System and method for hierarchical computation of key performance indicators

Abstract

The present invention relates to a system and method for hierarchical computation of KPI(s). The method comprises the step of receiving counter(s) associated with each node from one or more nodes for at least one time slice. The method further comprises the step of defining metrics for each node in each time slice based on the associated counter(s). The method then comprises the step of computing time-aggregated values ​​and locally aggregated values ​​of the metrics. The method further comprises the step of mapping the time-aggregated values ​​of each metric to a control center hierarchy (CCH). Additionally, the method calculates a hierarchically aggregated value for each metric existing in a target list of metrics associated with a KPI expression(s) corresponding to the CCH, based on the aggregation of all time-aggregated values ​​of each metric. The method then comprises the step of computing KPI(s) for the CCH based on at least the hierarchically aggregated values ​​and the KPI expression(s) by a KPI scheduler unit.

Description

Technology Field

[0001] The present invention generally relates to Key Performance Indicator (KPI) computation, and more specifically, to systems and methods for hierarchical computation of one or more Key Performance Indicators (KPIs) for a control center layer of a telecommunications network for a target time interval comprising at least one time slice. Background Technology

[0002] The following description of related technology is intended to provide background information within the field of the present disclosure. This section may include specific aspects of related technology that may relate to the various features of the present disclosure. However, it should be understood that this section is not intended to acknowledge prior art but is used solely to enhance the reader's understanding in relation to the present disclosure.

[0003] Wireless networks, currently widely deployed to provide various communication services such as voice, video, data, content, advertising, messaging, and broadcasts, generally comprise multiple access networks and support communications for multiple users by sharing available network resources. An example of such a network is E-UTRA (Evolved Universal Terrestrial Radio Access), a radio access network standard intended to replace the Universal Mobile Telecommunications System (UMTS) and High-Speed ​​Downlink Packet Access / High-Speed ​​Uplink Packet Access (HSUPA) techniques specified in 3GPP Release 5 or later. Unlike HSPA, LTE's E-UTRA is an entirely new air interface system that is not related to W-CDMA and is therefore incompatible with it. E-UTRA offers higher data rates and lower latency and is optimized for packet data. The initial UTRAN (UMTS Terrestrial Radio Access Network) is a RAN (radio access network) defined as part of UMTS (Universal Mobile Telecommunications System), a 3G (third generation) mobile telephone technique supported by 3GPP (3rd Generation Partnership Project).UMTS, the successor to GSM (Global System for Mobile Communications) techniques, currently supports various air interface standards, such as W-CDMA (Wideband-Code Division Multiple Access), TD-CDMA (Time Division-Code Division Multiple Access), and TD-SCDMA (Time Division-Synchronous Code Division Multiple Access). UMTS also supports improved 3G data communication protocols, such as HSPA (High-Speed ​​Packet Access), which provide higher data transfer rates and capacities to associated UMTS networks. Furthermore, as the demand for mobile data and voice access continues to grow, research and development are ongoing to advance techniques not only to satisfy these increasing access demands but also to improve and enhance the user experience through user devices. e-UTRA, which is one of the techniques that evolved the first GSM / EDGE, UMTS / HSPA, CDMA2000 / EV-DO, and TD-SCDMA radio interfaces with 3GPP Release 8, is designed to provide a single evolutionary path to provide increased data rates and spectrum efficiency and allow for the provision of greater functionality.

[0004] In addition, 3GPP introduced the Release 13 NB-IoT (Narrow Band Internet of Things) technique. Low-end IoT applications can be met by this technique. Efforts have been made to respond to the IoT market by completing the standardization of NB-IoT. The NB-IoT technique has been implemented in licensed bands. LTE licensed bands are used to utilize this technique. This technique uses a minimum system bandwidth of 180 kHz, meaning that one Physical Resource Block (PRB) is allocated for it. NB-IoT can be viewed as a distinct Radio Access Technology (RAT). NB-IoT can be deployed in three modes: "In-band," "Guard Band," and "Independent." In "In-band" operation, resource blocks existing within the LTE carrier are used. There are specific resource blocks reserved for the synchronization of LTE signals that are not used for NB-IoT. In "guard band" operation, resource blocks between LTE carriers that are not utilized by any operator are used. In "standalone" operation, LTE bands that are used or may not be used are used, such as GSM frequencies. Release 13 includes significant improvements such as discontinuous reception (eDRX) and power saving mode. Power Save Mode (PSM) in Release 12 is replaced by eDRX for devices that need to receive data more frequently and ensure battery life.

[0005] Furthermore, one or more of the smart devices / user devices and similar communication techniques disclosed above provide one or more communication services to users, wherein "smart computing device or UE (user equipment) or user device" refers to any electrical, electronic, electromechanical computing device or equipment or a combination of one or more of the above devices. Also, "smartphone" is a type of "smart computing device" referring to a mobile wireless cellular connected device that allows end users to use services via 2G, 3G, 4G, 5G and / or similar mobile broadband internet connections with an improved mobile operating system that combines features of a personal computer operating system with other features useful for mobile or portable use. Furthermore, smartphones are examples of smart mobility wireless cellular connected devices that allow end users to use services via 2G, 3G, 4G, 5G and / or similar mobile broadband internet connections through Wi-Fi, NFC, Bluetooth, Zigbee, etc., combined with an improved mobile operating system that combines the features of a personal computer operating system with other features useful for mobile or portable use. Moreover, recently, Wi-Fi techniques based on IEEE 802.11 standards have shown rapid growth and commercialization within the unlicensed wireless spectrum. Almost all available user equipment (UE) supporting cellular capabilities inherently possesses Wi-Fi capability to connect to Wi-Fi networks operating in either of the unlicensed frequency bands, namely 2.4 GHz or 5 GHz.

[0006] Furthermore, within a typical telecommunications network, there are multiple wireless and wired nodes hosting one or more telecommunications functions. Additionally, some of these nodes are deployed in data centers to provide centralized services, while others are distributed across geographical regions to provide telecommunications coverage. All of these nodes work together to provide seamless and reliable telecommunications services to users / customers through their smart computing device(s).

[0007] To detect potential issues that could impede seamless and reliable services provided to users, telecommunications service providers need to reliably monitor all such nodes hosting one or more telecommunications functions. Most telecommunications service providers / vendors rely on specific Key Performance Indicators (KPIs) related to one or more nodes or groups of nodes to implement monitoring mechanisms at various tier levels. Using the monitored KPIs, telecommunications service providers / vendors can implement optimization routines on corresponding nodes within a tier, estimate capacity requirements, prepare dashboards for business insights and capacity utilization, and proactively detect issues regarding the functions of those corresponding nodes. Additionally, each KPI consists of one or more counters / parameters generated by the node's monitoring module within a time slice and belongs to a specific tier, where the tier consists of one or more similar or dissimilar nodes. In addition, each KPI can be computed for multiple time intervals, such as quarterly, hourly, daily, weekly, monthly, or custom time ranges.

[0008] Furthermore, since conventional telecommunications networks consist of a large number of nodes that sequentially expose several counters or parameters, there exist a large number of KPIs that need to be computed across various hierarchical levels and time intervals. Therefore, these computations require efficient methods and systems capable of reliably and rapidly computing the desired KPIs with minimal resource usage. Most currently known computation solutions involve inefficient raw computation approaches. More specifically, due to the absence of modular computation approaches, in currently known solutions, hierarchical computation of various KPIs across various time intervals must be performed on the corresponding raw counters / parameters available at the nodes for various time slices within the time interval of the KPI definition. Such computation based on raw parameters can potentially become a large-scale computational task in the following scenarios:

[0009] · When there are thousands of corresponding raw counters / parameters available in small time slices for each node.

[0010] · When there are millions of nodes that need to perform KPI computation.

[0011] · When thousands of KPIs need to be computed for each node.

[0012] · When KPIs need to be computed at multiple layers of the hierarchical level.

[0013] · When KPIs need to be computed for multiple hierarchical levels.

[0014] · When KPIs need to be computed over multiple time intervals.

[0015] Furthermore, such large-scale computation requires massive resources, and due to this scale, the computation can sometimes be unreliable. Moreover, to address issues related to the computation of KPIs, some currently known solutions also provide solutions for presenting service-monitoring dashboards using key performance indicators derived from machine data, where one or more processing devices result in the display of a service monitoring dashboard containing one or more key performance indicator widgets. These currently known solutions fail to provide any solution for modular computation to compute KPIs at various hierarchical levels and time intervals. Additionally, currently known solutions are associated with many other scaling and reliability issues. Therefore, considering the scaling and reliability issues of currently known solutions, the limitations of currently known raw computation approaches, and such other limitations of currently known solutions, it is necessary to form a modular approach to reliably and efficiently compute KPIs at scale.

[0016] Therefore, it is necessary in the relevant technology to provide a modular computation solution for providing an efficient and effective system and method for the hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval that includes at least one time slice.

[0017] The foregoing examples of the relevant technology and related limitations are intended to be illustrative only and not exclusive. Other limitations of the relevant technology will be apparent to those skilled in the art by reading the specification and studying the drawings.

[0018] These sections are provided to briefly introduce specific purposes and aspects of the disclosure that are further described below in the detailed description. This overview is not intended to identify the core features or scope of the claimed subject matter.

[0019] To overcome at least some of the disadvantages mentioned in the preceding section and disadvantages otherwise known to those skilled in the art, the object of the present disclosure is to provide a new solution that provides a modular computation method and system for computing one or more KPIs across various layer levels and time intervals. Another object of the present invention is to provide a method and system for hierarchical computation of one or more Key Performance Indicators (KPIs) of one or more control center layers of a telecommunications network for a target time interval comprising at least one time slice. Additionally, the object of the present disclosure is to define a metric in a time slice for each telecommunications node existing in one or more control center layers based on one or more counters / parameters generated by a monitoring module of each telecommunications node in the time slice. Additionally, the object of the present invention is to provide a local aggregation expression for aggregating one or more counters / parameters mapped to a metric within a time slice in which a dataset comprising one or more counters / parameters is received from a node. Additionally, an object of the present invention is to provide a time aggregation expression for aggregating locally aggregated values ​​across multiple time slices included in a target time interval, such as time interval T. Another object of the present disclosure is to provide a scalable, easy-to-use, seamless, and efficient method for computing one or more KPIs to further estimate capacity requirements, prepare dashboards for business insights and capacity utilization, and implement optimization routines in the nodes to proactively detect problems in the functionality of the nodes corresponding to the hierarchy across various hierarchy levels and time intervals.Furthermore, the object of the present disclosure is to provide an efficient and effective novel mechanism for computing hierarchical KPI(s) as expressions of hierarchically aggregated metrics over time intervals. Another object of the present disclosure is to provide a scalable method for managing and computing hierarchical KPI(s). Additionally, the object of the present disclosure is to provide a novel method for reliably and rapidly computing desired KPI(s) with minimal use of resources. Additionally, the object of the present disclosure is to provide a service that provides features and capabilities for receiving seamless KPI computation services simultaneously or sequentially and, as a result, correcting desired parameters without any latency. Another object of the present disclosure is to provide features and capabilities for processing a large volume of KPIs simultaneously in a telecommunications ecosystem. Yet another object of the present invention is to provide a mechanism that provides a seamless method for rapidly computing desired KPIs of nodes / UEs, regardless of whether the nodes / UEs are 6G / 5G / 4G / 3G / EV-Do / eHRPD-enabled techniques.

[0020] Furthermore, to achieve the aforementioned objectives, the present invention provides a method and system for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice.

[0021] The first object of the present invention relates to a method for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval comprising at least one time slice. The method comprises the step of receiving a dataset corresponding to at least one time slice from one or more nodes existing in one or more control center layers of a telecommunications network at a transceiver unit, wherein the dataset received from each of the one or more nodes comprises one or more counters associated with each node. The method further comprises the step of defining one or more metrics for each node based on one or more counters associated with each node in each time slice by a processing unit in each of the at least one time slices. The method then comprises the step of computing a locally aggregated value of the one or more metrics defined for each node by a local scheduler unit in each time slice. Additionally, the method includes the step of computing the time-aggregated values ​​of one or more metrics defined for each node based on the aggregation of locally aggregated values ​​of one or more metrics defined for each node across all time slices existing in the target time interval, by a metric scheduler unit for the target time interval. The method further includes the step of mapping the time-aggregated values ​​of each metric of one or more metrics defined for each node to a control center layer corresponding to each node, by a processing unit.Additionally, the method includes the step of, by a processing unit, calculating a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer, based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node. The method then includes the step of, by a KPI scheduler unit, computing one or more KPIs for the control center layer based on at least one KIP expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer corresponding to each node, and the hierarchically aggregated value for each metric existing in the target list of metrics.

[0022] Another object of the present invention relates to a system for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval comprising at least one time slice. The system comprises a transceiver unit configured to receive a dataset corresponding to at least one time slice from one or more nodes existing in one or more control center layers of a telecommunications network, wherein the dataset received from each of the one or more nodes comprises one or more counters associated with each node. The system further comprises a processing unit configured to define one or more metrics for each node based on one or more counters associated with each node in each time slice of at least one time slice. Additionally, the system comprises a local scheduler unit configured to compute locally aggregated values ​​of one or more metrics defined for each node in each time slice. The system includes a metric scheduler unit configured to compute the time-aggregated values ​​of one or more metrics defined for each node, based on the aggregation of locally aggregated values ​​of one or more metrics defined for each node across all time slices existing in the target time interval, for the target time interval. Additionally, a processing unit is configured to map the time-aggregated values ​​of each metric of one or more metrics defined for each node to the control center layer corresponding to each node.Additionally, the processing unit is further configured to calculate a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer, based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node. Additionally, the system includes a KPI scheduler unit configured to compute one or more KPIs for the control center layer based on at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer corresponding to each node, and the hierarchically aggregated value for each metric existing in the target list of metrics. Brief explanation of the drawing

[0023] The attached drawings, which are incorporated herein and constitute part of the disclosure, illustrate exemplary embodiments of the disclosed methods and systems, in which similar reference numbers refer to the same parts throughout the drawings. The components in the drawings are not necessarily actual scale; instead, emphasis is made to clearly illustrate the principles of the disclosure. Some drawings may use block diagrams to represent components and may not show the internal circuitry of each component. It will be recognized by those skilled in the art that the disclosures in such drawings include electrical components, electronic components, or circuits commonly used to implement such components. FIG. 1 illustrates an exemplary telecommunications network according to exemplary embodiments of the present invention. FIG. 2 illustrates an exemplary block diagram of a system (200) for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, according to exemplary embodiments of the present invention. FIG. 3 illustrates an exemplary method flowchart (300) for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, according to exemplary embodiments of the present invention. FIG. 4 (i.e., FIG. 4a and FIG. 4b) illustrates an exemplary process for depicting a hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, according to exemplary embodiments of the present invention. The foregoing description will become more apparent from the following more detailed description of the present disclosure. Specific details for implementing the invention

[0024] In the following description, various specific details are described for the purposes of explanation to provide a complete understanding of the embodiments of the present disclosure. However, it will be apparent that the embodiments of the present disclosure can be practiced without these specific details. Each of the features described below may be used independently of one another or in any combination of other features. An individual feature may not solve all of the problems discussed above, or may solve only some of the problems discussed above. Some of the problems discussed above may not be completely solved by any of the features described herein.

[0025] The following description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of exemplary embodiments will provide those skilled in the art with possible descriptions for implementing the exemplary embodiments. It should be understood that various changes may be made to the function and arrangement of the elements without departing from the spirit and scope of the invention as described.

[0026] It is also noted that individual embodiments may be described as processes depicted as flowcharts, flow diagrams, sequence diagrams, data flow diagrams, structure diagrams, or block diagrams. While flowcharts may describe operations as sequential processes, most operations may be performed in parallel or simultaneously. Additionally, the order of operations may be rearranged. A process terminates when its operations are completed, but may have additional steps not included in the drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, the termination of the process may correspond to the return of that function to the calling function or the main function.

[0027] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented by software, firmware, middleware, or microcode, program code or code segments for performing necessary tasks (e.g., computer-program products) may be stored on a machine-readable medium. Processor(s) may perform the necessary tasks.

[0028] The terms “machine-readable storage medium” or “computer-readable storage medium” include, but are not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, storing, or carrying instruction(s) and / or data. A machine-readable medium may include a non-transient medium in which data can be stored but which does not include transient electronic signals and / or carriers that propagate wirelessly or through wired connections. Examples of non-transient media may include, but are not limited to, magnetic disks or tapes, optical storage media such as CDs (compact disks) or DVDs (digital versatile disks), flash memory, memory, or memory devices. A computer-program product may include code and / or machine-executable instructions that can represent any combination of procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, classes, or instructions, data structures, or program statements. A code segment may be coupled to another code segment or hardware circuit by transmitting and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be transmitted, forwarded, or sent through any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0029] The words “exemplary” and / or “exemplary” are used herein to mean functioning as an example, an example, or an illustration. To avoid any doubt, the subject matter disclosed herein is not limited by such examples. Additionally, any aspect or design described herein as “exemplary” and / or “exemplary” is not to be interpreted as being desirable or advantageous over other aspects or designs, nor is it intended to exclude equivalent exemplary structures and techniques known to those skilled in the art. Furthermore, insofar as the terms “to provide,” “to have,” “to keep,” and other similar words are used in the detailed description or claims, such terms are intended to be inclusive (in a manner similar to the term “comprising” as an open-ended transitional word) without excluding any additional or other elements.

[0030] Throughout this specification, references to “one embodiment” or “an embodiment” mean that a specific feature, structure, or characteristic described in relation to the embodiment is included in at least one embodiment of the present invention. Accordingly, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] The technical terms used herein are intended for the purpose of describing only specific embodiments and are not intended to limit the invention. As used herein, singular forms are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprising” and / or “comprising,” as used herein, specify the presence of the mentioned features, integers, steps, actions, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, actions, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated enumerated items.

[0032] The terms "first," "second," "primary," and "secondary" are used to distinguish one element, set, data, object, stage, process, function, activity, or thing from another, and are not used to specify a relative position, or an arrangement in terms of time or relative importance, unless explicitly stated otherwise. Each of the terms used herein, “coupled,” “coupled to,” and “coupled with,” comprises: (a) a connection, whether direct or through one or more other devices, devices, files, circuits, elements, functions, operations, processes, programs, media, components, networks, systems, subsystems, or means; (b) a communication relationship, whether direct or through one or more other devices, devices, files, circuits, elements, functions, operations, processes, programs, media, components, networks, systems, subsystems, or means; and / or (c) two or more devices, devices, files, circuits, elements, functions, operations, processes, programs, media, components, networks, systems, subsystems, or means constituting any one or more functional relationships in which any one or more of these depend wholly or partially on the operation of any one or more of them. It refers to the relationships between elements, functions, actions, processes, programs, media, components, networks, systems, subsystems, and / or means.

[0033] As used herein, the terms “to communicate” and “communicating” include both carrying data from a source to a destination and transmitting data to a communication medium, system, channel, network, device, wire, cable, fiber, circuit and / or link so as to be carried to a destination, and as used herein, the term “communication” means that data is carried or transmitted in this way. As used herein, the term “communications” includes one or more of a communication medium, system, channel, network, device, wire, cable, fiber, circuit and link.

[0034] Furthermore, terms such as "UE (user equipment)," "electronic device," "mobile station," "user device," "mobile subscriber station," "access terminal," "terminal," "smartphone," "smart computing device," "smart device," "device," "handset," and similar technical terms refer to any electrical, electronic, electromechanical equipment, or a combination of one or more of the above devices. Smart computing devices may include voice and non-voice capable devices, such as mobile phones, smartphones, VR (virtual reality) devices, AR (augmented reality) devices, pagers, laptops, general-purpose computers, desktops, personal digital assistants, tablet computers, mainframe computers, smart STBs (set-top boxes), smart speakers, smart fitness bands, smart watches, or any other computing devices that are obvious to those skilled in the art and may be necessary to implement the features of the present invention, but are not limited thereto. Generally, a smart computing device is a digital, user-configurable, computer networking device capable of operating independently. A smart computing device is one of the suitable systems for storing data and other personal / sensitive information. The device can operate at all seven levels of the ISO standard model, but its primary function is related to the application layer, along with the network, session, and presentation layers, which have any additional features such as touch screens, app ecosystems, and physical and biometric security.Additionally, a "smartphone" is a type of "smart computing device" referring to a mobile wireless cellular connected device that allows end users to use services via 2G, 3G, 4G, 5G and / or similar mobile broadband internet connections with an enhanced mobile operating system that combines the features of a personal computer operating system with other features useful for mobile or portable use. Smartphones are camera phones that possess high-speed mobile broadband 4G LTE internet, along with enhanced security features such as video calling, hotspot functionality, motion sensors, mobile payment mechanisms, and alarms and warnings in emergencies, and can access the internet and have a touchscreen user interface and run third-party apps, including the ability to host online applications such as music players. Mobile devices may include smartphones, wearable devices, smartwatches, smart bands, wearable augmentation devices, etc. For specific illustrative purposes, the term "mobile device" in this disclosure will refer to both feature phones and smartphones, but the scope of this disclosure is not limited thereto and may be extended to any mobile device for implementing technical solutions. The above-mentioned smart devices, including smartphones, and feature phones, including IoT and similar devices, enable communication between the devices. Furthermore, the terms described above are used interchangeably in this specification and the associated drawings.

[0035] As used herein, “processor” or “processing unit” comprises one or more processors, wherein a processor refers to any logic circuit for processing instructions. A processor may be a general-purpose processor, a special-purpose processor, a typical processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, a low-end microcontroller, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) circuit, any other type of integrated circuit, etc. Furthermore, as used herein, the term “processor” includes, but is not limited to, one or more computers, hardwired circuits, signal modification devices and systems, devices and machines for controlling systems, central processing units, programmable devices and systems, SoCs (systems on a chip), systems including discrete elements and / or circuits, state machines, virtual machines, data processors, processing facilities, and any combination of the above. A processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the operation of a system in accordance with the present disclosure. More specifically, a processor or processing unit is a hardware processor. As used herein, the term “processor” means processing devices, devices, programs, circuits, components, systems, and subsystems, regardless of whether they are implemented in hardware, tangibly implemented software, or both, and regardless of whether they are programmable.

[0036] As used herein, “memory unit,” “storage unit,” and / or “memory” refers to a machine or computer-readable medium comprising any mechanism for storing information in a form readable by a computer or similar machine. For example, computer-readable media include “ROM (read-only memory),” “RAM (random access memory),” magnetic disk storage media, optical storage media, flash memory devices, or other types of machine-accessible storage media. A memory unit as used herein is configured to store data, whether on the basis of temporary or permanent, and to provide such stored data to the various units to perform the individual functions of the various units.

[0037] As used herein, the "transceiver unit" may include, but is not limited to, a transmitter for transmitting data to one or more destinations and a receiver for receiving data from one or more sources. Additionally, the transceiver unit may include any other similar unit necessary to implement the features of the present invention. The transceiver unit may convert data or information into signals and vice versa for transmission and reception, respectively.

[0038] As disclosed in the background section, existing techniques have many limitations, and to overcome at least some of the limitations of known prior art solutions, the present disclosure provides a novel automated modular computation solution for computing one or more KPIs across various layer levels and time intervals. More specifically, the present invention provides a solution for the hierarchical computation of one or more Key Performance Indicators (KPIs) for one or more control center layers for a target time interval comprising at least one time slice. To compute one or more Key Performance Indicators for one or more control center layers (e.g., one or more layers of type 'H') for a target time interval (e.g., time interval ('T')), the present invention includes defining one or more metrics at the node level. In one implementation, a hierarchy of type 'H' represents a physical module composed of one or more nodes, in another implementation, a hierarchy of type 'H' represents a logical relationship composed of one or more nodes, and in yet another implementation, a hierarchy of type 'H' represents a spatial relationship composed of one or more nodes within a defined geographic boundary. Additionally, each metric from one or more metrics (e.g., a metric ('M') having a unique global value and domain and subdomain classification) is mapped to at least one counter / parameter (e.g., 'C') received from each node (e.g., node ('N')) where each metric is defined.Additionally, the mapping of each metric ('M') to at least one counter / parameter ('C') received from the node where each metric is defined is performed through a local aggregation expression (e.g., LAGG) for aggregating at least one counter / parameter ('C') mapped within a time slice ('TS'), and through a time aggregation expression (e.g., TAGG) defined for aggregating locally aggregated values ​​across multiple time slices belonging to a time interval ('T') (i.e., target time interval). Additionally, through a solution as disclosed by the present invention, hierarchical aggregation is performed for each metric (e.g., metric ('M')) according to a hierarchical expression (e.g., 'HAGG') specific to each metric (e.g., metric ('M')). Additionally, the present invention also includes defining a set of KPIs for a time interval ('T') in one or more layers of type 'H', wherein each KPI of the set of KPIs is associated with a KPI expression defined for each KPI. Each KPI expression consists of one or more hierarchically aggregated metrics. In one implementation, the same KPI definition may be applied to multiple layer types.

[0039] Additionally, the present invention comprises computing one or more metrics defined for each node (e.g., node ('N')) over a target time interval, wherein the computing of each metric (e.g., metric ('M')) is performed by first aggregating at least one counter / parameter (e.g., 'C') mapped according to a local aggregation expression (e.g., 'LAGG') for each time slice ('TS') of the time interval ('T') (i.e., target time interval), and then aggregating the locally aggregated values ​​of at least one counter / parameter ('C') mapped for all time slices of the time interval ('T') according to a time aggregation expression (e.g., 'TAGG'). Furthermore, in one example, the metric calculation in each time slice ('TS') is performed by a periodic scheduled task executed at a slight offset from the end of the corresponding time slice ('TS'). Also, in one implementation, specific filters may be applied before performing local aggregation on at least one mapped counter value within the time slice while computing the metric for the node. Additionally, the invention includes computing one or more KPIs for one or more control center layers (i.e., one or more layers of type 'H') by, for example, computing a Key Performance Indicator (KPI) for one layer 'H1' among at least one layer of type 'H'. In one implementation, one or more attributes of the node are used to assign the node to a specific layer, for example, 'H1' of type 'H'.Additionally, the computation of KPIs for layer 'H1' is first performed by hierarchically aggregating each metric of one or more metrics associated with the corresponding KPI expression of the KPI, wherein the hierarchical aggregation is performed according to a hierarchical expression (e.g., 'HAGG') corresponding to each metric of one or more metrics associated with the corresponding KPI mathematical expression, and the hierarchical aggregation is performed over the metric values ​​of all nodes belonging to layer 'H1'. Furthermore, once the hierarchically aggregated values ​​of one or more metrics for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers are determined, the present invention includes then assigning the hierarchically aggregated values ​​of one or more metrics to one or more corresponding KPI expressions of the one or more KPIs. After such assignment, one or more KPI expressions are evaluated to obtain the computed values ​​of the one or more corresponding KPIs.

[0040] Therefore, the present invention provides a novel automated modular computation solution for computing one or more KPIs across various hierarchical levels and time intervals. Furthermore, the present invention provides a technical effect by providing a solution for the hierarchical computation of one or more Key Performance Indicators (KPIs) across one or more control center hierarchies for a target time interval comprising at least one time slice. The present invention also provides a technical advancement over known prior art solutions by at least one of reducing large-scale computations requiring large resources, providing a more reliable solution, and providing a solution for the modular computation of KPIs across various hierarchical levels and time intervals. Additionally, the present invention provides a technical advancement over known prior art solutions by eliminating the limitations of raw computation approaches.

[0041] Thereafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily practice the present disclosure.

[0042] Referring to FIG. 1, an exemplary telecommunications network according to exemplary embodiments of the present invention is illustrated. More specifically, FIG. 1 illustrates a telecommunications network depicting a plurality of RAN (radio access network) nodes (eNodeB), namely 101A, 101B, 102A, 102B, 102C, 103A, 103B, 103C, and 103D, wherein the plurality of RAN (radio access networks) are distributed within a geographical area to provide telecommunications coverage. Additionally, all eNodeBs of FIG. 1 are arranged in a spatial hierarchy called CC (Control Center), and three hierarchys of control centers, namely 1 (i.e., CC1), 2 (i.e., CC2), and 3 (i.e., CC3), are depicted in FIG. 1, wherein CC1 consists of eNodeBs (101A and 101B), CC2 consists of eNodeBs (102A, 102B, and 102C), and CC3 consists of eNodeBs (103A, 103B, 103C, and 103D). Additionally, FIG. 1 depicts all eNodeBs being connected to a server unit (104), the server unit (104) being configured to collect one or more counters / parameters from each eNodeB in real time or at periodic time intervals, and to compute one or more KPIs at the control center layer level for layers (CC1, CC2 and CC3) according to one or more KPI definitions, and the computation of one or more KPIs is based on the configuration of the system (200) in the server unit (104) according to the implementation of the features of the present invention.

[0043] Referring to FIG. 2, an exemplary block diagram of a system (200) for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice is illustrated. The system (200) includes at least one transceiver unit (202), at least one processing unit (204), at least one local scheduler unit (206), at least one metric scheduler unit (208), at least one KPI scheduler unit (210), and at least one storage unit (not illustrated in FIG. 2). Additionally, all components / units of the system (200) are assumed to be connected to one another unless otherwise noted below. Also, although only a few units are illustrated in FIG. 2, the system (200) may include a plurality of such units, or any number of such units as necessary for the system (200) to implement the features of the present invention. Also, in one implementation, the system (200) may exist on a server device to implement the features of the present invention.

[0044] The system (200) is configured to hierarchically compute one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice with the help of interconnections between the components / units of the system (200).

[0045] A transceiver unit (202) of a system (200) is configured to receive a dataset corresponding to at least one time slice from one or more nodes (e.g., one or more eNodes) existing in one or more control center layers of a telecommunications network, wherein the dataset received from each of the one or more nodes includes one or more counters associated with each node. For example, Table 1 provided below provides an exemplary dataset received from an eNodeB corresponding to a time slice ('TS'), wherein the exemplary dataset consists of four records for counters / parameters (P1, P2, P3 and P4).

[0046]

[0047] Table 1

[0048] Additionally, the transceiver unit (202) is configured to provide the processing unit (204) with a dataset corresponding to at least one time slice (i.e., a dataset received from one or more nodes). Subsequently, the processing unit (204) is configured to define one or more metrics for each of the one or more nodes based on one or more counters associated with each node in each time slice of at least one time slice. Additionally, each metric of the one or more metrics defined for each node is mapped to one or more counters received from each node, and each metric of the one or more metrics is associated with a corresponding local aggregation expression and a corresponding time aggregation expression. For example, Table 2 provided below describes exemplary metrics (M1, M2, M3, and M4) defined for an eNodeB in each time slice (TS) based on a dataset received from one of one or more eNodeBs (i.e., nodes), wherein each metric of the metrics (M1, M2, M3, and M4) is mapped to a parameter / counter received from the eNodeB and has a local aggregation expression and a time aggregation expression defined for it. For example, Table 2 describes a metric (M1) defined for an eNodeB being mapped to a parameter (P1) present in the dataset received from the eNodeB, where the local / local aggregation is defined as SUM and the time aggregation is defined as MAX. In addition, Table 2 describes how a metric (M2) defined for an eNodeB is mapped to a parameter (P2) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as MAX and the temporal aggregation is defined as MAX.Additionally, Table 2 describes a metric (M3) defined for an eNodeB that maps to a parameter (P3) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as SUM and the time aggregation is defined as SUM. Furthermore, Table 2 also describes a metric (M4) defined for an eNodeB that maps to a parameter (P4) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as AVG and the time aggregation is defined as MAX.

[0049]

[0050] Table 2

[0051] Additionally, the processing unit (204) is configured to provide the local scheduler unit (206) with one or more metrics defined in each time slice for each node of one or more nodes existing in one or more control center layers. Then, the local scheduler unit (206) is configured to compute locally aggregated values ​​of one or more metrics defined for each node in each time slice for each time slice of at least one time slice. The locally aggregated values ​​of one or more metrics defined for each node in each time slice are computed based on the aggregation of one or more mapped counters of each metric according to the corresponding local aggregation expression. For example, for the metrics disclosed in Table 2, i.e., for M1, M2, M3, and M4 defined for the eNodeB in each time slice (TS), the local scheduler unit (206) is configured to compute locally aggregated values ​​of M1, M2, M3, and M4 for each time slice (TS). Additionally, locally aggregated values ​​of M1, M2, M3, and M4 are computed according to the corresponding local aggregation expressions (i.e., SUM, MAX, SUM, and AVG, respectively) for each of M1, M2, M3, and M4 by performing local aggregation on multiple records of corresponding mapped parameters / counters (i.e., multiple records of P1, P2, P3, and P4 for each of M1, M2, M3, and M4), wherein each of the multiple records exists in the dataset received from the eNodeB for the time slice (TS).

[0052] Additionally, once locally aggregated values ​​of one or more metrics defined for each node in each time slice are computed, the processing unit (204) is configured to store the computed values ​​in a storage unit. Therefore, locally aggregated metric values ​​for each node in each time slice are stored in a database, i.e., a storage unit.

[0053] The metric scheduler unit (208) is further configured to receive from the local scheduler unit (206) or storage unit the computed locally aggregated value of one or more metrics defined for each node in each time slice, where computation is performed for each time slice. Afterward, the metric scheduler unit (208) is configured to compute the time aggregated value of one or more metrics defined for each node based on the aggregation of the locally aggregated value of one or more metrics defined for each node across all time slices existing in the target time interval for the target time interval. The time aggregated value of one or more metrics defined for each node is based on the corresponding time aggregation expression. Additionally, once the time aggregated value of one or more metrics defined for each node in the target time interval is computed, the processing unit (204) is configured to store the computed value in the storage unit. Therefore, the time-aggregated metric values ​​for each of the one or more nodes in the target time interval are also stored in a database, i.e., a storage unit. For example, for the metrics disclosed in Table 2, i.e., for each metric (M1, M2, M3, and M4) defined for eNodeB, the metric scheduler unit (208) is configured to compute the aggregated values ​​for the target time interval (e.g., time interval (T)) by first reading the locally aggregated values ​​of the corresponding nodes, i.e., eNodeB, corresponding to each of the metrics (M1, M2, M3, and M4) for each time slice forming part of the time interval (T).After the read operation, for each metric, the aggregation of locally aggregated values ​​of all time slices of the time interval (T) is executed by the metric scheduler unit (208) according to the time aggregation expression defined for each metric to reach the final metric value (i.e., time aggregated value) for each of the defined metrics (M1, M2, M3 and M4) that are stored again in the database for the corresponding node, i.e., eNodeB and the time interval (T).

[0054] After that, the processing unit (204) is configured to map the time-aggregated values ​​of each metric of one or more metrics defined for each node to the control center layer corresponding to each node. For example, if the time-aggregated values ​​for each of the M1, M2, and M3 metrics are calculated for each of the three eNodeBs (A, B, and C) respectively (each of A, B, and C exists in the control center layer (CC1, CC2, and CC3), then in such a scenario, the processing unit (204) is further configured to map the time-aggregated values ​​calculated for each of the M1, M2, and M3 metrics to the corresponding control center layers, namely CC1, CC2, and CC3, respectively.

[0055] Additionally, the processing unit (204) is further configured to define at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers for a target time interval, wherein each KPI expression associated with one or more control center layers includes at least one metric associated with one or more control center layers. For example, if two metrics (M1 and M2) are defined for a control center layer, i.e., a node of CCH1, i.e., Node A, then at a set moment, the processing unit (204) is configured to define at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to a control center layer (CCH1) for a target time interval, wherein each KPI expression associated with CCH1 includes at least one metric associated with CCH1, i.e., M1 and M2. For example, at a set moment for a target time interval (T), the KPI expression for one or more KPIs corresponding to CCH1 may be M1+M2. Additionally, the processing unit (204) is further configured to determine a list of metrics and a hierarchical aggregation expression corresponding to each metric in the list of metrics based on at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers. For example, in the above case where the KPI expression for one or more KPIs corresponding to CCH1 for a target time interval (T) is defined as M1+M2, the processing unit (204) is further configured to determine a list of metrics and a hierarchical aggregation expression corresponding to each metric in the list of metrics based on the KPI expression M1+M2 at a set moment.Therefore, in a given case, the list of metrics includes M1 and M2, the hierarchical aggregation expression for metric (M1) may be AVG, and the hierarchical aggregation expression for metric (M2) may be SUM.

[0056] Additionally, the processing unit (204) is further configured to calculate a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer, based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node. The target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer may be identified from a list of metrics determined based on at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers. Additionally, in one implementation, the processing unit (204) is further configured to calculate a hierarchically aggregated value for the control center layer based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node. In addition, the aggregation of all time-aggregated values ​​of each metric in the target list of metrics across all nodes in the control center layer at a target time interval is performed according to a hierarchical aggregation expression corresponding to each metric in the target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer. In one example, the list of metrics may include a metric (M1) corresponding to node 1 of the control center layer (CCH1), a metric (M2) corresponding to node 2 of the control center layer (CCH2), and metrics (M3 and M4) corresponding to node 3 of the control center layer (CCH3).Additionally, the list of metrics is determined based on a KPI expression defined for a Key Performance Indicator (KPI) corresponding to the control center layers, namely CCH1, CCH2, and CCH3. In a given case, a target list of metrics associated with a KPI expression defined for a KPI corresponding to a control center layer (CCH3) can be identified from the list of metrics determined based on a KPI expression defined for a Key Performance Indicator (KPI) corresponding to CCH1, CCH2, and CCH3, and in a given example, the target list of metrics may include metrics (M3 and M4). Subsequently, the processing unit (204) is configured to calculate a hierarchically aggregated value for each metric present in the target list of metrics (i.e., for each of M3 and M4) based on the aggregation of all time aggregated values ​​of M3 and M4 at a target time interval across all nodes present in the control center layer (CCH3). Additionally, in a given example, in one implementation, the processing unit (204) is further configured to calculate a hierarchically aggregated value for the control center layer (CCH3) based on the aggregation of all time aggregated values ​​of M3 and M4 respectively in a target time interval across all nodes existing in the control center layer (CCH3). Furthermore, the aggregation of all time aggregated values ​​of M3 and M4 respectively in a target time interval across all nodes existing in the control center layer (CCH3) is performed according to a hierarchical aggregation expression corresponding to each of M3 and M4 existing in a target list of metrics associated with a KPI expression defined for the KPIs corresponding to the control center layer (CCH3).

[0057] Additionally, Table 3 provided below describes exemplary KPI expressions defined for exemplary KPIs for exemplary control center hierarchies with a time interval (T). Furthermore, Table 3 describes hierarchical aggregation expressions corresponding to metrics identified based on the defined KPI expressions, and the hierarchy types corresponding to each exemplary KPI:

[0058]

[0059] Table 3

[0060] Therefore, Table 3 provides definitions of hierarchical KPIs for time intervals (T) at the control center hierarchy level, where each KPI is based on a corresponding KPI expression of one or more hierarchically aggregated metrics at the control center hierarchy level according to a hierarchical aggregation expression specified for each metric corresponding to each KPI expression. For example, the KPI expression for KPI1 is defined as the sum of the hierarchically aggregated values ​​of the metrics (M1 and M2), where M1 is hierarchically aggregated using a SUM expression, while M2 is hierarchically aggregated using a MAX expression. Additionally, the KPI expression for KPI2 is defined as the MAX of the hierarchically aggregated values ​​of the metrics (M3 and M1), where M1 is hierarchically aggregated using a SUM expression, while M3 is hierarchically aggregated using an AVG expression.

[0061] Additionally, once a hierarchically aggregated value for each metric existing in the target list of metrics is calculated, the processing unit (204) is configured to provide the calculated value to the KPI scheduler unit (210). Subsequently, the KPI scheduler unit (210) is configured to compute one or more KPIs for the control center layer based on at least one KIP expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer corresponding to each node and the hierarchically aggregated value for each metric existing in the target list of metrics. More specifically, one or more KPIs for the control center layer corresponding to each node are computed by substituting the hierarchically aggregated value for the metric(s) existing in the target list of metrics into the corresponding at least one KIP expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer. In one example, to compute two KPIs (KPI1 and KPI2) in a time interval (T) for control center layers (CC1, CC2 and CC3), a KPI scheduler unit (210) is configured to evaluate one or more KPI expressions defined for one or more KPIs corresponding to the control center layers (CC1, CC2 and CC3) by substituting hierarchically aggregated metric values ​​into one or more KPI expressions, wherein the hierarchically aggregated metric values ​​are determined based on the hierarchical aggregation of each metric according to the corresponding hierarchical aggregation expression.

[0062] Referring to FIG. 3, an exemplary method flowchart (300) for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, according to exemplary embodiments of the present invention, is illustrated. In one embodiment, the method is performed by a system (200). Also, in one embodiment, the system (200) may exist on a server device to implement the features of the present invention. Also, as illustrated in FIG. 3, the method starts at step (302).

[0063] In step (304), the method comprises receiving a dataset corresponding to at least one time slice from one or more nodes existing in one or more control center layers of a remote communication network at a transceiver unit (202), wherein the dataset received from each of the one or more nodes includes one or more counters associated with each node.

[0064] For example, Table 4 provided below provides an exemplary dataset received from an eNodeB corresponding to a time slice ('T'), wherein the exemplary dataset consists of four records for counters (C1, C2, C3 and C4).

[0065]

[0066] Table 4

[0067] Additionally, the method includes the step of providing a dataset corresponding to at least one time slice (i.e., a dataset received from one or more nodes) to a processing unit (204) through a transceiver unit (202). Next, in step (306), the method includes the step of defining one or more metrics for each node based on one or more counters associated with each node in each time slice by the processing unit (204) in each time slice of at least one time slice. Additionally, each metric of the one or more metrics defined for each node is mapped to one or more counters received from each node, and each metric of the one or more metrics is associated with a corresponding local aggregation expression and a corresponding time aggregation expression. For example, Table 5 provided below describes exemplary metrics (M1, M2, M3, and M4) defined for each eNodeB in each time slice (T1) based on a dataset received from one of one or more eNodeBs, wherein each metric of the metrics (M1, M2, M3, and M4) is mapped to a counter received from the eNodeB and has a local aggregation expression and a time aggregation expression defined for it. For example, Table 5 describes a metric (M1) defined for an eNodeB being mapped to a counter (C1) present in the dataset received from the eNodeB, where the basic / local aggregation is defined as MAX and the time aggregation is defined as SUM. In addition, Table 5 describes how a metric (M2) defined for an eNodeB is mapped to a counter (C2) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as MAX and the time aggregation is defined as MAX.Additionally, Table 5 describes a metric (M3) defined for an eNodeB that maps to a counter (C3) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as SUM and the time aggregation is defined as SUM. Furthermore, Table 5 also describes a metric (M4) defined for an eNodeB that maps to a counter (C4) present in a dataset received from the eNodeB, where the basic / local aggregation is defined as MAX and the time aggregation is defined as AVG.

[0068]

[0069] Table 5

[0070] Additionally, the method includes the step of providing one or more metrics defined in each time slice for each node of one or more nodes existing in one or more control center layers to a local scheduler unit (206) by means of a processing unit (204). Next, in step (308), the method includes the step of computing a locally aggregated value of one or more metrics defined for each node by means of a local scheduler unit (206) for each time slice. Additionally, the locally aggregated value of one or more metrics defined for each node is computed based on the aggregation of one or more mapped counters of each metric according to a corresponding local aggregation expression. For example, for the metrics disclosed in Table 5, that is, for M1, M2, M3, and M4 defined for eNodeB in each time slice (T1), the method includes the step of computing locally aggregated values ​​of M1, M2, M3, and M4 for each time slice (T1) by a local scheduler unit (206). Additionally, locally aggregated values ​​of M1, M2, M3, and M4 are computed according to the corresponding local aggregation expressions (i.e., MAX, MAX, SUM, and MAX, respectively) for each of M1, M2, M3, and M4 by performing local aggregation on multiple records of corresponding mapped parameters / counters (i.e., multiple records of C1, C2, C3, and C4 for each of M1, M2, M3, and M4), wherein each of the multiple records exists in a dataset received from eNodeB for a time slice (T1). Additionally, once locally aggregated values ​​of one or more metrics defined for each node in each time slice are computed, the method includes the step of storing the computed locally aggregated values ​​in a storage unit through a processing unit (204).Therefore, the locally aggregated metric values ​​for each node in each time slice are stored in the database, i.e., the storage unit.

[0071] Then, the method includes the step of receiving, from the local scheduler unit (206) or storage unit, a locally aggregated value of one or more metrics defined for each node in each time slice from the metric scheduler unit (208), wherein computation is performed for each time slice. Next, in step (310), the method includes the step of computing a time aggregated value of one or more metrics defined for each node based on the aggregation of locally aggregated values ​​of one or more metrics defined for each node across all time slices existing in the target time interval by the metric scheduler unit (208) for the target time interval. The time aggregated value of one or more metrics defined for each node is based on a corresponding time aggregation expression.

[0072] Additionally, once locally aggregated values ​​of one or more metrics defined for each node in the target time interval are computed, the method includes the step of storing the computed time aggregated values ​​in a storage unit by the processing unit (204). Therefore, the time aggregated metric values ​​for each node of one or more nodes in the target time interval are also stored in a database, i.e., a storage unit. For example, for the metrics disclosed in Table 5, i.e., for each metric (M1, M2, M3, and M4) defined for eNodeB, the method includes the step of computing the aggregated values ​​for the target time interval (e.g., time interval (T)) by first reading the locally aggregated values ​​of the corresponding eNodeB corresponding to each of the metrics (M1, M2, M3, and M4) for each time slice forming part of the time interval (T) through the metric scheduler unit (208). After the read operation, for each metric, the aggregation of locally aggregated values ​​of all time slices of the time interval (T) is executed by the metric scheduler unit (208) according to the time aggregation expression defined for each metric to reach the final metric value (i.e., the time aggregated value) for each of the defined metrics (M1, M2, M3, and M4) that are stored again in the database for the corresponding eNodeB and the time interval (T).

[0073] Then, in step (312), the method includes the step of mapping the time-aggregated value of each of the one or more metrics defined for each node to the control center layer corresponding to each node by the processing unit (204). For example, if the time-aggregated value for each of the M1, M2, and M3 metrics is calculated for each of the three eNodeBs (B, C, and D) respectively (each of B, C, and D exists in the control center layer (CC1, CC2, and CC3), then in such a scenario, the method includes the step of mapping the time-aggregated value calculated for each of the M1, M2, and M3 metrics by the processing unit (204) to the corresponding control center layer, i.e., CC1, CC2, and CC3, respectively.

[0074] Additionally, the method includes the step of defining at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers by a processing unit (204) for a target time interval, wherein each KPI expression associated with one or more control center layers includes at least one metric associated with one or more control center layers. For example, if two metrics (M1 and M2) are defined for a control center layer, i.e., a node of CC1, i.e., Node 1, at a set moment, the method defines at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to a control center layer (CC1) through the processing unit (204) for a target time interval, wherein each KPI expression associated with CC1 includes at least one metric associated with CC1, i.e., M1 and M2. For example, at a set moment for a target time interval (T), the KPI expression for one or more KPIs corresponding to CC1 may be MAX(M1, M2). Additionally, thereafter, the method includes the step of determining a list of metrics and a hierarchical aggregation expression corresponding to each metric in the list of metrics based on at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers by the processing unit (204). For example, in the above case where the KPI expression for one or more KPIs corresponding to CC1 for a target time interval (T) is defined as MAX(M1, M2), at a given moment, the method includes the step of determining a list of metrics and a hierarchical aggregation expression corresponding to each metric in the list of metrics based on the KPI expression MAX(M1, M2) by the processing unit (204).Therefore, in a given case, the list of metrics includes M1 and M2, the hierarchical aggregation expression for metric (M1) may be SUM, and the hierarchical aggregation expression for metric (M2) may be AVG.

