IDENTIFYING THE USER EXPERIENCE AND SLA FULFILLMENT IMPACT OF CONTROL APPLICATIONS PRIOR TO DEPLOYMENT

Embodiments of the invention provide a computer-implemented method that includes intercepting, using a controller of a programmable network (PN), a deployment of a control application in the PN. Responsive to intercepting the deployment of the control application, a user experience (UE) analysis is performed. The UE analysis includes determining, based at least in part on a set of digital twins, a UE impact of the control application; and, based at least in part on the UE impact, generating a UE-based control application deployment recommendation. The UE analysis further includes deploying the control application based at least in part on the UE-based control application deployment recommendation.

BACKGROUND

The present invention relates generally to programmable networks. More specifically, the present invention relates to programmable computer systems, computer-implemented methods, and computer program products operable to identify the user experience (UE) and service level agreement (SLA) fulfillment impact of control application software prior to deployment of the control application in its programmable network.

A communications service provider (CSP) offers telecommunications services or some combination of information service, media services, content, entertainment, and application services over networks, thereby leveraging the network infrastructure as a rich, functional platform. CSPs include telecommunications carriers; content and application service providers (CASP); cable service providers; satellite broadcasting operators; cloud communications service providers; and the like. CSPs use networks configured as programmable networks (PNs) that decouple the network control logic from the devices performing the function. Such devices can include, for example, the network routers that control the movement of information in the underlying network.

An example type of PN is so-called software-defined networks (SDNs). In general, SDNs decouple the control logic in a network from the devices performing network functions. Such devices can include, for example, the network routers that control the movement of information in the underlying network. SDNs include three main components, namely, SDN control applications, SDN controllers, and networking devices. These components may or may not be located in the same physical area. The SDN controller relays information about the network or requests for specific resource availability or allocation. The SDN controller communicates with the SDN control applications to determine the destination of data packets. Network devices receive instructions from the SDN controller regarding how to route the packets. A CSP can deploy several SDN control applications, each having its own operational goals.

The SDN controller is the core of an SDN. It resides between network devices at one end of the network and SDN control applications at the other end. Any communication between SDN control applications and network devices must go through the SDN controller. SDN control applications direct traffic according to forwarding policies that a network operator puts in place, thereby minimizing manual configurations for individual network devices. By taking the control plane off of the network hardware and running it instead as software, the centralized controller facilitates automated network management and makes it easier to integrate and administer business applications. In effect, the SDN controller serves as a sort of operating system (OS) for the network.

However, known PNs and/or SDNs have shortcomings. For example, known PNs/SDNs do not identify the customer/user experience and/or SLA fulfillment impact of each control application as part of its deployment process. Accordingly, it can be unclear how a specific update from a specific control application relates to or will impact the quality of experience (QoE) and/or SLA fulfillment for end consumers/users. Additionally, some control application updates perform improvements that are actually not needed as all users have their adequate and desired QoE fulfilled at the observed point of time, and at the same point of time there is no higher-level policy to justify such updates (e.g., energy saving).

Various factors contribute to the difficulty that known PNs/SDNs would experience if they attempted to evaluate the UE/SLA impact of a control application. For example, the operational network can have users with diverse applications, and the user-devices can include their own diverse requirements and capabilities, which cannot be simulated in a lab using known techniques. As another example, it is difficult to evaluate the combined impact of multiple control applications. For example, when two or more control applications govern the network's behavior, it is not straightforward to identify their combined effect on QoE and how actions enforced by each contribute to or detract from QoE and/or SLA fulfillment.

SUMMARY

Embodiments of the invention provide a computer-implemented method that includes intercepting, using a controller of a programmable network (PN), a deployment of a control application in the PN. Responsive to intercepting the deployment of the control application, a user experience (UE) analysis is performed. The UE analysis includes determining, based at least in part on a set of digital twins, a UE impact of the control application; and, based at least in part on the UE impact, generating a UE-based control application deployment recommendation. The UE analysis further includes deploying the control application based at least in part on the UE-based control application deployment recommendation.

Thus, embodiments of the invention provide improvements over known methods of PNs/SDNS by using digital twins of the network, users/customers, and user-devices to provide robust and dynamic data that can be used to predict or identify the customer/user experience and/or SLA fulfillment impact of each control application as part of its pre-deployment process. Accordingly, embodiments of the invention provide visibility into how a specific update from a specific control application relates to or will impact the quality of experience (QoE) and/or SLA fulfillment for end consumers/users. Additionally, embodiments of the invention provide visibility into the control applications and updates that perform improvements that are actually needed vs the control applications and updates that perform improvement that are not needed as all users have their adequate and desired QoE fulfilled at the observed point of time, and at the same point of time there is no higher-level policy to justify such updates (e.g., energy saving).

In addition to one or more of the features described above, or as an alternative to any of the foregoing embodiments, the UE analysis further includes predicting a change in how a deployed control application will perform after deploying the control application; predicting the change in how the deployed control application and the control application will perform after deploying the control application based at least in part on a PN condition; and predicting the change in how the deployed control application and the control application will perform after deploying the control application is based at least in part on a capability of a user-device deployed in the PN.

