Patent Application
20250240642
Embodiments presented in this disclosure generally relate to wireless communications. More specifically, embodiments disclosed herein relate to techniques for analyzing and optimizing a deployment of access points to reduce an occurrence of sticky clients within a wireless network.
Wireless networks have long been impacted by sticky clients, which is a term generally used to describe a client station (STA) that stays associated to an access point (AP) long after the client STA has reached the basic service set (BSS) of another AP offering better connection conditions. For example, the roaming process generally involves a client STA deciding to dissociate from one AP and deciding to reassociate to a new AP. However, because the roaming process is generally controlled by the client STA itself, this process can lead to scenarios in which some client STAs may not reassociate to a different AP offering better connection conditions.
The presence of sticky clients within a wireless network can impact the performance of the sticky clients themselves as well as the performance of other client STAs within the network. For example, a sticky client in poor connection conditions may communicate using a lower modulation and coding scheme (MCS). In addition, the lower MCS used by sticky client(s) can take a higher proportion of the airtime being shared by all client STAs within the wireless network connected to the same AP as the sticky client(s), impacting the network performance of all client STAs.
So that the manner in which the above-recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate typical embodiments and are therefore not to be considered limiting; other equally effective embodiments are contemplated.
To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one embodiment may be beneficially used in other embodiments without specific recitation.
One embodiment described herein is a computer-implemented method. The computer-implemented method includes obtaining a first layout of a set of access points (APs) to be deployed within an environment. The computer-implemented method also includes determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients. The computer-implemented method further includes generating a second layout of the set of APs to be deployed within the environment using the machine learning model. The computer-implemented method further includes transmitting information associated with the second layout.
Another embodiment described herein is a system. The system includes one or more memories collectively storing computer-executable instructions. The system also includes one or more processors communicatively coupled to the one or more memories. The one or more processors are collectively configured to execute the computer-executable instructions to cause the system to perform an operation. The operation includes obtaining a first layout of a set of access points (APs) to be deployed within an environment. The operation also includes determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients. The operation further includes generating a second layout of the set of APs to be deployed within the environment using the machine learning model. The operation further includes transmitting information associated with the second layout.
Another embodiment described herein is a non-transitory computer-readable medium. The non-transitory computer-readable medium includes computer-executable instructions, which when collectively executed by one or more processors of a computing system cause the computing system to perform an operation. The operation includes obtaining a first layout of a set of access points (APs) to be deployed within an environment. The operation also includes determining, based on evaluating the first layout with a machine learning model, that at least a first AP of the set of APs, will be associated with one or more sticky clients. The operation further includes generating a second layout of the set of APs to be deployed within the environment using the machine learning model. The operation further includes transmitting information associated with the second layout.
Other embodiments provide: an apparatus operable, configured, or otherwise adapted to perform any one or more of the aforementioned methods and/or those described elsewhere herein; a non-transitory, computer-readable media comprising instructions that, when executed by a processor of an apparatus, cause the apparatus to perform the aforementioned methods as well as those described elsewhere herein; a computer program product embodied on a computer-readable storage medium comprising code for performing the aforementioned methods as well as those described elsewhere herein; and/or an apparatus comprising means for performing the aforementioned methods as well as those described elsewhere herein.
In certain wireless networks (e.g., enterprise wireless local area networks (LANs) (WLANs)), seamless roaming by client STAs (e.g., smartphones, laptops, etc.) between APs plays a crucial role in ensuring the network and user application performance. Although several extensions to wireless communication standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 technical standard, have been released to improve roaming by aiding clients, one potential drawback to these efforts is the fact that the process of a client STA deciding to dissociate from one AP and reassociate to a new AP (with preferably a better radio frequency connection as indicated by a stronger received signal strength indicator (RSSI)) is generally controlled by the client STA itself.
In particular, in some cases, the roaming process may be sub-optimal and result in some client STAs staying associated to an AP with a lower RSSI as opposed to reassociating to a different AP with a higher RSSI. Such client STAs that behave in this manner are generally referred to as sticky clients. A sticky client connected to a given AP may experience diminished network performance (e.g., reduced throughput, increased latency, etc.). Additionally, the presence of the sticky client can impact the performance of other client STAs connected to the same AP as the sticky client. For example, a sticky client with a low RSSI may communicate using a lower MCS for successful packet delivery. Packets transmitted with a lower MCS, however, may take a higher proportion of the airtime, which is shared between all client STAs connected to the same AP. As such, other client STAs connected to the same AP as the sticky client(s) can also experience diminished network performance.
Certain embodiments described herein provide techniques for analyzing (or evaluating) and/or optimizing an AP layout (as well as radio resource configurations of the APs within the AP layout) using machine learning (ML) in order to reduce the occurrence of sticky clients within a wireless network. In certain scenarios, the physical topology of deployed APs may have a significant impact on the occurrence of sticky clients. For example, certain AP layouts and radio resource configurations, such as maximum transmit power, may create environments where some APs are prone to suffer from sticky clients (e.g., the likelihood of the AP(s) being associated with a sticky client is greater than a threshold).
According to certain embodiments herein, one or more ML models are trained to predict a likelihood of an AP in an AP layout being associated with a sticky client. The ML model(s) can be trained based on a dataset including one or more AP parameters (e.g., AP placement locations, inter-AP RSSIs, AP transmit powers, etc.) and one or more client STA parameters (e.g., client RSSI to associated AP, client RSSI(s) to neighboring AP(s), client STA type, etc.). Once trained, the ML models can be used to analyze and/or optimize different types of AP deployments to reduce the occurrence of sticky clients. The ML models can be configured to analyze and/or optimize an AP deployment with or without the use of client telemetry data.
In certain embodiments described herein, the trained ML model(s) can be used to analyze a proposed AP layout and associated radio resource configurations to determine whether at least one or more APs within the proposed AP layout have a high likelihood of being associated with one or more sticky clients. Additionally or alternatively, in certain embodiments described herein, the trained ML models can be used to monitor an existing deployment of APs within an environment and determine an adjustment to the deployment of APs and/or associated radio resource configurations to reduce the occurrence of sticky clients. Additionally or alternatively, in certain embodiments described herein, the trained ML models can be used to determine an optimal location for adding an AP within an existing deployment of APs, where the optimal location is associated with a target likelihood (e.g., minimum likelihood or below a threshold likelihood) of being associated with a sticky client.
In certain embodiments, the techniques described herein can be incorporated into network planning tools that are used to design and analyze AP layouts for a given environment. For example, the techniques described herein allow for designing an AP layout to minimize (or at least reduce) the occurrence of sticky clients as well as to meet other deployment metrics, such as a target coverage, expected RSSI, and client capacity, as illustrative, non-limiting examples.
Advantageously, the techniques described herein can improve the communication performance (e.g., increased throughput, lower latency, lower interference, etc.) of client STAs within a wireless network by reducing the occurrence of sticky clients within the wireless network.
Note, the techniques described herein for analyzing and/or optimizing an AP layout (as well as associated radio resource configurations) to reduce the occurrence of sticky clients may be incorporated into (such as implemented within or performed by) a variety of wired or wireless apparatuses (such as nodes). In some implementations, a node includes a wireless node. Such wireless nodes may provide, for example, connectivity to or for a network (such as a wide area network (WAN) such as the Internet or a cellular network) via a wired or wireless communication link. In some implementations, a wireless node may include a computing system or a controller.
An AP is generally a fixed station that communicates with client STA(s) and may be referred to as a base station, wireless device, or some other terminology. A client STA may be fixed or mobile and also may be referred to as a mobile STA, a client, a STA, a wireless device, or some other terminology. Note that while a certain number of APs and client STAs are depicted, the system 100 may include any number of APs and client STAs.
