The present disclosure relates generally to a cluster technique, and more specifically, to node management for a cluster of computing nodes.
A cluster is a set of computing nodes that work together so that they can be viewed as a single system, which allows for collaborative work on computationally intensive tasks instead of having to complete the tasks on a single computing node. By way of example, high performance computing (HPC) clusters are widely deployed to provide faster computing speed, higher scheduling efficiency, greater stability and reliability in order to solve complex problems and process vast amount of data in the fields of science, engineering, or business.
One of the challenges in the use of a cluster is the efficient management for the computing nodes in the cluster. For example, an administrator of a cluster may want to incorporate a large number of computing nodes in a cluster, such as hundreds or thousands of computing nodes; however, how to manage such a large number of computing nodes in an efficient way is challenging to the administrator.
According to one embodiment of the present disclosure, there is provided a computer-implemented method for node management. In this method, a plurality of computing nodes in a cluster can be grouped into a hierarchy of groups according to a hierarchy of grouping policies. One of computing nodes in each group of the hierarchy of groups can be determined as a leader node of the corresponding group. A leader node of a first group is responsible for collecting and reporting status of all computing nodes in the first group to a leader node of a second group superior to the first group by one level in the hierarchy of groups.
According to another embodiment of the present disclosure, there is provided a system for node management. The system comprises one or more processors, a memory coupled to at least one of the processors and a set of computer program instructions stored in the memory. When executed by at least one of the processors, the set of computer program instructions perform following actions. A plurality of computing nodes in a cluster can be grouped into a hierarchy of groups according to a hierarchy of grouping policies. One of computing nodes in each group of the hierarchy of groups can be determined as a leader node of the corresponding group. A leader node of a first group is responsible for collecting and reporting status of all computing nodes in the first group to a leader node of a second group superior to the first group by one level in the hierarchy of groups.
According to a yet another embodiment of the present disclosure, there is provided a computer program product for node management. The computer program product comprises a non-transitory computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform following actions. A plurality of computing nodes in a cluster can be grouped into a hierarchy of groups according to a hierarchy of grouping policies. One of computing nodes in each group of the hierarchy of groups can be determined as a leader node of the corresponding group. A leader node of a first group is responsible for collecting and reporting status of all computing nodes in the first group to a leader node of a second group superior to the first group by one level in the hierarchy of groups.
Through the more detailed description of some embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, wherein the same reference generally refers to the same components in the embodiments of the present disclosure.
Some embodiments will be described in more detail with reference to the accompanying drawings, in which the embodiments of the present disclosure have been illustrated. However, the present disclosure can be implemented in various manners, and thus should not be construed to be limited to the embodiments disclosed herein.
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, embodiments of the present disclosure are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
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.
Characteristics are as follows:
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.
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).
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).
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.
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.
Service Models are as follows:
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.
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.
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).
Deployment Models are as follows:
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.
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.
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.
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).
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.
Referring now to
In cloud computing node 10 there is a computer system/server 12 or a portable electronic device such as a communication device, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 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, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.
System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the disclosure as described herein.
Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.
Referring now to
Referring now to
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.
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.
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.
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 node management 96.
As mentioned in the above, in order to increase the speed of performing computationally intensive tasks and improve the efficiency of running workloads in a cluster of computing nodes, it is desirable to incorporate a large number of computing nodes in the cluster. A conventional cluster is usually preconfigured manually by an administrator, which is low efficient and costly. Moreover, it is difficult to add more computing nodes or re-group computing nodes to meet workload requirements or fully utilize node resources. Especially, as the number of the computing nodes in the cluster increases, it becomes difficult for the administrator to manage the computing nodes in an efficient way. The administrator should be familiar with the attributes of hardware and/or software of all the computing nodes in the cluster, and have a full understanding of both the current status and future development of the cluster, so as to build a suitable cluster based on his/her experience.
