properly applied to determine an optimal
configuration that suits the coverage area being processed. The
exemplary embodiments are also described with regard to stateful
and stateless deployments associated with storage functionalities
for the pods. However, the use of the stateful and stateless
deployments as well as the storage functionality is only
illustrative. The exemplary embodiments may be utilized and/or
modified for other configurations that may be directed toward other
functionalities.
[0021] FIG. 1 depicts a kube-edge configuration system 100, in
accordance with the exemplary embodiments. According to the
exemplary embodiments, the kube-edge configuration system 100 may
include one or more smart devices 110, cloud services 120, and a
configuration server 130, which may all be interconnected via a
network 108. While programming and data of the exemplary
embodiments may be stored and accessed remotely across several
servers via the network 108, programming and data of the exemplary
embodiments may alternatively or additionally be stored locally on
as few as one physical computing device or amongst other computing
devices than those depicted. The kube-edge configuration system 100
represents a communication arrangement in which the components
thereof are configured to exchange data with one another in a
direct or indirect manner.
[0022] In the exemplary embodiments, the network 108 may be a
communication channel capable of transferring data between
connected devices. Accordingly, the components of the kube-edge
configuration system 100 may represent network components or
network devices interconnected via the network 108. In the
exemplary embodiments, the network 108 may be the Internet,
representing a worldwide collection of networks and gateways to
support communications between devices connected to the Internet.
Moreover, the network 108 may utilize various types of connections
such as wired, wireless, fiber optic, etc. which may be implemented
as an intranet network, a local area network (LAN), a wide area
network (WAN), or a combination thereof. In further embodiments,
the network 108 may be a Bluetooth network, a WiFi network, or a
combination thereof. In yet further embodiments, the network 108
may be a telecommunications network used to facilitate telephone
calls between two or more parties comprising a landline network, a
wireless network, a closed network, a satellite network, or a
combination thereof. In general, the network 108 may represent any
combination of connections and protocols that will support
communications between connected devices. For example, the network
108 may also represent direct or indirect wired or wireless
connections between the components of the kube-edge configuration
system 100 that do not utilize the network 108.
[0023] In the exemplary embodiments, the one or more smart devices
110 may include a edge processing client 112, and may be an
enterprise server, a laptop computer, a notebook, a tablet
computer, a netbook computer, a personal computer (PC), a desktop
computer, a server, a personal digital assistant (PDA), a rotary
phone, a touchtone phone, a smart phone, a mobile phone, a virtual
device, a thin client, an Internet of Things (IoT) device, or any
other electronic device or computing system capable of receiving
and sending data to and from other computing devices. While the
smart device 110 is shown as a single device, in other embodiments,
the smart device 110 may be comprised of a cluster or plurality of
computing devices, in a modular manner, etc., working together or
working independently. The smart device 110 is described in greater
detail as a hardware implementation with reference to FIG. 3, as
part of a cloud implementation with reference to FIG. 4, and/or as
utilizing functional abstraction layers for processing with
reference to FIG. 5.
[0024] The smart devices 110 may represent any mobile device that
may be part of a mobile environment that may be present in a
predefined coverage area. The coverage areas may be predefined in a
variety of manners based on various factors (e.g., physical area
boundary, load factors, etc.). With regard to the kube-edge aspect
of the mobile environment directed toward a cloud, the smart
devices 110 may also represent edge devices configured to provide
edge processing. In this manner, the smart devices 110 may also
include edge gateways within the edge-computing infrastructure.
[0025] In the exemplary embodiments, the edge processing client 112
may act as a client in a client-server relationship and may be a
software, hardware, and/or firmware based application capable of
exchanging data utilized in determining a kube-edge pod
configuration for a coverage area in a mobile environment
particularly where the smart device 110 is located, the data being
exchanged via the network 108. In embodiments, the edge processing
client 112 may exchange polling data and other associated
information, and utilize various wired and/or wireless connection
protocols for data transmission and exchange associated with data
used for modifying a version of an application, including
Bluetooth, 2.4 gHz and 5 gHz internet, near-field communication,
Z-Wave, Zigbee, etc.
