Patent Application
20250200394
Call models are typically used by network operators to model network usage, which in turn is used to allocate network resources. Network operators typically estimate a call model by obtaining current and previous field data and manually estimating the model and usage. However, such call models are typically generic and not site specific. The modeling of network usage is complex, and the use of a simplified model for different markets can lead to inaccuracies and thus inefficient allocation of resources.
It is with respect to these considerations and others that the disclosure made herein is presented.
Methods and systems are disclosed for implementing a dynamic call model predictor that incorporates machine learning models to efficiently generate and predict call/traffic models with higher accuracy. The predicted call/traffic models are dynamically and continuously updated. By dynamically/continuously updating the call models, resource allocation can be more accurately predicted and at a finer level of granularity.
In an embodiment a site or location is provided as input to a dynamic call model predictor. The site or location includes the location where a user plane/control plane is to be deployed. The site or location is identifiable with demographic information such as population, age, race, housing, family arrangements, internet and computer usage, education, health, economy, and income. For each of a plurality of target attributes, the dynamic call model predictor computes a corresponding dynamic call model for the target site. In an embodiment, the dynamic call model predictor is a cluster of different machine learning models that are used to generate dynamic call model characteristics. For example, for a target attribute value comprising throughput, a first dynamic call model predictor is generated to compute the throughput per user for each site. The process is repeated for n target attributes and n call models are output. The outputs from the dynamic call model predictor are merged into a complete dynamic call model with n characteristics for the input site.
This Summary is not intended to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure.
The Detailed Description is described with reference to the accompanying FIGS. In the FIGS., the left-most digit(s) of a reference number identifies the FIG. in which the reference number first appears. The same reference numbers in different FIGS. indicate similar or identical items.
Call models are typically used by network operators to model network usage, which in turn is used to allocate network resources in a communications network such as a 5G network. As used herein, a call model is a representation of user behavior at any given location and time that demonstrates or represents the current network traffic based on usage patterns for CPU, memory, and other resources.
Network operators typically estimate a call model by obtaining current and previous field data and manually estimating the model and usage. However, such call models are typically generic and not site specific. The modeling of network usage is complex, and the use of a simplified model for different markets can lead to inaccuracies and thus inefficient allocation of resources.
The present disclosure describes methods and systems for implementing a dynamic call model predictor that incorporates machine learning models to efficiently generate and predict call/traffic models with higher accuracy. The predicted call/traffic models are dynamically and continuously updated. By dynamically/continuously updating the call models, resource allocation can be more accurately predicted and predicted at a finer level of granularity.
In an embodiment, a site or location is provided as input to a dynamic call model predictor. The site or location includes the location where a user plane/control plane is to be deployed. The site or location is identifiable with location-specific information, including demographic information such as population, age, race, housing, family arrangements, internet and computer usage, education, health, economy, and income. For each of a plurality of target attributes, the dynamic call model predictor computes a corresponding dynamic call model for the target site. In an embodiment, the dynamic call model predictor comprises a cluster of different machine learning models that are used to generate dynamic call model characteristics. For example, for a target attribute value comprising throughput, a first dynamic call model predictor is generated to compute the throughput per user for each site. The process is repeated for n target attributes and n call models are output. The outputs from the dynamic call model predictor are merged into a complete dynamic call model with n characteristics for the input site.
Referring to the appended drawings, in which like numerals represent like elements throughout the several FIGURES, aspects of various technologies for video super resolution using motion vectors will be described. In the following detailed description, references are made to the accompanying drawings that form a part hereof, and which are shown by way of illustration specific configurations or examples.
With reference to
The site data is input to a dynamic call model predictor 130. The dynamic call model predictor 130 comprises a plurality of different machine learning models 110 that are each configured to generate a dynamic call model characteristic 120 associated with one of the plurality of target attributes 140. For each of the plurality of target attributes 140, the dynamic call model predictor 130 can be used to compute a corresponding dynamic call model 150 for the location. The dynamic call model predictor 130 comprises a cluster of different machine learning models 110 that are configured to generate dynamic call model characteristics 120.
The dynamic call model characteristics 120 generated by the plurality of different machine learning models 110 of the dynamic call model predictor 130 are merged 135 into dynamic call model 150. The dynamic call mode characteristics 120 are merged to correspond to a representation of computing and network resources of the telecommunications network. Merging the outputs can be performed so as to enable the allocation of resources in a network based on the dynamic call model characteristics. Merging can include averaging, adding, dimensional transformation, mapping, or other techniques in order to determine a specific allocation of resources in the network to achieve the target attributes.
With reference to
For each of a plurality of target attributes, the dynamic call model predictor 204 computes a corresponding dynamic call model for the target site. In an embodiment, the dynamic call model predictor 204 is a cluster of different machine learning models that are used to generate dynamic call model characteristics. For example, for a target attribute value (1) comprising throughput, dynamic call model predictor 1 206 is generated to compute the throughput per user for each site. The process is repeated for n target attributes and n call models are output.