As used herein, an AP along with the STAs associated with the AP (e.g., within the coverage area (or cell) of the AP) may be referred to as a basic service set (BSS). Here, AP 102-1 is the serving AP for client STA 104-1, AP 102-2 is the serving AP for client STAs 104-2 and 104-3, and AP 102-3 is the serving AP for client STA 104-4. The AP 102-1, AP 102-2, and AP 102-3 are neighboring (peer) APs. The APs 102 may communicate with one or more client STAs 104 on the downlink and uplink. The downlink (e.g., forward link) is the communication link from the AP 102 to the client STA(s) 104, and the uplink (e.g., reverse link) is the communication link from the client STA(s) 104 to the AP 102. In some cases, a client STA may also communicate peer-to-peer with another client STA.
As shown in
In certain embodiments, the APs 102 may be controlled or managed at least partially by the controller 130. Here, the controller 130 couples to and provides coordination and control for the APs 1021-3. For example, the controller 130 may handle adjustments to RF power, channels, authentication, and security for the APs. The controller 130 may also coordinate the links formed by the client STA(s) 104 with the APs 102.
The operations of the controller 130 may be implemented by any device or system, and may be combined or distributed across any number of systems. For example, the controller 130 may be a WLAN controller for the deployment of APs 102 within the system 100. In some examples, the controller 130 is included within or integrated with an AP 102 and coordinates the links formed by that AP 102 (or otherwise provides control for that AP). For example, each AP 102 may include a controller that provides control for that AP. In some examples, the controller 130 is separate from the APs 102 and provides control for those APs. In
The database(s) 170 are representative of storage systems that may include historical and/or real-time AP telemetry data associated with one or more AP deployments, historical and/or real-time client STA telemetry data associated with one or more AP deployments, AP topological information (e.g., AP positions, positions/arrangements of interfering physical structures, such as walls) associated with one or more AP layouts, radio resource configurations (e.g., AP maximum transmit powers), radio resource management (RRM) information, logic (e.g., trained ML models) for analyzing and/or optimizing an AP layout, or a combination thereof.
In certain cases, one or more client STAs 104 depicted in
As noted, the presence of a sticky client can impact the network performance of all client STAs connected to the AP (e.g., AP 1) that is associated with the sticky client, due to the shared airtime between the client STAs and the use of lower MCS rates by the sticky client. In particular, as shown in the plot 300 of
Accordingly, certain embodiments described herein provide techniques for using machine learning to analyze a layout of APs (as well as their radio resource configurations) to determine a likelihood that a given AP within the AP layout will be associated with a sticky client. The techniques presented herein can analyze a proposed AP layout to be deployed in an environment without the use of client telemetry data. As such, the techniques described herein may enable network administrators to optimize an AP layout and/or radio resource configurations during the design stage and before deployment, or when planning to place additional APs. Additionally, the techniques described herein for analyzing and/or optimizing an AP layout (including associated radio resource configurations) can be incorporated into existing AP layout optimization techniques as part of a multi-objective optimization, such that service levels, capacity, and other deployment parameters can be considered alongside the propensity for sticky clients.
As shown, the computing system 140 includes a planning tool 180 and a training tool 190, each of which is configured to implement one or more techniques described herein. Although a single computing system 140 is depicted, note that the planning tool 180 and training tool 190 may be implemented using any number of computing systems. For example, one or more operations of the planning tool 180 and/or the training tool 190 may be performed by one or more computing systems distributed across a cloud computing environment. The planning tool 180 and the training tool 190 may include hardware, software, or combinations thereof.
In certain embodiments, the training tool 190 may train one or more ML models to predict a likelihood of an AP being associated with a sticky client. The training tool 190 may store the trained ML models in one or more database(s) 170. The planning tool 180 may obtain the trained ML model(s) from the database(s) 170 and use the trained ML models to analyze and/or optimize an AP layout as well as the associated radio resource configurations to reduce an occurrence of sticky clients.
An example AP layout 400 is depicted in
In certain cases, the physical topology of the AP layout 400 and/or the radio resource configurations of the APs may have an impact on the occurrence of sticky clients within a wireless network configured with the AP layout 400. In
Method 500 may enter at block 505, where the training tool generates a dataset including one or more parameters associated with one or more APs within one or more AP deployments. For example, the dataset may include a respective set of information for each AP, where each set of information includes an AP identifier, the (maximum) transmit power of the AP, AP location, average number of associated client STAs over a time period, and one or more RSSIs to one or more neighboring APs. The one or more RSSIs may be measured or estimated from inter-AP distances and environmental obstructions. Note, the dataset may include information from one or more different time windows (e.g., 5 minutes, 1 hour, 1 day, etc.).