Thus, embodiments of the invention provide improvements over known methods of PNs/SDNS by using digital twins of the network, users/customers, and user-devices to provide robust and dynamic data that can be used to predict or identify the customer/user experience and/or SLA fulfillment impact of each control application as part of its pre-deployment process. Accordingly, embodiments of the invention provide visibility into how a specific update from a specific control application relates to or will impact or change the quality of experience (QoE) and/or SLA fulfillment for end consumers/users based at least in part on a PN condition; and/or a capability of a user-device deployed in the PN.

Embodiments of the invention further provide a computer system and a computer program product having substantially the same features and as the above-described computer-implemented method.

Additional features and advantages are realized through the techniques described herein. Other embodiments and aspects are described in detail herein. For a better understanding, refer to the description and to the drawings.

In the accompanying figures and following detailed description of the disclosed embodiments, the various elements illustrated in the figures are provided with three or four digit reference numbers, where possible. The leftmost digit(s) of each reference number corresponds to the figure in which its element is first illustrated.

DETAILED DESCRIPTION

Many of the functional units described in this specification are illustrated as logical blocks such as generators, discriminators, modules, processors, and the like. Embodiments of the invention apply to a wide variety of implementations of the logical blocks described herein. For example, a given logical block can be implemented as a hardware circuit operable to include custom VLSI circuits or gate arrays, as well as off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. The logical blocks can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, and the like. The logical blocks can also be implemented in software for execution by various types of processors. Some logical blocks described herein can be implemented as one or more physical or logical blocks of computer instructions which can, for instance, be organized as an object, procedure, or function. The executables of a logical block described herein need not be physically located together but can include disparate instructions stored in different locations which, when joined logically together, include the logical block and achieve the stated purpose for the logical block.

Turning now to a more detailed description of technologies that are relevant to aspects of the invention, a communications service provider (CSP) offers telecommunications services or some combination of information service, media services, content, entertainment, and application services over networks, thereby leveraging the network infrastructure as a rich, functional platform. CSPs include telecommunications carriers; content and application service providers (CASP); cable service providers; satellite broadcasting operators; cloud communications service providers; and the like. CSPs use networks configured as programmable networks (PNs) that decouple the network control logic from the devices performing the function. Such devices can include, for example, the network routers that control the movement of information in the underlying network.

One example of a PN is software-defined networks (SDNs). SDNs include three main components, namely, SDN control applications, SDN controller, and networking devices. These components may or may not be located in the same physical area. SDN control applications are triggered by the real-time events from the network. The control applications identify and enforce the appropriate network updates with respect to their network operations goal. For example, an SDN control application can reroute network data traffic to avoid predicted congestion. An SDN control application can also increase the transmission power of a radio cell in a radio access network (RAN) to improve the coverage for the customers/users located at the cell edge. A CSP can deploy several SDN control applications, each having its own operational goals.

Turning now to an overview of aspects of the present invention, embodiments of the invention provide programmable computer systems, computer-implemented methods, and computer program products operable to identify the UE, and SLA fulfillment impact of control application software in PNs prior to deployment of the control application. In embodiments of the invention, application (or software) deployment is the process of installing, configuring, and enabling a specific application or set of applications, usually through an application manager (app manager) or software management system, to a specific URL (uniform resource locator) on a server. In some embodiments of the invention, the to-be-deployed control application is a new control application. In some embodiments of the invention, the to-be-deployed control application is an update for an existing and deployed control application. In embodiments of the invention, a mechanism is provided for analyzing the impact of a control application or a set of control applications on the perceived UE. UE is often referred to as QoE. In general, QoE is a measure of the delight or annoyance a user experiences when utilizing a service such as web browsing, phone calls, television broadcasts, and the like. In embodiments of the invention, the mechanism for analyzing the impact of a control application or a set of control applications on the perceived UE leverages the use of a set of digital domain twins operable to include any combination of PN digital twins, user digital twins, and user-device digital twins. The mechanism for analyzing UE impact can also be used to identify and propose an optimal combination of control applications for reaching specific operational goals (e.g., SLA fulfillment with the least energy consumption, SLA fulfillment with superior service for premium customers, etc.). The mechanism for analyzing UE impact can be applied on a to-be-deployed control application to ensure that to-be-deployed controlled applications are controlled and deployed in a manner that does not degrade or negatively impact UE/QoE, and/or SLA fulfillment requirements.

Turning now to a more detailed description of aspects of the present invention,FIG.1is a non-limiting, simplified block diagram of a system100in accordance with embodiments of the invention. The system100includes an SDN architecture110in communication with a CSP150and an optional cloud computing system50, configured and arranged as shown. In embodiments of the invention, the SDN architecture110includes an application layer120, a control layer130, and an infrastructure layer140, configured and arranged as shown. The SDN110can be split into a management plane, a control plane, and a data plane, where the application layer120is part of the management plane; the control layer130is part of the control plane; and the infrastructure layer140is part of the data plane. The management plane is represented by network management/control applications such as those related to traffic engineering, mobility management, wireless communications, security, and reliability. These management/control applications are implemented using network programming languages and they interact with the control plane through an open northbound application programming interface (API). The control plane controls the forwarding devices via an open southbound API. The control plane is responsible for sending commands to the forwarding devices in order for them to apply the required networking policy. The data plane is formed from forwarding devices (e.g., switches, routers, etc.).