[0026] The edge processing client 112 may be configured to
communicate polling data. In an exemplary embodiment, upon entering
a coverage area, the edge processing client 112 may generate
polling data that is transmitted to, for example, the configuration
server 130. In this exemplary embodiment, the smart device 110 may
include network operations in which communications with network
components (e.g., association processes, handshakes, roaming, etc.)
may include information as to whether the smart device 110 has
entered a new coverage area as well as an identity of the coverage
area. In another exemplary embodiment, the edge processing client
112 may receive a request for the polling data, generating the
polling data, and transmit a response including the polling data
to, for example, the configuration server 130. In this exemplary
embodiment, the smart device 110 may have been identified as being
in a coverage area for which the features of the exemplary
embodiments are to be provided.
[0027] The edge processing client 112 may generate the polling data
to include various types of information. For example, the polling
data may include availability information of the smart device 110
to be an edge device, location information of the smart device 110,
movement information of the smart device 110, etc. as well as
technical parameters or information of the smart device 110 that is
generating the polling data. The technical information may include
available mobile resources (e.g., CPU, disk space, etc.), cost
associated with being used as an edge device, etc. The polling data
may also be used to ascertain other information about the smart
device 110. For example, the location information and the movement
information may be used to determine mobile availability time that
the smart device 110 remains in the coverage area being processed.
The edge processing client 112 may provide any information that is
to be included in the polling data such that an optimal
determination of the kube-edge pod configuration may be
performed.
[0028] The cloud services 120 may represent any cloud service
provider and the services that are rendered for users associated
with the smart devices 110. Those skilled in the art will
understand the various components, devices, connections, etc. that
may be involved in providing the cloud services 120. For example,
the cloud services 120 may utilize cloud computing where a network
of remote servers and other network devices hosted through a
network (e.g., Internet) may provide storage, management,
processing, etc. of data in contrast to a local server or network
maintained by an entity. The exemplary embodiments may be utilized
and/or modified to be used with the cloud services 120 and may
encompass any cloud service that is available to the users of the
smart devices 110.
[0029] The cloud services 120 may have various characteristics
associated with a cloud. For example, in providing the cloud
services 120, the cloud may include Kubernetes that define building
blocks that provide mechanisms configured to deploy, maintain,
scale, etc. a plurality of applications based on various metrics.
Those skilled in the art will understand the various technical
aspects associated with Kubernetes and their function in the cloud.
In another example, the cloud may include pods that group
containerized components where each container functionally
decouples applications from underlying host infrastructure
components. The pods may comprise one or more containers that may
be co-located on a host machine with the capability of sharing
resources. With regard to kube-edge systems such as those for which
the exemplary embodiments may be applied, the pods may be
configured in a stateful or a stateless deployment in which storage
of data may be for individual pods or shared across a plurality of
pods, respectively.
[0030] In the exemplary embodiments, the configuration server 130
may include an identification program 132, a bid program 134, a
coupling program 136, and a selecting program 138, and act as a
server in a client-server relationship with the edge processing
client 112. The configuration server 130 may be an enterprise
server, a laptop computer, a notebook, a tablet computer, a netbook
computer, a PC, a desktop computer, a server, a PDA, a rotary
phone, a touchtone phone, a smart phone, a mobile phone, a virtual
device, a thin client, an IoT device, or any other electronic
device or computing system capable of receiving and sending data to
and from other computing devices. While the configuration server
130 is shown as a single device, in other embodiments, the
configuration server 130 may be comprised of a cluster or plurality
of computing devices, working together or working independently.
The configuration server 130 is described in greater detail as a
hardware implementation with reference to FIG. 3, as part of a
cloud implementation with reference to FIG. 4, and/or as utilizing
functional abstraction layers for processing with reference to FIG.
5.
[0031] According to the exemplary embodiments, the configuration
server 130 may discover and enable a kube-edge pod configuration
with a focus on stateful vs stateless deployment for a given mobile
environment which enables high coverage and availability with
minimal incentives in the context of multi-device availability and
social-network consideration in the mobile environment.
[0032] In the exemplary embodiments, the identification program 132
may be a software, hardware, and/or firmware application configured
to select a coverage area in a mobile environment and determine
which of the smart devices 110 are located in the selected coverage
area. As described above with regard to the edge processing client
112, the identification program 132 may poll the smart devices 110
that are located in the selected coverage area and receive polling
data in an active or passive manner. For example, in an active
manner, when the coverage area has been selected, the
identification program 132 may poll the coverage area and request
that devices provide polling data. The identification program 132
may generate a polling signal that is broadcast in the selected
coverage area such that each of the smart devices 110 in the
selected coverage area receives the polling signal. Alternatively,
the identification program 132 may receive information from network
components that provide identifications of the smart devices 110
for which the identification program 132 may selectively transmit a
polling signal to these smart devices 110. In another example, in a
passive manner, the identification program 132 may be assigned one
or more coverage areas in the mobile environment. Each time that
the smart devices 110 enter the coverage area assigned to the
identification program 132, the identification program 132 may
receive polling data from the smart devices 110. In such a
scenario, the identification program 132 may passively broadcast
the polling signal (e.g., continuously, at intermittent time
intervals, etc.).