The outputs from the dynamic call model predictor 204 are merged into a complete dynamic call model with n characteristics 208 for the input site.
With reference to
In an embodiment, dimensionality reduction of the dataset is performed using the principal component analysis (PCA) technique that linearly transforms the data into a new coordinate system that can be described with fewer dimensions than the initial data. An optimal number of clusters can be obtained using the Elbow method, and a K-Means algorithm is applied to cluster the data. Merging the outputs can be performed so as to enable the allocation of resources in a network based on the dynamic call model characteristics.
In some embodiments, with reference to
For a target attribute value 1, dynamic call model characteristic 1 212 is generated that computes the target attribute value 1 for each site. The process is repeated for n target attributes and n dynamic call models are output. The feature vectors are split into training data used to train the regression model and the test data is used to evaluate the final model.
An optimal machine learning model can be determined by tuning hyperparameters. Additionally, ensemble models can be used to improve ML results and predictive performance by combining multiple models.
The output of call model characteristic generator trainer 210 is the final optimized ML model that will be used to predict call models. This optimized ML model is provided to the call model generator 204 of
Referring to
For each call model characteristic an ML model is generated for, example for 24 hours of the day. This process can be repeated 24 times to generate call model characteristic per hour of the day for a full day. Based on the Call Model Characteristic Hour 1 through the Call Model Characteristic Hour 24, a single Dynamic Call Model Characteristic 1 (246) is generated. This process is repeated n times to create n Dynamic Call Model Characteristics. Dimensionality reduction can be used to merge outputs in order to determine a specific allocation of resources in the network to achieve the target attributes. This enables the Dynamic Call Model Characteristics to be translated into a specific set of configurable network resources such as processing capacity, storage capacity, bandwidth allocations, and so forth.
In various embodiments, the machine learning model(s) may be run locally on the client. In other embodiments, the machine learning inferencing can be performed on a server of a network. For example, in the system illustrated in
Turning now to
It should be understood by those of ordinary skill in the art that the operations of the methods disclosed herein are not necessarily presented in any particular order and that performance of some or all of the operations in an alternative order(s) is possible and is contemplated. The operations have been presented in the demonstrated order for ease of description and illustration. Operations may be added, omitted, performed together, and/or performed simultaneously, without departing from the scope of the appended claims.
It should also be understood that the illustrated methods can end at any time and need not be performed in their entireties. Some or all operations of the methods, and/or substantially equivalent operations, can be performed by execution of computer-readable instructions included on a computer-storage media, as defined herein. The term “computer-readable instructions,” and variants thereof, as used in the description and claims, is used expansively herein to include routines, applications, application modules, program modules, programs, components, data structures, algorithms, and the like. Computer-readable instructions can be implemented on various system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, personal computers, hand-held computing devices, microprocessor-based, programmable consumer electronics, combinations thereof, and the like. Although the example routine described below is operating on a computing device, it can be appreciated that this routine can be performed on any computing system which may include a number of computers working in concert to perform the operations disclosed herein.
Thus, it should be appreciated that the logical operations described herein are implemented (1) as a sequence of computer implemented acts or program modules running on a computing system such as those described herein and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof.
Referring to
Operation 503 illustrates determining a plurality of target attributes indicative of capabilities of the telecommunications network.
Operation 505 illustrates inputting the site data to a dynamic call model predictor. In an embodiment, the dynamic call model predictor comprises a plurality of different machine learning models that are each configured to generate a dynamic call model characteristic associated with one of the plurality of target attributes.
Operation 507 illustrates merging the dynamic call model characteristics generated by the plurality of different machine learning models of the dynamic call model predictor into a dynamic call model. In an embodiment, the dynamic call mode characteristics are merged to correspond to a representation of computing and network resources of the telecommunications network.
Operation 509 illustrates using the dynamic call model to allocate computing and network capacity in the telecommunications network.
The computer architecture 600 illustrated in
The mass storage device 612 is connected to the CPU 602 through a mass storage controller (not shown) connected to the bus 77. The mass storage device 612 and its associated computer-readable media provide non-volatile storage for the computer architecture 600. Although the description of computer-readable media contained herein refers to a mass storage device, such as a solid-state drive, a hard disk or optical drive, it should be appreciated by those skilled in the art that computer-readable media can be any available computer storage media or communication media that can be accessed by the computer architecture 600.
Communication media includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics changed or set in a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media.
By way of example, and not limitation, computer-readable storage media might include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer architecture 600. For purposes of the claims, the phrase “computer storage medium,” “computer-readable storage medium” and variations thereof, does not include waves, signals, and/or other transitory and/or intangible communication media, per se.