In addition, a management node in a cluster needs to collect status of all the computing nodes continuously for recording the up-to-date status of the entire cluster. In a conventional cluster, collecting the status of the computing nodes results in large overhead of network traffic since every computing node needs to report its status directly to the management node.
As shown in
In view of the above, there exists a need for an improved node management approach to manage the computing nodes in an efficient way.
Embodiments of the present disclosure aim to solve at least one of the technical problems described above, and propose a method, system and computer program product for node management based on a hierarchical architecture instead of the flat structure. In the node management according to embodiments of the present disclosure, the cluster of computing nodes can be built automatically based on a hierarchy of grouping policies, and accordingly, the nodes can be grouped into a hierarchy of groups automatically. In addition, a leader node can be determined for each of the groups in the hierarchy of groups, and the leader node can be responsible for collecting and reporting status of all computing nodes in its group to the leader node of a group superior to this group by one level in the hierarchy of groups. In other words, a computing node may not report its status directly to the management node, but can report it to its leader node (referred to as a first leader). The first leader can report the status received from its member nodes as well as the status of the first leader itself to its leader node (referred to a second leader), and the second leader can report the status received from its member nodes as well as the status of the second leader itself to its superior node until the status reaches the management node. In such a way, the management node does not need to receive the status information directly from all the computing nodes, which reduces the bandwidth and burden of the management node. In addition, the leader node may also be responsible for receiving workloads dispatched from the management node and then allocating the workloads to its member nodes for execution, which may also reduce the bandwidth and burden of the management node. Further, the hierarchical architecture can be updated automatically with new nodes added or other changes, which can effectively reduce management difficulties for the cluster administrator.
As shown in
As shown in
The above illustrative architecture of the cluster according to the embodiments of the present disclosure is described with respect to a hierarchy of groups with four levels. Obviously, the number of levels of groups in the hierarchy, the number of computing nodes in each group, and the position of each group relative to the hierarchy of groups are just examples of the embodiments of the present disclosure, and do not limit the embodiments of the present disclosure to the above specific form of the above examples. Instead, more or less levels in the hierarchy of groups, different number of nodes contained in each group and different arrangements of the nodes relative to the hierarchy are possible.
Based on the grouping of the computing nodes into a hierarchy of groups, the cluster of computing nodes can be managed based on the hierarchy in an efficient way. In order to reduce the overhead related to the status reporting and/or workload dispatching and/or to improve the efficiency for scheduling the computing nodes for executing the dispatched workloads, one of computing nodes in each group of the hierarchy of groups can be determined as a leader node of the corresponding group. The leader node of a group (referred as a first group) can be responsible for collecting and reporting status of all computing nodes in the first group to the leader node of a group (referred as a second group) superior to the first group by one level in the hierarchy of groups. In such a way, a computing node may not report its status directly to the management node, but report it to its leader nodes. Then, the leader nodes report the status to the management node level by level. There can be various ways to determine the leader node for each of the groups, for example, the determination may be based on the workload status and/or the working performance of the computing nodes in a corresponding group. For example, the computing node with the lightest workload or the best working performance in the corresponding group may be selected as the leader node of the corresponding group.
Still referring to
For example, the leader Node L can collect the status of all computing nodes in Group G1 and report them to the leader Node K of Group G2 which is superior to Group G1 by one level in the hierarchy of groups. Then, the leader Node K can collect and report the status of all computing nodes in Group G2 to the leader Node D of Group G3 which is superior to the Group G2 by one level in the hierarchy, and so on. Please understand that the leader Node K can collect the status of the computing nodes in Group G1 by receiving them from the leader Node L. Accordingly, the status of all the computing nodes in the cluster can be transmitted to the cluster reconciler through the leader nodes such as Nodes A and B of the groups at the top level. The leader nodes of the groups at the top level can directly transmit the collected status of the computing nodes to the cluster reconciler. The status can be finally stored in the database. In this manner, the burden for collecting the status of all the nodes contained in the cluster can be offloaded to the leader nodes of the groups in the hierarchy, which can reduce the overhead for network traffic and the work burden of the management node.