[0033] In the exemplary embodiments, the bid program 134 may be a
software, hardware, and/or firmware application configured to
process the polling data that includes bid information from the
smart devices 110 in the coverage area of the mobile environment.
As described above, the polling data may include bid information
that includes various types of inputs. For example, the polling
data may include information related to the smart device 110
including availability information, location information, movement
information, technical parameters, etc. The availability
information may indicate whether the smart device 110 is available
to be used as an edge device for the kube-edge pod configuration.
The location information may indicate a physical location within
the coverage area. The technical parameters may provide various
technical characteristics of the smart device 110 (e.g., model,
processor type, graphics card, etc.). The bid information included
in the polling data (e.g., when the smart device 110 is available
as an edge device) may include information related to available
resources and expected costs in offering the service (e.g., of
being an edge device). For example, the bid information may include
mobile resources (e.g., CPU, disk space, etc.), cost (e.g.,
processing costs, financial costs, etc.), min and max resources
required for the service (e.g., one of the cloud services 120),
mobile availability time (e.g., a duration that the smart device
110 remains in the coverage area, a duration that the smart device
110 is available to be an edge device for any other reason,
etc.).
[0034] Using the polling information and the bid data, the bid
program 134 may generate a utility score based on the respective
costs and resources of the smart devices 110 in the coverage area.
The utility score may be directed to the coverage area that
incorporates the various information of the smart devices in the
coverage area. The utility score may be indicative of whether to
utilize a stateless deployment for the kube-edge pod configuration
(e.g., storage is shared across the pods) or a stateful deployment
for the kube-edge pod configuration (e.g., storage is individual
for each pod). The bid program 134 may generate the utility score
along a range of values and utilize a utility threshold where a
utility score on one side of the utility threshold is indicative of
the stateless deployment and a utility score on the other side of
the utility threshold is indicative of the stateful deployment.
[0035] In an exemplary implementation, the smart devices 110 may be
substantially static in the coverage area such that the resources
of each of the smart devices 110 are relatively substantial, the
costs are relatively minimal, the required resources of the cloud
service are manageable, and the smart devices 110 are available at
least for a duration that the cloud service is to be used. In this
exemplary implementation, the bid program 134 may process the bid
information and determine a utility score that is toward one
extreme of the utility score range. For example, the utility score
may range from 0 to 1 where 1 is indicative of using the stateful
deployment and 0 is indicative of using the stateless deployment.
The utility score may also include a utility threshold set in a
midpoint of the range (e.g., 0.5) although the threshold may be
positioned anywhere along the range for various reasons (e.g.,
biasing the deployment towards one configuration over another,
consideration of the types of bid information, etc.). Thus, in this
exemplary implementation, the bid program 134 may determine the
utility score to be above 0.5, closer to 1. Thus, based on the
utility score alone that utilizes the costs and resources of the
smart devices 110 in the coverage area, the stateful deployment may
be determined for this scenario. In another exemplary
implementation, the smart devices 110 may be substantially mobile
and ephemeral in the coverage area such that the resources of each
of the smart devices 110 are relatively unstable, the costs are
varying, the required resources of the cloud service may or may not
be manageable, and the smart devices 110 are available for
indeterminate amounts of time that may or may not cover a duration
that the cloud service is to be used. In this exemplary
implementation, the bid program 134 may process the bid information
and determine a utility score that is toward the other extreme of
the utility score range. For example, the bid program 134 may
determine the utility score to be below 0.5, closer to 0. Thus,
based on the utility score alone that utilizes the costs and
resources of the smart devices 110 in the coverage area, the
stateless deployment may be determined for this scenario.
[0036] The bid program 134 may also determine how to sort the smart
devices 110 in the coverage area based on the utility score and the
bid information. For example, when the utility score is indicative
of a stateful deployment, the bid program 134 may identify which of
the smart devices 110 are to be associated with a pod for which
individual storage operations are to be used. In another example,
when the utility score is indicative of a stateless deployment, the
bid program 134 may identify which of the smart devices 110 are to
be associated with the various pods for which storage is shared for
these pods.