The application layer120includes an application pool122, which includes a control application pool124having available control applications designated as Ctrl App1, Ctrl App2, . . . , Ctrl AppK (where “K” is the number of available control applications). The control layer130includes an SDN controller132, a user experience (UE) (and/or QoE) analysis module134, and a UE (and/or QoE) recommendation module136, configured and arranged as shown. The infrastructure layer140includes a variety of infrastructure hardware devices, including, for example COTS (commercial off-the-shelf) hardware such as switch devices142.

In embodiments of the invention, a cloud computing system50is, optionally, in wired or wireless communication with one or more components/modules of the system100. Cloud computing system50can supplement, support, or replace some or all of the functionality of the components/modules of the system100. Additionally, some or all of the functionality of the components/modules that form the system100can be implemented as a node of the cloud computing system50. Additional details of how cloud computing functionality can be implemented in accordance with aspects of the invention are depicted by the computing environment1200shown inFIG.12and described in greater detail subsequently herein.

The various components/modules of the system100shown inFIG.1are depicted separately for ease of illustration and explanation. In embodiments of the invention, the functions performed by the various components/modules of the system100can be distributed differently than shown. For example, in some embodiments of the invention, some or all of the functionality of the UE analysis module134and/or the UE recommendation module136could be integrated into other network components of the CSP150and/or the cloud computing system50.

In accordance with embodiments of the invention, the UE analysis module134is operable to analyze the impact of a to-be-deployed control application, or a set of control applications including the to-be-controlled control application, on the perceived UE by leveraging the creation of a set of digital domain twins160A that track a digital domain160of the SDN architecture110. In accordance with embodiments of the invention, the digital domain160is designated as including the actual network162(corresponding to the SDN110), users164, and the user-device166. Further in accordance with embodiments of the invention, a set of digital domain twins160A are created, with each set including network digital twins162A, user digital twins164A, and user-device digital twins166A. The digital twins described herein in connection with embodiments of the invention can each be viewed as a machine that emulates or “twins” the life of a physical entity. A digital twin in accordance with aspects of the invention is more than just a simple simulation or a static model. The disclosed digital twin is a continuously evolving model that is always aware of the events happening in its physical twin as it follows the physical twin's lifecycle to supervise and optimize the physical twin's functions. The synchronization between the digital twin and its physical counterpart is possible because of the real-time data uploading ensured by IoT devices and sensor technology, while big data storage capabilities enable the capture and maintenance of historical data that can also be useful for the digital twin. AI/ML algorithms can be used to predict future states of the physical twin. A digital twin can also simulate new configurations for the corresponding physical twin before the physical twin is deployed.

In aspects of the invention, the network digital twin162A can be created and maintained using domain infrastructure-related data including the installed network hardware assets; spectrum carriers; technology support; hardware locations and capabilities; domain user-plane states including performance and fault data; and domain control-plane state including the set of deployed and active control applications, along with a sequential set of triggered actions from the control applications (i.e., control application activity). In aspects of the invention, the user digital twin164A can be created and maintained using user-related data including user location, user mobility, user activity patterns, application usage, desired QoE, current QoE, customer profile, SLAs and end-user service activity. In aspects of the invention, the user-device digital twin166A can be created and maintained using information about user-devices in use, user-device capabilities, perceived network conditions, and carriers supported.

In embodiments of the invention, UE analysis module134performs a variety of operations including, for example, intercepting updates to deployed control applications, and the creation of application activity history (e.g., block202B shown inFIG.2; and App activity histories920shown inFIG.9A). In embodiments of the invention, the UE analysis module134is operable to perform a pre-deployment impact analysis for an activity or a sequential set of activities from a control application (from the application activity history) to identify the impact on the QoE of the active users/subscribers). In embodiments of the invention, the UE analysis module134is further operable to perform periodic analysis (e.g., in a tact of tens of minutes) using simulations on the set of digital domain twins160A in order to project the user QoE impact of individual control applications; project the user QoE impact of various deployment combinations from the available set of control applications; identification of the optimal control application combination for the observed domain for the specific QoE fulfillment; and identification of the unnecessary operations loops that target improvements of QoE that are already within the agreed SLA. In embodiments of the invention, the UE analysis module134is further operable to recognize in an automated manner network conditions and specific QoE fulfillment requirements for which optimal control application combination have been identified. In embodiments of the invention, the UE analysis module134is further operable to perform automated deployment of the identified optimal control application combinations in the domain for which network conditions and specific QoE fulfillment requirements are set. In embodiments of the invention, the UE analysis module134is further operable to provide a test bed to the control application owners and operators to verify the control applications on a scale that is not available in a lab environment.

Additional details of how the system100operates are illustrated by the computer-implemented methodologies200,300,400shown inFIGS.2,3, and4, respectively. The operation of the system100and the methodologies200,300,400will now be provided with reference toFIGS.1,2,3, and4. Turning first toFIG.2, the methodology200begins at block202by continuously collecting and/or updating operation data of the digital domain160(i.e., digital domain operation data) during operation of the SDN110(shown inFIG.1). Blocks202A,202B,202C depict examples of the digital domain operation data continuously collected/updated at block202. At block202A, SLA fulfillment goals and UE reports are collected. At block202B, to-be-deployed control applications (new control applications and/or updates to existing control applications) are intercepted prior to deployment, and App activity histories920(shown inFIG.9A) are created. At block202C, snapshots are created of operational activity of the SDN110, including network, user, and user-device information to capture network dynamics over time. At block204the methodology200uses the continuously collected/updated digital domain operation data to create/update the set of digital domain twins160A, including any combination of the SDN digital twin162A; the user digital twin164A; and/or the user-device digital twin166A. Decision block206repeats block204according to a schedule (periodic, event-based, etc.) as long as the SDN110is operating and generating the digital domain operation data that is continuously collected and updated at block202. If it is determined at decision block206that the SDN110is no longer operating, the methodology200moves to block208and ends.

FIG.5depicts additional details of how the user digital twin164A of block204andFIG.1can be generated using a digitization module520and a digital twin processor module510in accordance with aspects of the invention. The process of creating the user digital twin164A can be slightly different from the process of creating the network digital twin162A and the user-device digital twin166A in that, for a human digital twin, not only the outer aspects of a human being (e.g., physical and physiological characteristics) are captured, but also inner qualities can be captured such as personality, sensibilities, predicted thoughts, and skills. Expressing each user's individuality through its user digital twin can improve the quality of the UE/SLA fulfillment assessments performed in accordance with aspects of the invention. As shown the digitization module520digitizes a variety of data types, including, for example, user profiles512, UE/SLA reports514, snapshots516(corresponding to the user twin snapshots700shown inFIG.7), and network usage518. The digitization module520provides the digitized data to the digital twin processor510where known machine learning technologies are used to create and update the user digital twin164A.

Turning now toFIG.3, the methodology300is operable to simulate a variety of SDN operating conditions and a variety of combinations of SDN control applications through the digital domain twins160A (generated using the methodology200) to capture the UE and SLA fulfillment impact of the various scenarios. The methodology300begins at block302by selecting/updating a set of SDN operating conditions. The SDN operating conditions can be periodically set or can be event-based. The methodology300moves to block304and selects/updates a combination of SDN control applications. At block306, the methodology300applies the selected/updated set of SDN operating conditions and the selected/updated combination of deployed SDN control applications to the set of digital domain twins160A, including any combination of the SDN digital twin162A, the user digital twin164A, and/or the user-device digital twin166A. At block308, the methodology300captures the UE and SLA fulfillment impact generated by the set of digital domain twins160A. The methodology300further captures the selected/updated set of SDN operating conditions and the selected/updated combination of deployed SDN control applications. At decision block310, the methodology determines whether there are additional SDN control application combinations. If the answer to the inquiry at decision block310is yes, the methodology300returns to block304and performs additional iterations of the operations at blocks304,306,308. If the answer to the inquiry at decision block310is no, the methodology300moves to decision block312to determines whether there are additional SDN control application combinations. If the answer to the inquiry at decision block312is yes, the methodology300returns to block302and performs additional iterations of the operations at blocks302,304,306,308,310. If the answer to the inquiry at decision block312is no, the methodology300moves to block314and ends.

Turning now toFIG.4, the methodology400is operable to utilize the data captured at block308, along with other data, to predict/recommend a combination of SDN control applications to deploy with a to-be-deployed SDN control application in order to best satisfy UE and SLA fulfillment requirements. The methodology400begins by performing, substantially in parallel, the operations at blocks402,404,406. At block402, the methodology400accesses a to-be-deployed SDN control application. At block404, the methodology400accesses actual SDN operating conditions. At block406, the methodology400accesses the captured UE and SLA fulfillment impact generated by the set of digital domain twins160A, which includes the network digital twin162A, the user digital twin164A, and the user-device digital twin166A. The methodology400further captures the selected/updated set of SDN operating conditions and the selected/updated combination of deployed SDN control applications. The outputs from blocks402,404,406are provided to block408. At block408, the methodology400predicts/updates the UE/SLA impact of the to-be-deployed SDN control application. The outputs from blocks402,404,406,408are provided to block410. At block410, the methodology400predicts/recommends a combination of SDN control applications to deploy with the to-be-deployed SDN control application in order to best satisfy UE and SLA fulfillment requirements. At decision block412, the methodology400determines whether the SDN conditions have changed. If the answer to the inquiry at decision block412is yes, the methodology400returns to block408and performs additional iterations of the operations at blocks408,410,412. If the answer to the inquiry at decision block412is no, the methodology400moves to block414and waits then returns to the input to decision block412.

Moving ahead toFIG.9A,FIG.9Adepicts a block diagram illustrating mechanisms and process flows of a PN system900in accordance with embodiments of the invention. The controller132(which corresponds to the controller132shown inFIG.1) controls the assigned controller domain910. The controller132hosts “N” (N=whole number) total available control applications. The hosted control applications are depicted as App1, App2. . . AppN. The control applications (App1, App2. . . AppN) register to receive specific events from the PN (e.g., SDN architecture110shown inFIG.1) and react according to their own operational logic.

In accordance with aspects of the invention, the digital domain twins160A includes a network digital twin162A, a user digital twin164A, and a user-device digital twin166A. The controller132is operable to control the dynamic creation and updating (e.g., using the methodology200shown inFIG.2) of the digital domain twins160A using continuously collected and updated digital domain operation data (e.g., block202of the methodology200shown inFIG.2). For the network digital twin162A, the digital domain operation data includes information about the user164(shown inFIGS.1and5), control plane conditions, and installed infrastructure details (e.g., infrastructure layer140shown inFIG.1). The digital domain operation data can also contain information about the connectivity between each individual user and the network infrastructure layer140(e.g., switching devices, radio assets, and the like). Examples of the types of digital domain operation data that can be used to create the network digital twin162A are depicted in the network twin snapshot table600shown inFIG.6.

For the user digital twin164A, the digital domain operation data includes information about each user's SLA, perceived QoE, the desired QoE, the user's activity/mobility, lists of the user's active sessions, and the end-user's services. Examples of the types of digital domain operation data that can be used to create the user digital twin164A are depicted in the user twin snapshot table700shown inFIG.7. For the user-device digital twin166A, the digital domain operation data includes information about the end-user individual devices and their specific capabilities. The digital domain operation data also contains the information about the relation between the user-devices (e.g., User-device1, User-device2, User-device3shown inFIG.9B; and user-device166shown inFIG.1) and individual users164. Examples of the types of digital domain operation data that can be used to create the user-device digital twin166A are depicted in the user-device twin snapshot table800shown inFIG.8.

The digital domain operation data reflected in the tables600,700,800(shown inFIGS.6-8) is collected periodically according to a schedule, or is collected for events (outdoor concert), areas (near office buildings during business hours), or time periods (family-centric holidays like Thanksgiving) that are likely to provide useful data about UE/SLA impact. The digital domain operation data reflected in the tables600,700,800can be collected from different sources like databases, configurations from OSS (operational support system), performance data from OSS, probe data, call traces, device vendors, a network protocol analyzer (e.g., Wireshark®), inventory tools, BSS (business support system) data, and the like. The controller132can create a digital domain twin snapshot930of the digital domain twins160A at a specific point of time, which includes all the information that is collected until that point of time, and sends it to the analytics layer940in a network of an owner of the relevant PN(s) with the specific ask. The controller132also collects control application activity (operations performed by the various control applications of the controller132) and creates application activity histories920, which are passed to the analytics layer940together with the digital domain twin snapshot(s)930of the digital domain twins160A.

The analytics layer940runs the AI/ML algorithms (e.g., the classifier system1000shown inFIG.10) using the digital domain twin snapshot930of the digital domain twins160A and the application activity history920as the input. In accordance with some embodiments of the invention, the AI/ML algorithms are trained to perform tasks associated with predicting the UE/SLA impact of a to-be-deployed control application and/or a combination of the to-be-deployed control applications and other control applications in the runtime of a programmable network such as an SDN. In accordance with some embodiments of the invention, the AI/ML algorithms can also be trained to perform tasks associated with recommending combinations of control applications (e.g., the to-be-deployed control application and other control applications in the runtime of a programmable network) that satisfy the UE/SLA and operator-selected specific operational goals (e.g., SLA fulfillment with the least energy consumption, SLA fulfillment with superior service for premium customers, etc.) while taking into account characteristics of the relevant PN (e.g., PN1, PN2shown inFIG.9B), users164(shown inFIGS.1and5), and user-devices (e.g., User-device1, User-device2, User-device3shown inFIG.9B). In accordance with some embodiments of the invention, the AI/ML algorithms can also be trained to perform tasks associated with recommending combinations of control applications (e.g., the to-be-deployed control application and other control applications in the runtime of a programmable network) that optimize UE/SLA levels while taking into account characteristics of the relevant PN, users164, and user-devices. The UE/SLA-related tasks of the AI/ML algorithms can be targeted to prioritize certain results, including, for example, prioritizing one combination of control applications (e.g., the to-be-deployed control application and other control applications in the runtime of a programmable network) among many that reduces energy consumption the most or achieves a superior UE/SLA for specific devices.

In accordance with embodiments of the invention, the UE/SLA-related tasks of the AI/ML algorithms of the analytics layer940can also include generating its QoE impact assessment in the form of an icon-based GUI (graphical user interface) in which icons are used to represent different types of PNs; different types of users; different types of user-devices; and different combinations of the control application deployment combinations that each satisfy the UE and SLA fulfillment requirements. The icon-based GUI can use a pre-deployment view of the control application deployment combinations that can be compared with a “predicted” post deployment view of the control application deployment combinations to show graphically how the UE and SLA fulfillment prior to deployment of the new/updated control application combination is predicted to compare with the UE and SLA fulfillment after deployment of the new/updated control application combination. The icon-based GUI can be displayed to, for example, an operator950.

In accordance with embodiments of the invention, the UE/SLA-related tasks of the AI/ML algorithms of the analytics layer940can also include generating test results970towards the controller132, which is relevant for automated assessment and deployment of control applications. In accordance with embodiments of the invention, the UE/SLA-related tasks of the AI/ML algorithms of the analytics layer940can also include generating instructions for QoE fulfillment960towards the controller132. The controller132can leverage this information to automatically reconfigure the deployed application set.

In embodiments of the invention, the owners of the relevant PNs (e.g., SDN architecture110) have “K” number of control applications that can govern network behavior. The control applications are deployed to run in specific network domains, such that two domains might have a different set of control applications running at the observed point of time, for example, urban vs. rural regions; regions with a high share of ultra-low latency traffic vs. regions with a low share of ultra-low latency traffic; regions with high level redundancy vs. regions with low levels of redundancy; similar functionality control applications from different vendors, and the like. As the conditions of the PN change, e.g., ultra-low latency traffic shifts to a different domain, the set of control applications that was previously deployed in the observed domain might not be optimal anymore. In such cases, embodiments of the invention can be used to automatically identify sub-optimal domain behavior governance and propose a different set of the control applications to be deployed in the domain under specific conditions.

It can also be the case that a certain set of control application deployment combinations may improve certain end user's desired QoE (like low latency requirement) but may lead to degradation of certain end-user's desired QoE (like data throughput) so the analytics layer940would also be capable of handling the control applications' implementation gaps and accordingly would be able to configure different control applications sets for different users served by same base station in runtime.

In accordance with embodiments of the invention, the analytics layer940is operable to considers user-device capabilities when assessing control application actions because it could be the case that a user-device A may be supporting three (3) carriers while user-device B may be only supporting one (1) carrier so UE/SLA impact of control applications may be visible for user-device A but not on user-device B so a diverse set of control applications would be needed to overcome the UE/SLA issue in this scenario.

Accordingly, it can be seem from the foregoing detailed description that embodiments of the invention provide technical effects and benefits. In known PNs/SDNs, updates issued by the control applications are not justified in the context of the customer QoE/SLA fulfillment. The failure to adequately factor in customer QoE/SLA considerations results in unclear customer impact of deployed new/updated control application in that current control application deployments are focused on network operations goal so it is not clear how a specific update from a specific control application relates to the QoE/SLA fulfilment and the type of impact the specific update has on end consumers. The failure to adequately factor in customer QoE/SLA considerations results in unnecessary control application updates. In other words, some updates may perform improvements that are actually not needed as all users have adequate and desired QoE/SLA fulfilled at the observed point of time and at the same point of time there is no higher-level policy to justify such updates (e.g., energy saving). The failure to adequately factor in customer QoE/SLA considerations results in difficulties in evaluating the impact of a control application. For example, the operational network may have users with diverse applications and end devices with diverse requirements and capabilities, which cannot be simulated in lab using known techniques. The failure to adequately factor in customer QoE/SLA considerations results in Difficulties in evaluating combined impact of multiple control apps, e.g., when two or more applications govern the network behavior it is not straightforward to identify their combined effect on the QoE and how actions enforced by each contribute to the SLA fulfilment. In this invention proposal we suggest a mechanism for assessment of impact of a control application or a set of applications on the perceived QoE. The mechanism can also be used to identify and propose the optimal combination of control applications for reaching specific operational goal (e.g., SLA fulfilment with the least energy consumption, SLA fulfilment with superior service for premium customers, etc.).

Embodiments of the invention address the shortcomings in existing PN/SDN technologies by developing novel UE/QoE/SLA analysis techniques operable to closely monitor and analyze control applications prior to deployment, and taking into account all consumer-impacting scenarios before putting the control applications into operation. Embodiments of the invention collects real network snapshots, create a set of digital twins (network, user/customer and user-device levels) and assess or predict the QoE impact when given control application updates scenarios in the digital twin before deployment throughout the real network.

More specifically, the novel UE/QoE/SLA analysis techniques disclosed herein provide continuous and dynamic assessment of the UE/QoE/SLA effect of an observed pre-deployment control application or a combination of pre-deployment control applications by generating and leveraging a digital network twin and event history data gathered from the real operational network. The novel UE/QoE/SLA analysis techniques can include, inter alia, creating a set of digital domain twins for the control plane entities; intercepting updates from deployed control applications for the creation of control application activity history; performing impact analysis for an activity or a sequential set of activities from a control application (from the application activity history) to identify the impact on the UE/QoE/SLA of the active subscribers; performing periodical analysis (in a tact of tens of minutes) using simulations on the fresh digital domain twin; recognizing in and automated manner network conditions and specific UE/QoE/SLA fulfilment requirements for which optimal control application combinations have been identified; performing automated deployment of the identified optimal control application combination in the domain in which network conditions and specific UE/QoE/SLA fulfilment requirements; and providing test beds to the control application owners and operators to verify the control applications on a scale that is not available in the lab environment.

In embodiments of the invention, creating the digital domain twin for the control plane entities includes information on a network twin, a user/customer twin, and a user-device twin. The network twin is generated and maintained using domain infrastructure-related data including the installed network hardware assets, spectrum carriers, technology support, their location and capabilities. The network twin is also generated and maintained using domain user-plane state including performance and fault data, as well as domain control-plane state including the set of deployed and active control applications and a sequential set of triggered actions from the control applications (activity). The user/customer twin is generated and maintained using user-related data including user location, mobility and activity patterns, application usage, desired QoE, current QoE, customer profile, SLAs and end-user service activity. The user-device twin covers terminals in use, terminal capabilities, perceived network conditions, carriers supported, and the like.

In embodiments of the invention, the above-described performing of periodical analysis (in a tact of tens of minutes) using simulations on the fresh digital domain twin is perform in order to: project the subscriber QoE impact of individual control applications; project the subscriber QoE impact of various deployment combinations from the available set of control applications and identification of the optimal control application combination for the observed domain for the specific QoE fulfilment; and identify the unnecessary operations loops that target improvements of QoE that are already within the agreed SLA.

The novel UE/QoE/SLA analysis techniques disclosed herein provide, inter alia, QoE impact assessment towards the user in the form of, for example, a GUI; test results towards the controller, which is relevant for automated assessment and deployment of control applications; instructions for QoE Fulfilment towards the controller, which can leverage this information to automatically reconfigure the deployed control application set. In accordance with aspects of the invention, operators who own programmable network have “K” number of control applications that can govern network behavior. The control applications are deployed to run in specific network domains, such that two domains might have different set of control applications running at the observed point of time, e.g., urban vs rural regions, regions with high vs low share of ultra-low latency traffic, regions with high vs low level of redundancy, similar functionality control applications from different vendors, etc. As the conditions of the network change, e.g., ultra-low latency traffic shifts to a different domain, the set of control applications that was previously deployed in the observed domain might not be optimal anymore. In such cases, the novel UE/QoE/SLA analysis techniques disclosed herein can be used to automatically identify sub-optimal domain behavior governance and propose a different set of the control applications to be deployed in the domain under specific conditions.

It can also be a case that certain sets of control application deployment can improve certain end consumers desired QoE (like low latency requirement) but can lead degradation of certain end consumers desired QoE (like data throughput) so the novel UE/QoE/SLA analysis techniques disclosed herein would also be capable of handling the control applications implementation gaps and accordingly would be able to configure different control applications sets for different users served by same base station in run time.

The novel UE/QoE/SLA analysis techniques disclosed herein considers device capabilities for taking control application actions as there can be the case device A may be supporting three (3) carriers while device B may be only supporting one (1) carrier so control application impact may be visible for device A but not on device B so a diverse set of control applications would be needed (and recommended) to overcome this scenario.

An example of machine learning techniques that can be used to implement aspects of the invention will be described with reference toFIGS.10and11. Machine learning models configured and arranged according to embodiments of the invention will be described with reference toFIG.10. Detailed descriptions of an example computing system1200and network architecture capable of implementing embodiments of the invention described herein will be provided with reference toFIG.12.

FIG.10depicts a block diagram showing a classifier system1000capable of implementing various aspects of the invention described herein. More specifically, the functionality of the system1000is used in embodiments of the invention to generate various models and/or sub-models that can be used to implement computer functionality in embodiments of the invention. The system1000includes multiple data sources1002in communication through a network1004with a classifier1010. In some aspects of the invention, the data sources1002can bypass the network1004and feed directly into the classifier1010. The data sources1002provide data/information inputs that will be evaluated by the classifier1010in accordance with embodiments of the invention. The data sources1002also provide data/information inputs that can be used by the classifier1010to train and/or update model(s)1016created by the classifier1010. The data sources1002can be implemented as a wide variety of data sources, including but not limited to, sensors configured to gather real time data, data repositories (including training data repositories), and outputs from other classifiers. The network1004can be any type of communications network, including but not limited to local networks, wide area networks, private networks, the Internet, and the like.

The classifier1010can be implemented as algorithms executed by a programmable computer such as the computing system1200(shown inFIG.12). As shown inFIG.10, the classifier1010includes a suite of machine learning (ML) algorithms1012; natural language processing (NLP) algorithms1014; and model(s)1016that are relationship (or prediction) algorithms generated (or learned) by the ML algorithms1012. The algorithms1012,1014,1016of the classifier1010are depicted separately for ease of illustration and explanation. In embodiments of the invention, the functions performed by the various algorithms1012,1014,1016of the classifier1010can be distributed differently than shown. For example, where the classifier1010is configured to perform an overall task having sub-tasks, the suite of ML algorithms1012can be segmented such that a portion of the ML algorithms1012executes each sub-task and a portion of the ML algorithms1012executes the overall task. Additionally, in some embodiments of the invention, the NLP algorithms1014can be integrated within the ML algorithms1012.

The NLP algorithms1014includes text recognition functionality that allows the classifier1010, and more specifically the ML algorithms1012, to receive natural language data (e.g., text written as English alphabet symbols) and apply elements of language processing, information retrieval, and machine learning to derive meaning from the natural language inputs and potentially take action based on the derived meaning. The NLP algorithms1014used in accordance with aspects of the invention can also include speech synthesis functionality that allows the classifier1010to translate the result(s)1020into natural language (text and audio) to communicate aspects of the result(s)1020as natural language communications.

The NLP and ML algorithms1014,1012receive and evaluate input data (i.e., training data and data-under-analysis) from the data sources1002. The ML algorithms1012include functionality that is necessary to interpret and utilize the input data's format. For example, where the data sources1002include image data, the ML algorithms1012can include visual recognition software configured to interpret image data. The ML algorithms1012apply machine learning techniques to received training data (e.g., data received from one or more of the data sources1002) in order to, over time, create/train/update one or more models1016that model the overall task and the sub-tasks that the classifier1010is designed to complete.

Referring now toFIGS.10and11collectively,FIG.11depicts an example of a learning phase1100performed by the ML algorithms1012to generate the above-described models1016. In the learning phase1100, the classifier1010extracts features from the training data and converts the features to vector representations that can be recognized and analyzed by the ML algorithms1012. The feature vectors are analyzed by the ML algorithm1012to “classify” the training data against the target model (or the model's task) and uncover relationships between and among the classified training data. Examples of suitable implementations of the ML algorithms1012include but are not limited to neural networks, support vector machines (SVMs), logistic regression, decision trees, hidden Markov Models (HMMs), etc. The learning or training performed by the ML algorithms1012can be supervised, unsupervised, or a hybrid that includes aspects of supervised and unsupervised learning. Supervised learning is when training data is already available and classified/labeled. Unsupervised learning is when training data is not classified/labeled so must be developed through iterations of the classifier1010and the ML algorithms1012. Unsupervised learning can utilize additional learning/training methods including, for example, clustering, anomaly detection, neural networks, deep learning, and the like.