[0075] Next, in step (314), the method comprises the step of calculating, by the processing unit (204), a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to a control center layer, based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node. The target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to a control center layer may be identified from a list of metrics determined based on at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers. Additionally, in one implementation, the method also includes the step of calculating a hierarchically aggregated value for the control center layer based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer corresponding to each node by the processing unit (204). Furthermore, the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer is performed according to a hierarchical aggregation expression corresponding to each metric existing in the target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer.In one example, the list of metrics may include a metric (M1) corresponding to node A of the control center layer (CC1), a metric (M2) corresponding to node B of the control center layer (CC2), and metrics (M3 and M4) corresponding to node C of the control center layer (CC3). Additionally, the list of metrics is determined based on a Key Performance Indicator (KPI) expression defined for the Key Performance Indicators (KPIs) corresponding to the control center layers, namely CC1, CC2, and CC3. In a defined case, a target list of metrics associated with a KPI expression defined for the KPI corresponding to the control center layer (CC3) may be identified from the list of metrics determined based on the KPI expression defined for the Key Performance Indicators (KPIs) corresponding to CC1, CC2, and CC3, and in a defined example, the target list of metrics may include metrics (M3 and M4). Thereafter, the method comprises the step of calculating a hierarchically aggregated value for each metric present in the target list of metrics (i.e., for each of M3 and M4) based on the aggregation of all time aggregated values ​​of each of M3 and M4 in the target time interval across all nodes present in the control center layer (CC3) by the processing unit (204). Additionally, in a defined example, in one implementation, the method further comprises the step of calculating a hierarchically aggregated value for the control center layer (CC3) based on the aggregation of all time aggregated values ​​of each of M3 and M4 in the target time interval across all nodes present in the control center layer (CC3) by the processing unit (204).Furthermore, the aggregation of all time aggregated values ​​of M3 and M4 at target time intervals across all nodes existing in the control center layer (CC3) is performed according to a hierarchical aggregation expression corresponding to each of M3 and M4 existing in the target list of metrics associated with the KPI expression defined for the KPIs corresponding to the control center layer (CC3).

[0076] Additionally, Table 6 provided below describes exemplary KPI expressions defined for exemplary KPIs for exemplary control center hierarchies with a time interval (T). Furthermore, Table 6 describes hierarchical aggregation expressions corresponding to metrics identified based on the defined KPI expressions, and the hierarchy types corresponding to each exemplary KPI:

[0077]

[0078] Table 6

[0079] Therefore, Table 6 provides definitions of hierarchical KPIs for time intervals (T) at the control center hierarchy level, where each KPI is based on the corresponding KPI expression of one or more hierarchically aggregated metrics at the control center hierarchy level according to the hierarchical aggregation expression specified for each metric corresponding to each KPI expression. For example, in Table 6, the KPI expression for KPI1 is defined as the MAX of the hierarchically aggregated values ​​of the metrics (M1 and M2), where M1 is hierarchically aggregated using the MAX expression, while M2 is hierarchically aggregated using the SUM expression. Also, in Table 6, the KPI expression for KPI2 is defined as the sum of the hierarchically aggregated values ​​of the metrics (M3 and M1), where M1 is hierarchically aggregated using the AVG expression, while M3 is hierarchically aggregated using the MAX expression.

[0080] Additionally, once a hierarchically aggregated value for each metric existing in the target list of metrics is calculated, the method includes the step of providing the calculated value to the KPI scheduler unit (210) through the processing unit (204). Then, in step (316), the method includes the step of computing one or more KPIs for the control center layer by the KPI scheduler unit (210), based on at least one KIP expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer corresponding to each node and the hierarchically aggregated value for each metric existing in the target list of metrics. More specifically, one or more KPIs for the control center layer corresponding to each node are computed by substituting the hierarchically aggregated value for the metric(s) existing in the target list of metrics into the corresponding at least one KIP expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer. In one example, to compute two KPIs (KPI1 and KPI2) in a time interval (T1) for control center layers (CC1, CC2 and CC3), the method includes the step of evaluating one or more KPI expressions defined for one or more KPIs corresponding to the control center layers (CC1, CC2 and CC3) by substituting hierarchically aggregated metric values ​​into one or more KPI expressions by a KPI scheduler unit (210), wherein the hierarchically aggregated metric values ​​are determined based on the hierarchical aggregation of each metric according to the corresponding hierarchical aggregation expression.

[0081] After hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, the method terminates at step (318).

[0082] Referring to FIG. 4 (i.e., FIG. 4a and FIG. 4b), an exemplary process is illustrated that depicts a hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, according to exemplary embodiments of the present invention. As illustrated in FIG. 4, the process / method begins at step (402).

[0083] Additionally, in step (404), the exemplary process includes the step of reading a dataset for each of the nodes of one or more of the nodes of one or more of the control center layers (e.g., a node among two nodes (N1 and N2)) for each of the time slices of one or more time slices (e.g., time slices (TS1 and TS2)). Also, in FIG. 4, in (800), (801), (802) and (803), exemplary datasets received from two nodes (N1 and N2) for the time slices (TS1 and TS2) are illustrated, wherein each dataset includes multiple records of parameters / counters (P1, P2, P3 and P4) used to define metrics (M1, M2 and M3) for each of the nodes (N1 and N2).

[0084] Next, in step (406), the exemplary process includes the step of computing locally aggregated values ​​of various metrics defined for each node in each time slice, wherein the local aggregation is performed on one or more mapped parameters of each metric according to a local aggregation expression defined for each metric. Also, in FIG. 4, in (804), (805), (806), and (807), exemplary calculations of metrics (M1, M2, and M3) defined for each node (N1 and N2) in each time slice (TS1 and TS2) are illustrated, wherein the local aggregation is performed according to a local aggregation expression defined for M1, M2, and M3. In one example, the local aggregation expressions defined for the metrics (M1, M2, and M3) may be as provided in Table 2 disclosed above.

[0085] Additionally, in step (408), the exemplary process includes the step of storing locally aggregated metrics for each node and time slice in a database.

[0086] Next, in step (410), the exemplary process includes reading locally aggregated metrics from a database for each node and for the entire time slice which is part of a target time interval, such as a time interval (T).

[0087] Additionally, in step (412), the exemplary process includes the step of computing the time-aggregated values ​​of the defined metrics for each node for a time interval (T), wherein the aggregation is performed by aggregating the locally aggregated values ​​of the defined metrics for each node across all time slices belonging to the time interval (T). Additionally, the aggregation is performed according to the time aggregation expression defined for each metric. Also, in FIG. 4, at (808), the exemplary calculation of the metrics (M1, M2 and M3) for each exemplary node (N1, N2 and N3) according to the corresponding time aggregation expression is depicted.

[0088] Next, in step (414), the exemplary process includes the step of mapping the time-aggregated values ​​of each metric for each node to the hierarchy according to the mapping of the nodes to the hierarchy. Additionally, in FIG. 4, in (809), exemplary metrics (M1, M2 and M3) for each node of the nodes (N1, N2 and N3) are illustrated as being mapped to the CC (control center) hierarchy, wherein the exemplary nodes (N1 and N2) belong to the control center hierarchy (CC1) and N3 belongs to the control center hierarchy (CC2).

[0089] Next, in step (416), the exemplary process includes reading the pre-defined KPI definitions of all KPIs and forming a list of metrics of unique tuples and a corresponding hierarchical aggregation expression. Also, in FIG. 4, in (810), an example of such an exemplary list formed using the exemplary KPI definition(s) is illustrated.

[0090] Next, in step (418), the exemplary process describes an iteration of the list formed in step (416), wherein for each metric and for each hierarchical aggregation expression for each hierarchy, the hierarchically aggregated value is calculated by aggregating all time-aggregated values ​​of each metric over a time interval (T) across all nodes of the corresponding hierarchy according to the corresponding hierarchical expression of each metric. Additionally, in FIG. 4, for each corresponding hierarchical aggregation described in (811), (809), and (810), examples of hierarchically calculated values ​​for each of the exemplary metrics (M1, M2, and M3) for each exemplary hierarchy (CC1 and CC2) are illustrated.