At block 510, the training tool labels the dataset with an indication, for each AP, of whether the AP is associated with sticky clients. In certain embodiments, the training tool may use a set of client STA-related parameters to label each AP with an indication of whether the AP is associated with sticky clients. The client STA-related parameters may include, for each client STA, the RSSI of the client STA to its associated AP, the respective RSSI of the client STA to each neighboring AP, or a combination thereof.
Note, in certain embodiments, the training tool may obtain the AP-related parameters and/or the client STA-related parameters from a computing system(s) located in a cloud computing environment and/or a database (e.g., database 170) associated with the computing system(s). Such computing system(s) may be configured to perform network management tasks, such as security, predictive monitoring, and automation, as illustrative, non-limiting examples. As part of the network management tasks, the computing system(s) may periodically collect and store AP telemetry data and/or client STA telemetry data in one or more databases (e.g., database(s) 170). A reference example of such a computing system is Cisco Digital Network Architecture (DNA)®.
In certain embodiments, as part of the operations in block 510, the training tool may evaluate the client STAs associated with each AP and label each client STA as a sticky client when the client STA (i) is currently associated to an AP with an RSSI lower than a threshold (e.g., −65 dBm), (ii) is associated with the AP with an RSSI lower than one of the client's neighboring APs, by a threshold margin (e.g., 5 dBm), and (iii) the sub-optimal AP association has persisted for more than a threshold amount of time (e.g., 10 minutes). The training tool (at block 510) may label each client STA as a non-sticky client when at least one of aforementioned conditions is not satisfied.
After labeling each client STA associated with an AP as a sticky client or non-sticky client, the training tool (at block 510) may compute the ratio of clients labeled sticky to clients labeled non-sticky for that AP. If the training tool (at block 510) determines that the ratio is greater than a threshold, then the training tool may label the AP as being prone to sticky clients. On the other hand, if the training tool (at block 510) determines that the ratio is not greater than the threshold, then the training tool may label the AP as not being prone to sticky clients. Note that the thresholds described herein may be adjusted for different AP layouts based on empirical data.
At block 515, the training tool trains an ML model with the labeled dataset to predict a likelihood that an AP will be associated with a sticky client. At block 520, the training tool stores the trained ML model (e.g., in a database 170).
The training tool may use any suitable supervised artificial intelligence (AI)/ML technique to train the ML model. Such techniques may include gradient boosting trees, logistic regression, support vector machines, random forest algorithms, k-nearest neighbors algorithm (k-NN), deep-learning algorithms, and adaptive boosting, as illustrative, non-limiting examples. In a particular embodiment, the training tool uses a deep-learning graph neural network (GNN) to train a ML model to classify APs as being prone to suffering from sticky clients. One potential advantage of a GNN is that a GNN can leverage the inherent graph nature of neighboring APs in an AP layout, such as the AP layout 400.
Note that the method 500 can be used to train different ML models for different scenarios or environments. For example, the training tool may incorporate information on client types as part of the dataset that is used to train an ML model. For instance, in some cases, the tendency of a client STA to act as a sticky client may be related to the client STA's hardware and/or software. This is because the process of dissociating from one AP and associating to a new AP while roaming is generally controlled by the client STA. As such, the probability of a given AP being associated with sticky clients may depend upon the expected distribution of different client types.
Accordingly, in certain embodiments, the training tool may generate different ML models based on different sets of training data, where each training dataset includes data from certain distributions of clients. For example, the training tool may generate a first model with a certain ratio of smartphones of various manufacturers, generate a second model with another ratio of smartphones of various manufacturers, generate a third model with data from industrial sensors, and so on. In general, a network administrator (via the planning tool 180) may select one of multiple available ML models to use for analyzing and/or optimizing an AP layout, based on the expected types of clients for the AP layout.