According to various implementations, the computer architecture 600 might operate in a networked environment using logical connections to remote computers through a network 650 and/or another network (not shown). A computing device implementing the computer architecture 600 might connect to the network 650 through a network interface unit 616 connected to the bus 77. It should be appreciated that the network interface unit 616 might also be utilized to connect to other types of networks and remote computer systems.
The computer architecture 600 might also include an input/output controller 618 for receiving and processing input from a number of other devices, including a keyboard, mouse, or electronic stylus (not shown in
It should be appreciated that the software components described herein might, when loaded into the CPU 602 and executed, transform the CPU 602 and the overall computer architecture 600 from a general-purpose computing system into a special-purpose computing system customized to facilitate the functionality presented herein. The CPU 602 might be constructed from any number of transistors or other discrete circuit elements, which might individually or collectively assume any number of states. More specifically, the CPU 602 might operate as a finite-state machine, in response to executable instructions contained within the software modules disclosed herein. These computer-executable instructions might transform the CPU 602 by specifying how the CPU 602 transitions between states, thereby transforming the transistors or other discrete hardware elements constituting the CPU 602.
Encoding the software modules presented herein might also transform the physical structure of the computer-readable media presented herein. The specific transformation of physical structure might depend on various factors, in different implementations of this description. Examples of such factors might include, but are not limited to, the technology used to implement the computer-readable media, whether the computer-readable media is characterized as primary or secondary storage, and the like. If the computer-readable media is implemented as semiconductor-based memory, the software disclosed herein might be encoded on the computer-readable media by transforming the physical state of the semiconductor memory. For example, the software might transform the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory. The software might also transform the physical state of such components in order to store data thereupon.
As another example, the computer-readable media disclosed herein might be implemented using magnetic or optical technology. In such implementations, the software presented herein might transform the physical state of magnetic or optical media, when the software is encoded therein. These transformations might include altering the magnetic characteristics of locations within given magnetic media. These transformations might also include altering the physical features or characteristics of locations within given optical media, to change the optical characteristics of those locations. Other transformations of physical media are possible without departing from the scope and spirit of the present description, with the foregoing examples provided only to facilitate this discussion.
In light of the above, it should be appreciated that many types of physical transformations take place in the computer architecture 600 in order to store and execute the software components presented herein. It also should be appreciated that the computer architecture 600 might include other types of computing devices, including hand-held computers, embedded computer systems, personal digital assistants, and other types of computing devices known to those skilled in the art.
It is also contemplated that the computer architecture 600 might not include all of the components shown in
The network 704 can be or can include various access networks. For example, one or more client devices 706(1) . . . 706(N) can communicate with the host system 702 via the network 704 and/or other connections. The host system 702 and/or client devices can include, but are not limited to, any one of a variety of devices, including portable devices or stationary devices such as a server computer, a smart phone, a mobile phone, a personal digital assistant (PDA), an electronic book device, a laptop computer, a desktop computer, a tablet computer, a portable computer, a gaming console, a personal media player device, or any other electronic device.
According to various implementations, the functionality of the host system 702 can be provided by one or more servers that are executing as part of, or in communication with, the network 704. A server can host various services, virtual machines, portals, and/or other resources. For example, a can host or provide access to one or more portals, Web sites, and/or other information.
The host system 702 can include processor(s) 708 memory 710. The memory 710 can comprise an operating system 712, application(s) 714, and/or a file system 716. Moreover, the memory 710 can comprise the storage unit(s) 82 described above with respect to
The processor(s) 708 can be a single processing unit or a number of units, each of which could include multiple different processing units. The processor(s) can include a microprocessor, a microcomputer, a microcontroller, a digital signal processor, a central processing unit (CPU), a graphics processing unit (GPU), a security processor etc. Alternatively, or in addition, some or all of the techniques described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include a Field-Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Standard Products (ASSP), a state machine, a Complex Programmable Logic Device (CPLD), other logic circuitry, a system on chip (SoC), and/or any other devices that perform operations based on instructions. Among other capabilities, the processor(s) may be configured to fetch and execute computer-readable instructions stored in the memory 710.
The memory 710 can include one or a combination of computer-readable media. As used herein, “computer-readable media” includes computer storage media and communication media.
Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, phase change memory (PCM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory technology, compact disk ROM (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store information for access by a computing device.
In contrast, communication media includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave. As defined herein, computer storage media does not include communication media.
The host system 702 can communicate over the network 704 via network interfaces 718. The network interfaces 718 can include various types of network hardware and software for supporting communications between two or more devices. The host system 702 may also include machine learning model 719.
In closing, although the various techniques have been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended representations is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed subject matter.
The disclosure presented herein also encompasses the subject matter set forth in the following clauses:
