WO2023033815A1

WO2023033815A1 - Semantic mapping for measurement over terabit networks

Info

Publication number: WO2023033815A1
Application number: PCT/US2021/048553
Authority: WO
Inventors: Charif Mahmoudi
Original assignee: Siemens Corporation
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2023-03-09

Abstract

The Internet of things (IoT) generally refers to a network of physical objects (things) that can be embedded with sensors, software, and various technologies so as connect and exchange data with various devices and systems over the Internet. An example of such connected things include devices configured to obtain and communicate various measurement data. Such devices can measure and communicate data in different units or structures. It is recognized herein that communicating such heterogenous and unstructured data across the IoT can limit communication speeds at which the data can be shared. A network can include a plurality of virtual network functions configured to perform measurement unit conversions in a distributed manner, so as to operate at high speeds, for instance speeds suited for terabit networks.

Description

SEMANTIC MAPPING FOR MEASUREMENT OVER TERABIT NETWORKS

BACKGROUND

[0001] The Internet of things (loT) generally refers to a network of physical objects (things) that can be embedded with sensors, software, and various technologies so as connect and exchange data with various devices and systems over the Internet. An example of such connected things include devices configured to obtain and communicate various measurement data. Such devices can measure and communicate data in different units or structures. It is recognized herein that communicating such heterogenous and unstructured data across the loT can limit communication speeds at which the data can be shared.

BRIEF SUMMARY

[0002] Embodiments of the invention address and overcome one or more of the described- herein shortcomings by providing methods, systems, and apparatuses that can stream, process, and access heterogeneous and unstructured data at high speeds, for instance speeds suited for terabit networks (e.g., 1000 Gbs).

[0003] In an example aspect, a network can include a plurality of virtual network functions, for instance hosted on routers, switches, or servers, and a controller. The network can receive a request, from a first computing device, for measurement data. The request can indicate a first measurement unit for the measurement data that is associated with the first computing device. Based on the request, the controller can identify a second computing device for fulfilling the request. The controller can receive the measurement data from the second computing device. The measurement data can define a second measurement unit associated with the second computing device that is different than the first measurement unit. Based on the request and network traffic within the network, the controller can select virtual network functions from the plurality of virtual network functions, so as to define selected virtual network functions. The controller can then route data associated with the request through the selected virtual network functions. Furthermore, at the selected virtual network functions, the measurement data can be converted from the second measurement unit to the first measurement unit, thereby performing measurement conversion in a distributed manner within the network. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0004] The foregoing and other aspects of the present invention are best understood from the following detailed description when read in connection with the accompanying drawings. For the purpose of illustrating the invention, there is shown in the drawings embodiments that are presently preferred, it being understood, however, that the invention is not limited to the specific instrumentalities disclosed. Included in the drawings are the following Figures:

[0005] FIG. 1 is a block diagram of an example communications system defining a plurality of virtual network functions configured to process measurement unit conversions in accordance with an example embodiment.

[0006] FIG. 2 illustrates a computing environment within which embodiments of the disclosure may be implemented.

DETAILED DESCRIPTION

[0007] As an initial matter, it is recognized herein that current approaches to resolving issues related to data heterogeneity typically involve pre-processing data or custom converters. With respect to pre-processing data, generally a data customer first transfers the full dataset before initiating a conversion process to the desired format. With respect to custom converters, generally custom code, for instance custom stream processing (e.g., Apache flink), is designed for specific datasets. It is further recognized herein that these approaches can define significant limitations. For example, the current approaches can be limited in terms of dynamicity, as they may require prior knowledge of the source and the destination formats associated with the measurement data. Furthermore, those approaches can be limited in terms of scalability, for example, by requiring intermediate storage space or processing capabilities for high velocity near-real time data. Further still, it is recognized herein that technical shortcomings related to high-speed transfers, among other things, remain in approaches that might implement semantic descriptions for semantic mapping between devices in building automation and industrial automation systems.

[0008] Such technical shortcomings can be addressed by embodiments described herein. For example, in accordance with various embodiments, a semantic description can be associated with source data and with a data format that is requested. Using the semantic description, a metric representation of measurement units can be built. In some cases, the metric representation is composed according to a standard metric model. For example, the NIST metric model offers a framework to compose the metric description based on parameters, rules, and underlying metrics. In various examples, those metrics are associated with a semantic description, such as the W3C Web of Things standard. Such associations can enable automatic inference of the metric association between different definitions. As used herein, unless otherwise specified, measurement units, measurement data, data format, data structure, and data can be used interchangeably, without limitation. Furthermore, based on the semantic description, a configuration can be generated that is associated with mappers. Mappers generally refer to modules or mechanisms that can leverage heuristics or machine learning to determine the association between metrics, and to determine transformations that can be applied to data flows to convert the data between formats. In various examples, a network function is generated with an associated network configuration, so as to execute a given mapper in a distributed manner. In particular, for example, the mappers can be disseminated as Virtual Network Functions (VNFs) within the network infrastructure, so as to ensure that data flows are forwarded accordingly.

[0009] Thus, as described further herein, various embodiments define a method or system that is agnostic to the data structure of the source. For example, as a data structure is abstracted to determine the measurement units of the data structure, the VNFs can use the semantic description to gain awareness of the data structure by decomposing the metric according to its description. Thus, metric definitions can be reverse engineer to arrive at basic metrics that can be common to various the metrics definitions. For example, in a cloud computing measurement context, the network function virtualization (NFV) can break a definition for the Request- Response Time (RTT) to a basic measurement of Request Time and Response Time. That can enable multiple cloud providers to define their custom calculation formulae (e.g., expression in the NIST metric model) while also enabling a monitor to request a specific definition of the RTT, such that the NF Vs define the transformations and deliver the pre-adapted data. After the data structure is identified, the mapper can transform the source values to values in a target measurement unit.

[0010] Referring to FIG. 1, an example machine-to-machine (M2M), Internet of Things (loT) or Web of Things (WoT) communication system 100 includes a communication network 102 communicatively coupled to one or more computing devices 104. The computing devices 104 may comprise or be embodied in any type of apparatus or device configured to transmit and/or receive wireless signals, including, by way of example and without limitation, a smartphone, a laptop, a tablet, a notebook, a personal computer, a wireless sensor, consumer electronics, a wearable device such as a smart watch or smart clothing, a medical or eHealth device, a robot, industrial equipment, a drone, a vehicle such as a car, truck, train, or airplane, and the like. The communication network 102 may include a plurality of routers 106 and a plurality of switches 108 configured to communicate and process data throughout the system 100.

[0011] The communication network 102 may be a fixed network (e.g., Ethernet, Fiber, ISDN, PLC, or the like) or a wireless network (e.g., WLAN, cellular, or the like), or a network of heterogeneous networks. For example, the communication network 102 may include multiple access networks that provide content such as voice, data, video, messaging, broadcast, or the like to multiple users. The communication network 102 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal (OFDMA), single-carrier FDMA (SC-FDMA), and the like. Furthermore, the communication network 102 may include other networks such as, for example and without limitation, a core network, the Internet, a sensor network, an industrial control network, a personal area network, a satellite network, a home network, or an enterprise network. Thus, it will be understood that the system 100 is illustrated and simplified as an example, such that the system 100 can define other networks and nodes (devices) in alternative configurations, and all such systems are contemplated as being within the scope of this disclosure.

[0012] The communication network 102 can also define a plurality of virtualized network functions (VNFs) 110 hosted within the network 102, for instance on the routers 106 and the switches 108. Thus, the network 102 can provide Network Function Virtualization (NFV) and network programmability capabilities. In particular, the VNFs 110 can define the infrastructure of the network 102 that enables high-speed processing of the data flows through the network 102, as further described herein. The VNFs 110 can implement network functions in software that run on a range of industry standard server hardware, and that can be moved to, or instantiated in, various locations in the network as required, without the need for installation of new equipment. In particular, the communication network 102 can further define one or more control nodes or controllers 112 configured to monitor and control data flows within the network 102, for instance by implementing policy rules. The controllers 112 can define a specific Software-Defined Networking (SDN) application that can interact with network switches using protocols, such as OpenFlow for example. The controllers 112 can implement loadbearing and can manage the workload placement of the NFVs. In some examples, the controller 112 can perform beyond SDN flow control by performing an orchestration operation that enables the dynamic distribution of the data flow to the available NFVs for implementing the data transformation. For Terabit networks, a central processing node can be unrealistic because of a bottleneck effect. Thus, without being bound by theory, the distribution of the NFVs and the control of the data flows can be crucial to ensure that the required transformation bandwidth is available from the system.

[0013] In some examples, the controllers 112 can decompose the mapping between different measurement units into sub-atomic steps. For example, sub-atomic steps can include breaking down the requested metric until reaching the basic definitions, determining or inferring the required transformation by comparing the metric definition’s structure and formulae, instantiating the NFVs, and configuring the flows to hit the NFVs on their path. In particular, the controllers 112 can implement policies in the form of action/match rules, so as to determine which VNFs 110 are adequate for performing specific processing of the data flow.

[0014] With continuing reference to FIG. 1, the network 102 can receive a request from one of the computing devices 104, for instance a first computing device or requester 104a. The request can be for measurement data. In various examples, the request indicates a measurement unit (e.g., first measurement unit) for the requested measurement data. By way of example, and without limitation, the computing device 104a may define a mobile phone that is requesting a temperature reading from a thermostat defined by one of the other computing devices 104, for instance a second computing device 104b. Continuing with the example, the first measurement unit might be Celsius, and the second computing device 104b (e.g., thermostat or sensor) might measure its data in a second measurement unit (e.g., Fahrenheit or Kelvin) that is different than the first measurement unit. It will be understood that although the above-described measurement data and measurement units relates to temperature for purposes of example, measurement data and measurement units can relate to any type of data that defines different units or formats as desired (e.g., metric, English, distance, volume, pressure, speed, acceleration, etc.), and all such measurement data and measurement units are contemplated as being within the scope of this disclosure.

[0015] Still referring to FIG. 1, based on the request, the controller 112 that receives the request for measurement data, for instance a router 106 or switch 108 (or server) that defines the controller 112, can identify a target computing device for fulfilling the request, for instance the second computing device 104b. In some examples, identifying the target computing device can be performed in accordance with a federated approach, for instance an approach defined by the IEEE P2302 federation standard. The resources that are available to instantiate the NFVs can be exposed as part of this federation and can be used by the controllers 112 to orchestrate the NFVs and set up the flows. The controller 112 can route the request and data associated with the request through the network 102 to the second computing device 104b. The second computing device 104b can return the measurement data associated with the request to one of the controllers 112. Thus, the controller 112 can receive the measurement data from the second computing device 104b, and the measurement data can define a second measurement unit associated with the second computing device 104b that is different than the first measurement unit. Based on the request and network traffic within the network 102, the controllers 112 can select virtual network functions from the plurality of virtual network functions 110, so as to define selected virtual network functions. This selection can be based on potential constraints due to the deployment environment. Example constraints include the availability of network acceleration or the preference between Virtual Machines and Containers. Moreover, the controller 112 can choose the specific configuration of the network transformation by associating the mapping logic as an application inside the Virtual Machine or Container representing the NFV. The controllers 112 can then route the data associated with the request through the request through the selected virtual network functions. Continuing with the example, at the selected virtual network functions, the measurement data can be converted from the second measurement unit to the first measurement unit.

[0016] In particular, the VNFs through which the data is routed can, based on a first based on a first semantic description associated with the request, determine that the first computing device requests data structured in accordance with the first measurement unit. Further, based on a second semantic description associated with the measurement data, the selected VNFs can determine that the second computing device provides data structured in accordance with the second measurement unit. In various examples, converting the measurement data at the selected virtual network functions further includes performing a mapping between the second measurement unit and the first measurement unit over a plurality of the selected network functions so as to perform the mapping in a distributed manner. In some cases, the semantic descriptions are generated based on an ISO/IEC standard metric model. The semantic description might be available via efforts such as the W3C Web of Things or via a manufacturers description and converted to the ISO/IEC standard for integration in the system. Those definitions can be integrated into the data store at the controller level.

[0017] An example internal view of the controllers 112 can include the semantic description being part of a transformation request. The transformation request can be transformed to active mapping NFVs and SDN flows setup. The sematic description can be given to the system as input, and the system can store the information in a local datastore and trigger the generation of the source. The transformation NFVs can be identified, instantiated, and configured accordingly to meet the performance requirements. In various examples, the process assumes that the data provider already registered and declared its metrics to the system.

[0018] FIG. 2 illustrates an example of a computing environment within which embodiments of the present disclosure may be implemented. A computing environment 300 includes a computer system 510 that may include a communication mechanism such as a system bus 521 or other communication mechanism for communicating information within the computer system 510. The computer system 510 further includes one or more processors 520 coupled with the system bus 521 for processing the information. The communication system 100 may include, or be coupled to, the one or more processors 520.

[0019] The processors 520 may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art. More generally, a processor as described herein is a device for executing machine-readable instructions stored on a computer readable medium, for performing tasks and may comprise any one or combination of, hardware and firmware. A processor may also comprise memory storing machine-readable instructions executable for performing tasks. A processor acts upon information by manipulating, analyzing, modifying, converting or transmitting information for use by an executable procedure or an information device, and/or by routing the information to an output device. A processor may use or comprise the capabilities of a computer, controller or microprocessor, for example, and be conditioned using executable instructions to perform special purpose functions not performed by a general purpose computer. A processor may include any type of suitable processing unit including, but not limited to, a central processing unit, a microprocessor, a Reduced Instruction Set Computer (RISC) microprocessor, a Complex Instruction Set Computer (CISC) microprocessor, a microcontroller, an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a System-on-a-Chip (SoC), a digital signal processor (DSP), and so forth. Further, the processor(s) 520 may have any suitable microarchitecture design that includes any number of constituent components such as, for example, registers, multiplexers, arithmetic logic units, cache controllers for controlling read/write operations to cache memory, branch predictors, or the like. The microarchitecture design of the processor may be capable of supporting any of a variety of instruction sets. A processor may be coupled (electrically and/or as comprising executable components) with any other processor enabling interaction and/or communication there-between. A user interface processor or generator is a known element comprising electronic circuitry or software or a combination of both for generating display images or portions thereof. A user interface comprises one or more display images enabling user interaction with a processor or other device.

[0020] The system bus 521 may include at least one of a system bus, a memory bus, an address bus, or a message bus, and may permit exchange of information (e.g., data (including computer-executable code), signaling, etc.) between various components of the computer system 510. The system bus 521 may include, without limitation, a memory bus or a memory controller, a peripheral bus, an accelerated graphics port, and so forth. The system bus 521 may be associated with any suitable bus architecture including, without limitation, an Industry Standard Architecture (ISA), a Micro Channel Architecture (MCA), an Enhanced ISA (EISA), a Video Electronics Standards Association (VESA) architecture, an Accelerated Graphics Port (AGP) architecture, a Peripheral Component Interconnects (PCI) architecture, a PCI -Express architecture, a Personal Computer Memory Card International Association (PCMCIA) architecture, a Universal Serial Bus (USB) architecture, and so forth.

[0021] Continuing with reference to FIG. 2, the computer system 510 may also include a system memory 530 coupled to the system bus 521 for storing information and instructions to be executed by processors 520. The system memory 530 may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only memory (ROM) 531 and/or random access memory (RAM) 532. The RAM 532 may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM). The ROM 531 may include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM). In addition, the system memory 530 may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors 520. A basic input/output system 533 (BIOS) containing the basic routines that help to transfer information between elements within computer system 510, such as during start-up, may be stored in the ROM 531. RAM 532 may contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processors 520. System memory 530 may additionally include, for example, operating system 534, application programs 535, and other program modules 536. Application programs 535 may also include a user portal for development of the application program, allowing input parameters to be entered and modified as necessary.

[0022] The operating system 534 may be loaded into the memory 530 and may provide an interface between other application software executing on the computer system 510 and hardware resources of the computer system 510. More specifically, the operating system 534 may include a set of computer-executable instructions for managing hardware resources of the computer system 510 and for providing common services to other application programs (e.g., managing memory allocation among various application programs). In certain example embodiments, the operating system 534 may control execution of one or more of the program modules depicted as being stored in the data storage 540. The operating system 534 may include any operating system now known or which may be developed in the future including, but not limited to, any server operating system, any mainframe operating system, or any other proprietary or non-proprietary operating system.

[0023] The computer system 510 may also include a disk/media controller 543 coupled to the system bus 521 to control one or more storage devices for storing information and instructions, such as a magnetic hard disk 541 and/or a removable media drive 542 (e.g., floppy disk drive, compact disc drive, tape drive, flash drive, and/or solid state drive). Storage devices 540 may be added to the computer system 510 using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire). Storage devices 541 , 542 may be external to the computer system 510.

[0024] The computer system 510 may also include a field device interface 565 coupled to the system bus 521 to control a field device 566, such as a device used in a production line. The computer system 510 may include a user input interface or GUI 561, which may comprise one or more input devices, such as a keyboard, touchscreen, tablet and/or a pointing device, for interacting with a computer user and providing information to the processors 520.

[0025] The computer system 510 may perform a portion or all of the processing steps of embodiments of the invention in response to the processors 520 executing one or more sequences of one or more instructions contained in a memory, such as the system memory 530. Such instructions may be read into the system memory 530 from another computer readable medium of storage 540, such as the magnetic hard disk 541 or the removable media drive 542. The magnetic hard disk 541 (or solid state drive) and/or removable media drive 542 may contain one or more data stores and data files used by embodiments of the present disclosure. The data store 540 may include, but are not limited to, databases (e.g., relational, object-oriented, etc.), file systems, flat files, distributed data stores in which data is stored on more than one node of a computer network, peer-to-peer network data stores, or the like. The data stores may store various types of data such as, for example, skill data, sensor data, or any other data generated in accordance with the embodiments of the disclosure. Data store contents and data files may be encrypted to improve security. The processors 520 may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory 530. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0026] As stated above, the computer system 510 may include at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein. The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processors 520 for execution. A computer readable medium may take many forms including, but not limited to, non-transitory, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as magnetic hard disk 541 or removable media drive 542. Non-limiting examples of volatile media include dynamic memory, such as system memory 530. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the system bus 521. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0027] Computer readable medium instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, statesetting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0028] Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer readable medium instructions.

[0029] The computing environment 300 may further include the computer system 510 operating in a networked environment using logical connections to one or more remote computers, such as remote computing device 580. The network interface 570 may enable communication, for example, with other remote devices 580 or systems and/or the storage devices 541, 542 via the network 571. Remote computing device 580 may be a personal computer (laptop or desktop), a mobile device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system 510. When used in a networking environment, computer system 510 may include modem 572 for establishing communications over a network 571, such as the Internet. Modem 572 may be connected to system bus 521 via user network interface 570, or via another appropriate mechanism.

[0030] Network 571 may be any network or system generally known in the art, including the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a direct connection or series of connections, a cellular telephone network, or any other network or medium capable of facilitating communication between computer system 510 and other computers (e.g., remote computing device 580). The network 571 may be wired, wireless or a combination thereof. Wired connections may be implemented using Ethernet, Universal Serial Bus (USB), RJ-6, or any other wired connection generally known in the art. Wireless connections may be implemented using Wi-Fi, WiMAX, and Bluetooth, infrared, cellular networks, satellite or any other wireless connection methodology generally known in the art. Additionally, several networks may work alone or in communication with each other to facilitate communication in the network 571.

[0031] It should be appreciated that the program modules, applications, computer-executable instructions, code, or the like depicted in FIG. 2 as being stored in the system memory 530 are merely illustrative and not exhaustive and that processing described as being supported by any particular module may alternatively be distributed across multiple modules or performed by a different module. In addition, various program module(s), script(s), plug-in(s), Application Programming Interface(s) (API(s)), or any other suitable computer-executable code hosted locally on the computer system 510, the remote device 580, and/or hosted on other computing device(s) accessible via one or more of the network(s) 571, may be provided to support functionality provided by the program modules, applications, or computer-executable code depicted in FIG. 2 and/or additional or alternate functionality. Further, functionality may be modularized differently such that processing described as being supported collectively by the collection of program modules depicted in FIG. 2 may be performed by a fewer or greater number of modules, or functionality described as being supported by any particular module may be supported, at least in part, by another module. In addition, program modules that support the functionality described herein may form part of one or more applications executable across any number of systems or devices in accordance with any suitable computing model such as, for example, a client-server model, a peer-to-peer model, and so forth. In addition, any of the functionality described as being supported by any of the program modules depicted in FIG. 7 may be implemented, at least partially, in hardware and/or firmware across any number of devices.

[0032] It should further be appreciated that the computer system 510 may include alternate and/or additional hardware, software, or firmware components beyond those described or depicted without departing from the scope of the disclosure. More particularly, it should be appreciated that software, firmware, or hardware components depicted as forming part of the computer system 510 are merely illustrative and that some components may not be present or additional components may be provided in various embodiments. While various illustrative program modules have been depicted and described as software modules stored in system memory 530, it should be appreciated that functionality described as being supported by the program modules may be enabled by any combination of hardware, software, and/or firmware. It should further be appreciated that each of the above-mentioned modules may, in various embodiments, represent a logical partitioning of supported functionality. This logical partitioning is depicted for ease of explanation of the functionality and may not be representative of the structure of software, hardware, and/or firmware for implementing the functionality. Accordingly, it should be appreciated that functionality described as being provided by a particular module may, in various embodiments, be provided at least in part by one or more other modules. Further, one or more depicted modules may not be present in certain embodiments, while in other embodiments, additional modules not depicted may be present and may support at least a portion of the described functionality and/or additional functionality. Moreover, while certain modules may be depicted and described as sub-modules of another module, in certain embodiments, such modules may be provided as independent modules or as sub-modules of other modules.

[0033] Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and/or processing capabilities described with respect to a particular device or component may be performed by any other device or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure. In addition, it should be appreciated that any operation, element, component, data, or the like described herein as being based on another operation, element, component, data, or the like can be additionally based on one or more other operations, elements, components, data, or the like. Accordingly, the phrase “based on,” or variants thereof, should be interpreted as “based at least in part on.” [0034] Although embodiments have been described in language specific to structural features and/or methodological acts, it is to be understood that the disclosure is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the embodiments. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and/or steps are included or are to be performed in any particular embodiment.

[0035] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Claims

CLAIMS What is claimed is:

1. A method performed within a network comprising a plurality of virtual network functions and a controller, the method comprising: receiving a request, from a first computing device, for measurement data, the request indicating a first measurement unit for the measurement data that is associated with the first computing device; based on the request, the controller identifying a second computing device for fulfilling the request; receiving the measurement data, by the controller, from the second computing device, the measurement data defining a second measurement unit associated with the second computing device that is different than the first measurement unit; based on the request and network traffic within the network, the controller selecting virtual network functions from the plurality of virtual network functions, so as to define selected virtual network functions; routing data associated with the request through the selected virtual network functions; and at the selected virtual network functions, converting the measurement data from the second measurement unit to the first measurement unit.

2. The method as recited in claim 1, the method further comprising: based on a first semantic description associated with the request, determining that the first computing device requests data structured in accordance with the first measurement unit.

3. The method as recited in claim 2, wherein converting the measurement data at the selected virtual network functions further comprises: based on a second semantic description associated with the measurement data, determining that the second computing device provides data structured in accordance with the second measurement unit.

4. The method as recited in claim 3, converting the measurement data at the selected virtual network functions further comprises: performing a mapping between the second measurement unit and the first measurement unit over a plurality of the selected network functions so as to perform the mapping in a distributed manner.

5. The method as recited in claim 1, wherein the controller is hosted on a router of the network.

6. A network comprising: a plurality of virtual network functions; a processor; and a memory storing instructions that, when executed by the processor, cause the network to: receive a request, from a first computing device, for measurement data, the request indicating a first measurement unit for the measurement data that is associated with the first computing device; based on the request, identify a second computing device for fulfilling the request; receive the measurement data from the second computing device, the measurement data defining a second measurement unit associated with the second computing device that is different than the first measurement unit; based on the request and network traffic within the network, select virtual network functions from the plurality of virtual network functions, so as to define selected virtual network functions; rout data associated with the request through the selected virtual network functions, wherein the selected network functions are configured to convert the measurement data from the second measurement unit to the first measurement unit.

7. The network as recited in claim 6, the memory further storing instructions that, when executed by the processor, further cause the network to: based on a first semantic description associated with the request, determine that the first computing device requests data structured in accordance with the first measurement unit.

8. The network as recited in claim 7, wherein the selected network functions are further configured to: based on a second semantic description associated with the measurement data, determine that the second computing device provides data structured in accordance with the second measurement unit.

9. The network as recited in claim 8, wherein the selected network functions are further configured to: performing a mapping between the second measurement unit and the first measurement unit over a plurality of the selected network functions so as to perform the mapping in a distributed manner.

10. The network as recited in claim 1, wherein the network further comprises a router and the processor is hosted on the router of the network.

17