In another embodiment of the node management for the cluster, the workloads can be dispatched on a per-group basis. After receiving a job from a user, the management node can firstly determine one or more groups of computing nodes suitable for the job on a per-group basis. For example, the management node can determine a proper group of computing nodes (e.g., the first group) from the hierarchy of groups for executing a job received from a user, and the management node can accordingly dispatch workloads to the leader node of the first group for execution by one or more computing nodes in the first group, instead of having to directly dispatch the workloads to every one of the selected nodes to execute. In this manner, the efficiency for dispatching the workloads to the cluster can be improved. For example, as shown in
As shown in
Then, at the next level, the grouping policy requires that nodes with the same CPU platform are grouped together. For example, CPU platforms may include x86, ARM, and the like. Since the grouping based on the CPU platform is at a level lower than the level for grouping based on the location, the grouping based on the CPU platform is performed within each group with computing nodes at the same location. For example, for the group of computing nodes at the first position, it will be sub-divided to a group of computing nodes with the x86 platform and a group of computing nodes with the ARM platform. The same principle applies for the group of computing nodes at the second position.
Similarly, the grouping policies at other levels may require that nodes with the same OS type, with the same CU, or with the same network traffic are grouped together. For example, OS types may include Windows, Linux and the like. The compute unit may refer to a physical rack where the computing nodes are located. The network traffic may refer to the speed of the network card used in the computing nodes. In addition to the grouping policies related to the attributes of the computing nodes listed above, the grouping policy can be based on any customized attribute. The present disclosure does not restrict the specific attributes of the computing nodes for the hierarchy of the grouping policies.
The above examples for the grouping polices are directed to the cases where there is only one grouping policy at each level of the hierarchy of the grouping policies, but embodiments of the present disclosure are not limited thereto. There may be more than one grouping policies at a level of the hierarchy. For example, as shown in
The above illustrative architecture as shown in
Referring back to
According to the illustrative example of
As shown in
In order to determine the groups to which each computing node belongs, for example, attributes of each computing node can be compared with the attributes involved in the hierarchy of grouping policies such that all computing nodes in the same node group are with the same attributes.
In an embodiment, workloads can be dispatched to one or more groups based on a criterion corresponding to a grouping policy in the hierarchy of grouping policies. In other words, the workloads can be dispatched based on groups, and the selection of groups can be related to the grouping policies for dividing the groups. For example, the criteria can be that the workloads should be executed at a particular geographic position, using a particular CPU platform, and/or using a particular OS type. The criteria may be input by a user into the cluster reconciler, or be determined by the cluster reconciler based on for example the nature of the workloads to be executed. As shown in
It is noted that the dispatching can be at any level of the hierarchy, for example, a workload may also be dispatched to Group G4 at the top level or Group G3 at the second level. In addition, a workload can also be dispatched to two or more groups, for example, Group G3′ and Group G4′. Further, the criteria for determining the one or more suitable groups can be obtained in various ways. For example, a job request from the user may also indicate execution requirements for the job, and the execution requirements can be used as the criteria for determining the suitable groups.
In an embodiment, the hierarchy of groups may be updated in response to changing levels of at least two grouping policies in the hierarchy of grouping policies. As shown in
In another embodiment, the hierarchy of groups may be updated in response to replacing a grouping policy at a level in the hierarchy of grouping policies with a different grouping policy. For example, the replacement of a grouping policy at a particular level with another grouping policy may occur in the case where there are more than one grouping policy at the particular level of the hierarchy. Also as shown in
According to the above embodiments, the present disclosure provides a dynamic hierarchical grouping mechanism in a cluster. When the structure of a cluster needs to be changed due to for example dynamic requirements of the user or updates of the computing nodes in the cluster, the hierarchy of groups can be updated automatically by updating the hierarchy of grouping policies without re-configuring the cluster manually by the administrator. It enables dynamic and flexible node management for a cluster.