In certain embodiments, the training tool may generate ML models customized for certain client roaming patterns. Such ML models may be used in scenarios where an additional AP is to be deployed in an existing network. For example, a network administrator (via the planning tool 180) may select a particular ML model that is customized for the specific roaming patterns observed within the existing network. The roaming patterns may be determined based on client telemetry data collected from a computing system configured to perform one or more network management tasks. The client telemetry data may be used to fine tune a customized ML model using transfer learning.
The sticky client predictor 610 is generally configured to analyze an AP layout to predict a likelihood of one or more APs within the AP layout being associated with a sticky client. As shown, the sticky client predictor 610 may obtain parameters 602 associated with an AP layout from a deployment database 660, and may obtain a trained ML model 604 from the model database 670. Each of the databases 660 and 670 is a reference example of the database 170 depicted in
At least some of the parameters 602 stored within the database 660 may be derived from network telemetry (e.g., inter-AP RSSIs and other AP neighbor information) collected from a computing system configured to perform network management. Additionally or alternatively, at least some of the parameters 602 stored within the database 660 may be derived from geometric information of the APs and their environment. Such geometric information may include physical positions of APs, positions and arrangements of potential interfering physical structures (e.g., walls), etc. In some cases, the geometric information may be in the form of two-dimensional (2D) and/or three-dimensional (3D) maps of the environment.
In certain embodiments, the parameters 602 may include AP parameters, such as one or more inter-AP RSSIs, number of APs, AP positions, and maximum AP transmit powers, as illustrative reference examples. In other embodiments, the parameters 602 may include AP parameters (e.g., inter-AP RSSIs, AP positions, number of APs, maximum AP transmit powers, etc.) and client STA parameters (e.g., number of client STAs, client RSSI to associated AP, client RSSI to neighboring AP, client type, etc.) associated with an existing deployment of APs.
The sticky client predictor 610 may select one of multiple ML models within the model database 670 as the ML model 604, based on a type of environment and/or expected types of clients for the AP layout. The sticky client predictor 610 may output a prediction 606, based on analyzing the parameters 602 with the ML model 604. The prediction 606 may include, for each AP, an indication of the likelihood that the AP will be associated with a sticky client (e.g., once the AP is deployed based on the AP layout).
The deployment evaluator 620 is generally configured to analyze the AP layout using the prediction 606 and parameters 602 to determine whether the AP layout meets a set of target deployment metrics, including target service level, target coverage, target client signal to noise ratio (SNR), and target (e.g., minimum) sticky client probabilities, as illustrative, non-limiting examples. The deployment evaluator 620 may use any suitable network planning algorithm and/or AI/ML model to evaluate the AP layout based on the prediction 606 and parameters 602. If the deployment evaluator 620 determines that the AP layout does not satisfy the set of target deployment metrics, then the deployment evaluator 620 may trigger the optimization tool 640 to optimize the AP layout and/or associated radio resource configuration. For example, the optimization tool 640 is generally configured to determine an adjustment 608 to the AP layout and/or radio resource configuration. In certain embodiments, the optimization tool 640 may use the ML model 604 (or another ML model obtained from the model database 670) to determine the adjustment 608. The adjustment 608 may include at least one of an adjustment to a number of APs in the AP layout, an adjustment to at least one AP position in the AP layout, or an adjustment to a maximum transmit power of at least one AP in the AP layout.
The sticky client predictor 610 may adjust one or more parameters 602 based on the adjustment 608, and (re)-compute a prediction 606 for the adjusted AP layout. Once the deployment evaluator 620 determines that the AP layout satisfies the set of target deployment metrics, then the deployment evaluator 620 may output an indication of the optimal AP layout 650. The optimal AP layout 650 may include a respective set of AP parameters (e.g., number of APs, AP locations, and maximum AP transmit powers), that are predicted to achieve the set of target deployment metrics.
Method 700 may enter at block 705, where the planning tool obtains a first layout of a set of APs to be deployed within an environment.
At block 710, the planning tool determines at least one AP within the first layout that is associated with one or more sticky clients, based on evaluating the first layout with a trained ML model (e.g., ML model 604). For example, the planning tool may determine that the likelihood of the at least one AP being associated with one or more sticky clients exceeds a threshold.