When the models1016are sufficiently trained by the ML algorithms1012, the data sources1002that generate “real world” data are accessed, and the “real world” data is applied to the models1016to generate usable versions of the results1020. In some embodiments of the invention, the results1020can be fed back to the classifier1010and used by the ML algorithms1012as additional training data for updating and/or refining the models1016.

In aspects of the invention, the ML algorithms1012and the models1016can be configured to apply confidence levels (CLs) to various ones of their results/determinations (including the results1020) in order to improve the overall accuracy of the particular result/determination. When the ML algorithms1012and/or the models1016make a determination or generate a result for which the value of CL is below a predetermined threshold (TH) (i.e., CL<TH), the result/determination can be classified as having sufficiently low “confidence” to justify a conclusion that the determination/result is not valid, and this conclusion can be used to determine when, how, and/or if the determinations/results are handled in downstream processing. If CL>TH, the determination/result can be considered valid, and this conclusion can be used to determine when, how, and/or if the determinations/results are handled in downstream processing. Many different predetermined TH levels can be provided. The determinations/results with CL>TH can be ranked from the highest CL>TH to the lowest CL>TH in order to prioritize when, how, and/or if the determinations/results are handled in downstream processing.

In aspects of the invention, the classifier1010can be configured to apply confidence levels (CLs) to the results1020. When the classifier1010determines that a CL in the results1020is below a predetermined threshold (TH) (i.e., CL<TH), the results1020can be classified as sufficiently low to justify a classification of “no confidence” in the results1020. If CL>TH, the results1020can be classified as sufficiently high to justify a determination that the results1020are valid. Many different predetermined TH levels can be provided such that the results1020with CL>TH can be ranked from the highest CL>TH to the lowest CL>TH.

FIG.12depicts an example computing environment1200that can be used to implement aspects of the invention. Computing environment1200contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as computer-implemented methods and computer program products1250operable to identify the user experience (UE) and service level agreement (SLA) fulfillment impact of control application software prior to deployment of the control application in its programmable network. In addition to block1250, computing environment1200includes, for example, computer1201, wide area network (WAN)1202, end user device (EUD)1203, remote server1204, public cloud1205, and private cloud1206. In this embodiment, computer1201includes processor set1210(including processing circuitry1220and cache1221), communication fabric1211, volatile memory1212, persistent storage1213(including operating system1222and block1250, as identified above), peripheral device set1214(including user interface (UI) device set1223, storage1224, and Internet of Things (IoT) sensor set1225), and network module1215. Remote server1204includes remote database1230. Public cloud1205includes gateway1240, cloud orchestration module1241, host physical machine set1242, virtual machine set1243, and container set1244.

COMPUTER1201may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database1230. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment1200, detailed discussion is focused on a single computer, specifically computer1201, to keep the presentation as simple as possible. Computer1201may be located in a cloud, even though it is not shown in a cloud inFIG.12. On the other hand, computer1201is not required to be in a cloud except to any extent as may be affirmatively indicated.

PROCESSOR SET1210includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry1220may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry1220may implement multiple processor threads and/or multiple processor cores. Cache1221is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set1210. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set1210may be designed for working with qubits and performing quantum computing.

VOLATILE MEMORY1212is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memory1212is characterized by random access, but this is not required unless affirmatively indicated. In computer1201, the volatile memory1212is located in a single package and is internal to computer1201, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer1201.

PERIPHERAL DEVICE SET1214includes the set of peripheral devices of computer1201. Data communication connections between the peripheral devices and the other components of computer1201may be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device set1223may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storage1224is external storage, such as an external hard drive, or insertable storage, such as an SD card. Storage1224may be persistent and/or volatile. In some embodiments, storage1224may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer1201is required to have a large amount of storage (for example, where computer1201locally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor set125is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

END USER DEVICE (EUD)1203is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer1201), and may take any of the forms discussed above in connection with computer1201. EUD1203typically receives helpful and useful data from the operations of computer1201. For example, in a hypothetical case where computer1201is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module1215of computer1201through WAN1202to EUD1203. In this way, EUD1203can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD1203may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

REMOTE SERVER1204is any computer system that serves at least some data and/or functionality to computer1201. Remote server1204may be controlled and used by the same entity that operates computer1201. Remote server1204represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer1201. For example, in a hypothetical case where computer1201is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computer1201from remote database1230of remote server1204.

PUBLIC CLOUD1205is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloud1205is performed by the computer hardware and/or software of cloud orchestration module1241. The computing resources provided by public cloud1205are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set1242, which is the universe of physical computers in and/or available to public cloud1205. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set1243and/or containers from container set1244. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module1241manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway1240is the collection of computer software, hardware, and firmware that allows public cloud1205to communicate through WAN1202.

PRIVATE CLOUD1206is similar to public cloud1205, except that the computing resources are only available for use by a single enterprise. While private cloud1206is depicted as being in communication with WAN1202, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud1205and private cloud1206are both part of a larger hybrid cloud.