As shown in
Further, it should be noted that there can be various ways to perform the analysis of the historical data. For example, an artificial-intelligence or machine learning component can be employed in the cluster reconciler to analyze the historical workload running performance in order to decide movement of one or more computing nodes from one group to another group.
With reference to
At step 1102, one of computing nodes in each group of the hierarchy of groups can be determined as a leader node of the corresponding group. For example, determining one of computing nodes in each group of the hierarchy of groups as a leader node of the corresponding group can be based on the workload status and/or the working performance of the computing nodes in the corresponding group. Further, a leader node of a first group can be responsible for collecting and reporting status of all computing nodes in the first group to a leader node of a second group superior to the first group by one level in the hierarchy of groups. The first group can be any group in the hierarchy of groups. If the first group is at the top level, it reports the status of all computing nodes in the first group to the management node.
Optionally, the method 1100 can also comprise a step of dispatching a workload to one or more groups based on a criterion corresponding to a grouping policy in the hierarchy of grouping policies. For example, the workload can be dispatched to the leader node of the first group for execution by one or more computing nodes in the first group. Detailed description of dispatching workload to one or more groups of the cluster can refer to the content described with respect to
Optionally, the method 1100 can also comprise a step of updating the hierarchy of groups in response to updating of the hierarchy of grouping policies. As an example, the updating of the hierarchy of grouping policies comprises changing levels of at least two grouping policies in the hierarchy of grouping policies. Additionally or alternatively, the updating of the hierarchy of grouping policies comprises replacing a grouping policy at a level in the hierarchy of grouping policies with a different grouping policy. Detailed description of updating of the hierarchy of grouping policies can refer to the content described with respect to
Optionally, the method 1100 can also comprise a step of updating the hierarchy of groups according to historical workload running performance of the cluster. Detailed description can refer to the content described with respect to
It should be noted that the processing of the method of node management as described hereinbefore according to embodiments of this disclosure can be implemented by a system such as computer system/server 12 of
In an embodiment, each computing node newly added to the cluster can be grouped into the hierarchy of groups according to the hierarchy of grouping policies.
In an embodiment, a workload can be dispatched to one or more groups based on a criterion corresponding to a grouping policy in the hierarchy of grouping policies.
In an embodiment, a workload can be dispatched to the leader node of the first group for execution by one or more computing nodes in the first group.
In an embodiment, the hierarchy of groups can be updated in response to updating of the hierarchy of grouping policies. In one example for updating of the hierarchy of grouping policies, levels of at least two grouping policies in the hierarchy of grouping policies can be changed. In another example for updating of the hierarchy of grouping policies, a grouping policy at a level in the hierarchy of grouping policies can be replaced with a different grouping policy.
In an embodiment, the hierarchy of groups can be updated according to historical workload running performance of the cluster.
In an embodiment, the grouping policy of each level in the hierarchy of grouping policies can be based on at least one selected from a group comprising a physical location, a central processing unit (CPU) platform, an operating system (OS) type, a compute unit (CU), a network traffic, a core size, a memory size, and a customized attribute.
In an embodiment, the determining one of computing nodes in each group of the hierarchy of groups as a leader node of the corresponding group can be based on the workload status and/or the working performance of the computing nodes in the corresponding group.
The descriptions above related to the process of method 1100 can also be applied to the system of node management, and details are omitted herein for conciseness.
In addition, according to another embodiment of the present disclosure, a computer program product for feature processing is disclosed. As an example, the computer program product comprises a non-transitory computer readable storage medium having program instructions embodied therewith, and the program instructions are executable by a processor. When executed, the program instructions cause the processor to perform one or more of the above described procedures, and details are omitted herein for conciseness.
The present disclosure 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 disclosure.
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.
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.
Computer readable program instructions for carrying out operations of the present disclosure 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 disclosure.
Aspects of the present disclosure 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 disclosure. 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.
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.
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.
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 disclosure. 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.
The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.