At block 715, the planning tool generates and provides information associated with the at least one AP, e.g., to a computing system associated with a network administrator. In certain embodiments, the computing system associated with the network administrator is the computing system 140 depicted in
Method 800 may enter at block 805, where the planning tool monitors an existing set of APs deployed within an environment according to a first layout. At block 810, the planning tool generates, based on the monitoring, a set of information including (i) one or more AP parameters and (ii) one or more client STA parameters.
At block 815, the planning tool generates a second layout of the set of APs within the environment, based on evaluating the set of information with a trained ML model. The second layout may include at least one adjustment to the set of APs deployed within the environment. For example, the adjustment can include an additional AP, removal of an AP, a different AP location, a different maximum transmit power of an AP, or a combination thereof. At block 820, the planning tool provides information associated with the second layout e.g., to a computing system associated with a network administrator (e.g., computing system 140). The second layout may have a lower likelihood of being associated with one or more sticky clients than the first layout.
Method 900 may enter at block 905, where the planning tool obtains information associated with a set of APs deployed within an environment according to a first layout. At block 910, the planning tool receives a query to adjust the first layout of the set of APs deployed within the environment. The query may include adjustment information associated with the first layout. The adjustment information may include an additional AP, a removal of an AP, a change in an AP location, or a combination thereof.
At block 915, in response to the query, the planning tool generates a second layout of the set of APs, based on evaluating the first layout and the adjustment information with a trained ML model. At block 920, the planning tool provides information associated with the second layout, e.g., to a computing system associated with a network administrator. The second layout may have a lower likelihood of being associated with one or more sticky clients than the first layout.
The processor 1010 may be any processing element capable of performing the functions described herein. The processor 1010 represents a single processor, multiple processors, a processor with multiple cores, and combinations thereof. The communication interfaces 1030 (e.g., radios) facilitate communications between the computing device 1000 and other devices. The communications interfaces 1030 are representative of wireless communications antennas and various wired communication ports.
The memory 1020 may be either volatile or non-volatile memory and may include RAM, flash, cache, disk drives, and other computer readable memory storage devices. Although shown as a single entity, the memory 1020 may be divided into different memory storage elements such as RAM and one or more hard disk drives. As shown, the memory 1020 includes various instructions that are executable by the processor 1010 to provide an operating system 1022 to manage various functions of the computing device 1000. The memory 1020 also includes the planning tool 180, the training tool 190, and one or more application(s) 1026.
As used herein, “a processor,” “at least one processor,” or “one or more processors” generally refers to a single processor configured to perform one or multiple operations or multiple processors configured to collectively perform one or more operations. In the case of multiple processors, performance of the one or more operations could be divided amongst different processors, though one processor may perform multiple operations, and multiple processors could collectively perform a single operation. Similarly, “a memory,” “at least one memory,” or “one or more memories” generally refers to a single memory configured to store data and/or instructions or multiple memories configured to collectively store data and/or instructions.
In the current disclosure, reference is made to various embodiments. However, the scope of the present disclosure is not limited to specific described embodiments. Instead, any combination of the described features and elements, whether related to different embodiments or not, is contemplated to implement and practice contemplated embodiments. Additionally, when elements of the embodiments are described in the form of “at least one of A and B,” or “at least one of A or B,” it will be understood that embodiments including element A exclusively, including element B exclusively, and including element A and B are each contemplated. Furthermore, although some embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the present disclosure. Thus, the aspects, features, embodiments and advantages disclosed herein are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s).
As will be appreciated by one skilled in the art, the embodiments disclosed herein may be embodied as a system, method or computer program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments presented in this 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 program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block(s) of the flowchart illustrations and/or block diagrams.
These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other device to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the block(s) of the flowchart illustrations and/or block diagrams.
The computer 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 data processing apparatus, or other device provide processes for implementing the functions/acts specified in the block(s) of the flowchart illustrations and/or block diagrams.
The flowchart illustrations and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart illustrations or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In view of the foregoing, the scope of the present disclosure is determined by the claims that follow.