[0037] In the exemplary embodiments, the coupling program 136 may
be a software, hardware, and/or firmware application configured to
determine whether two or more smart devices 110 may be considered
as part of a set (e.g., coupling). The coupling program 136 may
utilize the polling data and/or other available information (e.g.,
publicly available information such as on social media sites). The
coupling program 136 may analyse the spatio-temporal social network
relation between two or more users associated with the smart
devices 110 and/or between two or more of the smart devices 110
themselves as to whether a given pair may be coupled or a plurality
of smart devices 110 may be considered to be a set. For example,
the coupling program 136 may receive information that a married
couple is traveling together in the same vehicle and passing
through the coverage area. The coupling program 136 may therefore
determine that the smart devices 110 associated with the married
couple may be coupled for purposes of the exemplary embodiments. In
another example, the coupling program 136 may determine that a
group of smart devices 110 are traveling together on a common
transport (e.g., a sports team on a bus) such that these smart
devices 110 may be coupled or grouped as a set for purposes of the
exemplary embodiments.
[0038] The coupling program 136 may utilize coupling information as
determined through the various analyses of the polling data to
modify the utility score determined by the bid program 134. For
example, the coupling program 136 may determine that a pair of
smart devices 110 may be coupled. The coupling program 136 may
subsequently determine that certain resources may be aggregated or
otherwise considered to be grouped based on the coupling. In this
manner, for the smart devices 110 that are coupled or grouped, the
coupled or grouped smart devices 110 may have a coupled cost and/or
coupled resource availability. In a particular example, the
coupling program 136 may determine a possibility of saving
resources by using a shared disk in kube-edges by the smart devices
110 that are coupled. In this manner, the coupling program 136
and/or the bid program 134 may utilize this updated cost and/or
resource information of the smart devices 110 in the coverage area
to re-evaluate the utility score as a modified utility score based
on the coupling information. The modified utility score may be
assessed relative to the utility threshold and a corresponding
deployment type may be determined.
[0039] In the exemplary embodiments, the selecting program 138 may
be a software, hardware, and/or firmware application configured to
implement the determination of the deployment to be used in the
kube-edge pod configuration. Using the modified utility score
(e.g., when coupling information is available to update the utility
score), the selecting program 138 may determine which deployment to
utilize for the coverage area given the current conditions of the
smart devices 110 that are present therein. As described above, the
bid program 134 and/or the coupling program 136 may also determine
the manner in which the smart devices 110 are to be sorted and/or
assigned to perform operations for the cloud service 120. For
example, for storage purposes, when a stateful deployment is to be
used for the kube-edge pod configuration, select one or more of the
smart devices 110 may be selected for the storage of data for a
given pod (e.g., based on the cost and/or resource information of
all present smart devices 110 in the coverage area). In another
example, when a stateless deployment is to be used for the
kube-edge pod configuration, select one or more of the smart
devices 110 may be selected for the storage of data for a plurality
of pods where storage is shared across the pods (e.g., based on the
cost and/or resource information of all present smart devices 110
in the coverage area)
[0040] FIG. 2 depicts an exemplary flowchart of a method 200
illustrating the operations of the configuration server 130 of the
kube-edge configuration system 100 in determining a kube-edge pod
configuration, in accordance with the exemplary embodiments. The
method 200 may relate to operations that are performed by the
identification program 132, the bid program 134, the coupling
program 136, and the selecting program 138 of the configuration
server 130. Accordingly, the method 200 will be described from the
perspective of the configuration server 130.
[0041] The configuration server 130 polls for devices in a coverage
area (step 202). The configuration server 130 may poll for the
smart devices 110 in a coverage in a variety of manners. In an
exemplary implementation, the configuration server 130 may utilize
an active approach in which the configuration server 130 selects
the coverage area and broadcasts or transmits a polling signal for
the selected coverage area. The smart devices 110 that are
currently located in the coverage area and capable of receiving the
polling signal may respond accordingly. In another exemplary
implementation, the configuration server 130 may utilize a passive
approach in which the configuration server 130 may be responsible
for a coverage area and continuously broadcasts the polling signal
and awaits responses from smart devices 110 that are in the
coverage area. In a further exemplary implementation, the
configuration server 130 may be configured with the active and the
passive approach.
[0042] The configuration server 130 determines whether there are
any devices present in the coverage area for which the kube-edge
pod configuration is to be determined (decision 204). Based on the
polling signal and the responses to the polling signal, the
configuration server 130 may determine the presence of any of the
smart devices 110 in the coverage area. As a result of there being
no smart devices 110 in the coverage area (decision 204, "NO"
branch), the configuration server 130 may utilize a default
deployment. The default deployment may be a preselected deployment
as selected by, for example, an administrator. The default
deployment may also be dynamically selected based on historical
deployments and selecting a more frequently used deployment.
[0043] As a result of there being at least one of the smart devices
110 in the coverage area (decision 204, "YES" branch), the
configuration server 130 requests bid data from each device in
coverage area (step 206). In the response to the polling signal,
the configuration server 130 may receive polling data from the
smart devices 110 in the coverage area. The polling data may
include information about the smart devices 110 (e.g., availability
information, location information, movement information, etc.) as
well as bid information (e.g., information related to cost and/or
resources of the smart devices 110).
[0044] The configuration server 130 determines whether bid
information or whether the smart devices 110 are available for the
kube-edge pod configuration (decision 208). As a result of the
configuration server 130 not receiving bid data or determining that
none of the smart devices 110 are available (decision 208, "NO"
branch), the configuration server 130 may again defer to a default
deployment.
[0045] As a result of the configuration server 130 receiving bid
data and determining that at least one of the smart devices 110 are
available (decision 208, "YES" branch), the configuration server
130 determines a utility score for the coverage area based on the
bid information (step 210). With the bid information being
indicative of the costs and resources of the smart devices 110 as
well as the cloud service 120, the configuration server 130 may
determine the utility score as an optimization analysis.
[0046] The configuration server 130 determines whether there are
any spatio-temporal social network relation data between or among
the smart devices 110 (step 212). Specifically, the configuration
server 130 determines coupling information as to whether a pair of
the smart devices 110 may be coupled or a plurality of the smart
devices 110 may be considered a set. In coupling or grouping the
smart devices 110, the costs and/or resources may be re-evaluated
such that costs may be reduced or resources may be saved. For
example, if the smart devices 110 may be coupled, a storage
operation may be shared by the disks of the coupled smart devices
110. In this manner, the costs and/or resources of the smart
devices 110 in the coverage area may be re-evaluated. Accordingly,
the configuration server 130 may determine a modified utility score
based on the utility score and the coupling information.
[0047] Using the modified utility score (e.g., when coupling
information is available) or the utility score (e.g., when coupling
information is unavailable), the configuration server 130
determines a configuration to be used for coverage area (step 214).
Specifically, the configuration server 130 may determine whether
the kube-edge pod configuration is to be stateful where storage is
individual for each pod or stateless where storage is shared across
the pods, the storage in either deployment being through one or
more of the smart devices 110. The configuration server 130 may
also determine how to provide the selected kube-edge pod
configuration deployment by determining which of the smart devices
110 are to be used for the storage functionality.
[0048] The exemplary embodiments are configured to determine a
kube-edge pod configuration for a coverage area of a mobile
environment in which one or more mobile devices may be present. As
mobile environments entail dynamic conditions with mobile devices
entering and exiting the coverage area as well as each mobile
device having dynamic costs and resources associated therewith in
being an edge device, the exemplary embodiments dynamically
determine the kube-edge pod configuration through a selection
between a stateful deployment or a stateless deployment based on
the existing conditions of the coverage area.
[0049] According to various features, the exemplary embodiments
leverage spatio-temporal social network analysis in the context of
multi-device availability for discovering and enabling a best
kube-edge pod configuration for a given mobile environment which
enables high coverage and availability with minimal incentives. The
exemplary embodiments also leverage mobility patterns of mobile
nodes and their communication interaction with peer nodes so as to
generate service-level-objective conforming kube-edge pod
configurations which span the spectrum of stateless-to-stateful
options. The exemplary embodiments further consider standard
parameters with respect to resource and cost as well as understand
a potential to couple or group devices based on spatio-temporal
social network analysis which is leveraged to better trade-off
between stateful vs stateless in kube-edges leading to better
trade-off decisions.
[0050] FIG. 3 depicts a block diagram of devices within the
kube-edge configuration system 100 of FIG. 1, in accordance with
the exemplary embodiments. 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 environment may be made.