[0091] Additionally, at step (420), the exemplary process includes the step of calculating each KPI for each layer by substituting the corresponding calculated hierarchically aggregated values ​​of the metrics into the corresponding KPI expression(s). Also, in FIG. 4, at step (812), an example is illustrated in which the KPIs for layers (CC1 and CC2), namely KPI1 and KPI2, are calculated by substituting the hierarchically aggregated values ​​of M1, M2, and M3 from (811) into the corresponding KPI expressions of KPI1 and KPI2. After that, the exemplary process terminates at step (422).

[0092] Accordingly, the present invention provides a novel automated modular computation solution for computing one or more KPIs across various hierarchical levels and time intervals. Furthermore, the present invention provides a scalable, easy-to-use, seamless, and efficient solution for computing KPIs to estimate capacity requirements, prepare dashboards for business insights and capacity utilization, and implement optimization routines in the nodes to detect problems in advance regarding the functionality of corresponding nodes in the hierarchy across various hierarchical levels and time intervals. Additionally, the present invention provides technical benefits by providing a solution for the hierarchical computation of one or more Key Performance Indicators (KPIs) for one or more control center hierarchies for a target time interval including at least one time slice. The present invention also provides technical advancements over known prior art solutions by at least one of reducing large-scale computations requiring large resources, providing a more reliable solution, and providing a solution for the modular computation of KPIs across various hierarchical levels and time intervals. Furthermore, the present invention also provides a technological advancement over known conventional solutions by eliminating the limitations of primitive computation approaches.

[0093] Although much emphasis has been placed on preferred embodiments herein, it will be recognized that many embodiments may be practiced and that many modifications may be made to the preferred embodiments without departing from the principles of the invention. Such and other modifications in the preferred embodiments of the invention will be obvious to those skilled in the art from the disclosure herein, and thereby it should be clearly understood that the foregoing description is merely illustrative and not limiting to the invention.

Claims

Claim 1 A method for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval comprising at least one time slice, comprising: receiving a dataset corresponding to said at least one time slice from one or more nodes existing in one or more control center layers of a remote communication network at a transceiver unit (202) — said dataset received from each of said nodes comprises one or more counters associated with said node —; defining one or more metrics for said node based on one or more counters associated with said node in said time slice by a processing unit (204) in said time slice of said at least one time slice; computing a locally aggregated value of said one or more metrics defined for said node by a local scheduler unit (206) for said time slice; and locally aggregating of said one or more metrics defined for said node across all time slices existing in said target time interval by a metric scheduler unit (208) for said target time interval Based on the aggregation of aggregated values, a step of computing the time aggregated values ​​of one or more metrics defined for each of the nodes; a step of mapping the time aggregated values ​​of each metric from one or more metrics defined for each node to a control center layer corresponding to each node by the processing unit (204);A method for hierarchical computation of one or more KPIs, comprising: a step of calculating, by the processing unit (204), a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer, based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics over a target time interval across all nodes existing in the control center layer corresponding to each node; and a step of computing one or more KPIs for the control center layer by the KPI scheduler unit (210), based at least on the hierarchically aggregated value for each metric existing in the target list of metrics and the at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to the control center layer corresponding to each node. Claim 2 A method for hierarchical computation of one or more KPIs, wherein each metric from one or more metrics defined for each of the respective nodes is mapped to one or more counters received from each of the respective nodes. Claim 3 A method for hierarchical computation of one or more KPIs, wherein each metric from the one or more metrics is associated with a corresponding local aggregation expression and a corresponding time aggregation expression. Claim 4 A method for hierarchical computation of one or more KPIs, wherein, in claim 3, locally aggregated values ​​of one or more metrics defined for each of the above nodes are computed based on the aggregation of one or more mapped counters of each of the above metrics from the one or more metrics according to a corresponding local aggregation expression. Claim 5 A method for hierarchical computation of one or more KPIs, wherein, in claim 3, the time-aggregated values ​​of one or more metrics defined for each of the above nodes are additionally based on the corresponding time-aggregation expression. Claim 6 A method for hierarchical computation of one or more KPIs, wherein, in claim 1, the method further comprises: a step of defining at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers by the processing unit (204) for the target time interval, wherein each KPI expression associated with one or more control center layers comprises at least one metric associated with one or more control center layers, and a step of determining, by the processing unit (204), a list of metrics and a hierarchical aggregation expression corresponding to each metric present in the list of metrics based on the at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to the one or more control center layers. Claim 7 A method for hierarchical computation of one or more KPIs, wherein the method of calculating the hierarchically aggregated value further comprises calculating the hierarchically aggregated value for the control center layer based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer corresponding to each node by the processing unit (204). Claim 8 A method for hierarchical computation of one or more KPIs, wherein, in claim 1, the aggregation of all time-aggregated values ​​of each of the metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer follows a hierarchical aggregation expression corresponding to each of the metric existing in the target list of metrics associated with at least one KPI expression defined for the one or more KPIs corresponding to the control center layer. Claim 9 A system for hierarchical computation of one or more Key Performance Indicators (KPIs) for a target time interval including at least one time slice, comprising: a transceiver unit (202) configured to receive a dataset corresponding to said at least one time slice from one or more nodes existing in one or more control center layers of a remote communication network — said dataset received from each of said nodes includes one or more counters associated with said node —; a processing unit (204) configured to define one or more metrics for said node based on one or more counters associated with said node in each time slice of said at least one time slice; and a local scheduler unit (206) configured to compute a locally aggregated value of the one or more metrics defined for said node for said time slice.A metric scheduler unit (208) configured to compute a time aggregated value of one or more metrics defined for each node based on an aggregation of locally aggregated values ​​of one or more metrics defined for each node across all time slices existing in the target time interval for the above target time interval ― the processing unit (204) is further configured to map the time aggregated value of each metric from one or more metrics defined for each node to a control center layer corresponding to each node, and to calculate a hierarchically aggregated value for at least each metric existing in a target list of metrics associated with at least one KPI expression defined for one or more KPIs corresponding to the control center layer, based on an aggregation of all time aggregated values ​​of each metric existing in a target list of metrics in the target time interval across all nodes existing in the control center layer corresponding to each node ―; A system for hierarchical computation of one or more KPIs, comprising a KPI scheduler unit (210) configured to compute one or more KPIs for a control center layer based at least one KPI expression defined for each of the above metrics existing in a target list of the above metrics and one or more KPIs corresponding to a control center layer corresponding to each of the above nodes. Claim 10 In claim 9, a system for hierarchical computation of one or more KPIs, wherein each metric from one or more metrics defined for each of the above nodes is mapped to one or more counters received from each of the above nodes. Claim 11 In claim 9, a system for hierarchical computation of one or more KPIs, wherein each metric from the one or more metrics is associated with a corresponding local aggregation expression and a corresponding time aggregation expression. Claim 12 A system for hierarchical computation of one or more KPIs, wherein, in claim 11, locally aggregated values ​​of one or more metrics defined for each of the above nodes are computed based on the aggregation of one or more mapped counters of each of the above metrics from the one or more metrics according to a corresponding local aggregation expression. Claim 13 A system for hierarchical computation of one or more KPIs, wherein, in claim 11, the time-aggregated values ​​of one or more metrics defined for each of the above nodes are additionally based on the corresponding time-aggregation expression. Claim 14 A system for hierarchical computation of one or more KPIs, wherein the processing unit (204) is further configured to: define at least one KPI expression for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers for the target time interval — each KPI expression associated with one or more control center layers includes at least one metric associated with one or more control center layers — and, based on the at least one KPI expression defined for one or more KPIs (Key Performance Indicators) corresponding to one or more control center layers, determine a list of metrics and a hierarchical aggregation expression corresponding to each metric present in the list of metrics. Claim 15 In claim 9, the processing unit (204) is further configured to calculate a hierarchically aggregated value for the control center layer based on the aggregation of all time aggregated values ​​of each metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer corresponding to each node. A system for hierarchical computation of one or more KPIs. Claim 16 A system for hierarchical computation of one or more KPIs, wherein, in claim 9, the aggregation of all time-aggregated values ​​of each of the metric existing in the target list of metrics at a target time interval across all nodes existing in the control center layer follows a hierarchical aggregation expression corresponding to each of the metric existing in the target list of metrics associated with at least one KPI expression defined for the one or more KPIs corresponding to the control center layer.

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