[0051] Devices used herein may include one or more processors 02,
one or more computer-readable RAMs 04, one or more
computer-readable ROMs 06, one or more computer readable storage
media 08, device drivers 12, read/write drive or interface 14,
network adapter or interface 16, all interconnected over a
communications fabric 18. Communications fabric 18 may be
implemented with any architecture designed for passing data and/or
control information between processors (such as microprocessors,
communications and network processors, etc.), system memory,
peripheral devices, and any other hardware components within a
system.
[0052] One or more operating systems 10, and one or more
application programs 11 are stored on one or more of the computer
readable storage media 08 for execution by one or more of the
processors 02 via one or more of the respective RAMs 04 (which
typically include cache memory). In the illustrated embodiment,
each of the computer readable storage media 08 may be a magnetic
disk storage device of an internal hard drive, CD-ROM, DVD, memory
stick, magnetic tape, magnetic disk, optical disk, a semiconductor
storage device such as RAM, ROM, EPROM, flash memory or any other
computer-readable tangible storage device that can store a computer
program and digital information.
[0053] Devices used herein may also include a R/W drive or
interface 14 to read from and write to one or more portable
computer readable storage media 26. Application programs 11 on said
devices may be stored on one or more of the portable computer
readable storage media 26, read via the respective R/W drive or
interface 14 and loaded into the respective computer readable
storage media 08.
[0054] Devices used herein may also include a network adapter or
interface 16, such as a TCP/IP adapter card or wireless
communication adapter (such as a 4G wireless communication adapter
using OFDMA technology). Application programs 11 on said computing
devices may be downloaded to the computing device from an external
computer or external storage device via a network (for example, the
Internet, a local area network or other wide area network or
wireless network) and network adapter or interface 16. From the
network adapter or interface 16, the programs may be loaded onto
computer readable storage media 08. The network may comprise copper
wires, optical fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers.
[0055] Devices used herein may also include a display screen 20, a
keyboard or keypad 22, and a computer mouse or touchpad 24. Device
drivers 12 interface to display screen 20 for imaging, to keyboard
or keypad 22, to computer mouse or touchpad 24, and/or to display
screen 20 for pressure sensing of alphanumeric character entry and
user selections. The device drivers 12, R/W drive or interface 14
and network adapter or interface 16 may comprise hardware and
software (stored on computer readable storage media 08 and/or ROM
06).
[0056] The programs described herein are identified based upon the
application for which they are implemented in a specific one of the
exemplary embodiments. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the exemplary embodiments should not be
limited to use solely in any specific application identified and/or
implied by such nomenclature.
[0057] Based on the foregoing, a computer system, method, and
computer program product have been disclosed. However, numerous
modifications and substitutions can be made without deviating from
the scope of the exemplary embodiments. Therefore, the exemplary
embodiments have been disclosed by way of example and not
limitation.
[0058] It is to be understood 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, the exemplary embodiments are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
[0059] 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.
[0060] Characteristics are as follows:
[0061] 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.
[0062] 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).
[0063] 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).
[0064] 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.
[0065] 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.
[0066] Service Models are as follows:
[0067] 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.
[0068] 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.
[0069] 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).
[0070] Deployment Models are as follows:
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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).
[0075] 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 that includes a network of interconnected nodes.
[0076] Referring now to FIG. 4, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 40 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 40 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 40 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).
[0077] Referring now to FIG. 5, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 4) 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 the exemplary embodiments are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0078] Hardware and software layer 60 include 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.
[0079] 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.
[0080] 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 include 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.
[0081] 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
kube-edge configuration processing 96.
[0082] 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.
[0083] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0084] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0085] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, 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.
[0086] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0087] These computer readable program instructions may be provided
to a processor of a computer, or other programmable data processing
apparatus to produce a machine, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, create means for implementing the
functions/acts specified in the flowchart and/or block diagram
block or blocks. These computer readable program instructions may
also be stored in a computer readable storage medium that can
direct a computer, a programmable data processing apparatus, and/or
other devices to function in a particular manner, such that the
computer readable storage medium having instructions stored therein
comprises an article of manufacture including instructions which
implement aspects of the function/act specified in the flowchart
and/or block diagram block or blocks.
[0088] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0089] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be accomplished as one step, executed concurrently,
substantially concurrently, in a partially or wholly temporally
overlapping manner, 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.
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