Patent Description:
MPEG Network Based Media Processing (NBMP) project has developed a concept of processing media on cloud. However, current NBMP design does not provide any information about how a workflow including various tasks may be allocated to the different cloud and network resources considering the proximity of those tasks to various sources and sinks. Further, to the extent that the current NBMP design provides a proximity parameter for running various tasks, there is not provided a logical grouping of the tasks, or any method for measuring the quality of dividing a workflow among different network entities, media processing entities (MPEs), sources, or sinks.

The NBMP Draft International Specification shows a great potential to increase media processing efficiency, faster and lower-cost deployment of media services, and ability to provide large scale deployment by leveraging the public, private or hybrid cloud services.

The current NBMP specification defines a placement of the tasks to the geographical location of the data center. However, there is no relative distance signaling of tasks when multiple sources and sinks exist, and there does not exist a logical grouping of the task to be run on the same hardware or cloud node or a network cluster, or a way to measure the efficiency of assigning tasks to different network entities. <CIT> discloses a method, an electronic device, and computer readable medium. The method includes receiving, from a media source, a request for media processing, where the request includes a requested media output. The method also includes identifying one or more media processing functions to perform the request for media processing based on information associated with each of the one or more media processing functions. The method further includes configuring each of the one or more media processing functions by mapping the request for media processing to the one or more media processing functions based on the information associated with each of the one or more media processing functions. The method additionally includes monitoring the one or more media processing functions while the one or more media processing functions perform tasks to generate the requested media output. <CIT> discloses methods and systems for cloud computing which enable the efficient and flexible placement of application components within a cloud. A computing device (<NUM>) is described. The computing device (<NUM>) is adapted to receive a plurality of component placement requests for one or more components (<NUM>) of a corresponding plurality of applications (<NUM>); determine a plurality of feature vectors (<NUM>) from the plurality of component placement requests, respectively; wherein each feature vector (<NUM>) comprises vector dimensions which describe different attributes of the respective component placement request: determine a plurality of placement decisions (<NUM>) regarding the plurality of component placement requests, respectively: wherein each placement decision (<NUM>) comprises an indication of one or more executing computing devices (<NUM>) onto which the one or more components (<NUM>) of the respective application (<NUM>) have been placed; cluster the plurality of feature vectors (<NUM>), thereby yielding one or more clusters (<NUM>); wherein each cluster (<NUM>) comprises a default feature vector (<NUM>) describing the different attributes of a default component placement request; determine a default placement decision (<NUM>) for each of the one or more clusters; and store the one or more default feature vectors and the respective one or more default placement decisions (<NUM>) in a database (<NUM>) of the computing device (<NUM>).

According to one or more embodiments, a method performed by at least one processor is provided. The method includes: obtaining, by a workflow manager, a network based media processing (NBMP) workflow including a plurality of workflow tasks and a plurality of proximity parameters which are numbers indicating a plurality of desired distances between all of the plurality of workflow tasks and all of a media source, a media sink and other entities, the other entities including at least one cloud element or network element; assigning the plurality of workflow tasks to the media sink, the media source, and the at least one cloud element or network element, based on the plurality of desired distances; wherein the assigning is the one that optimizes splitting of the workflow, wherein the optimizing comprises calculating the average distance of all tasks of the workflow, selecting the workflow as the optimal one if the workflow has the minimum average distance, and assigning the plurality of workflow tasks of the workflow based on the minimum average distance; wherein a p-norm distance is used for calculating the average distance, the p-norm distance being pd = <MAT>, wherein di is the distance of task i to the sink and source that is allocated to, for all tasks in the workflow; managing the NBMP workflow according to the assigned plurality of workflow tasks; wherein the managing includes managing the assigned plurality of workflow tasks of the NBMP workflow by sending a message to one or more of the media processing entities, the message includes a message causing the workflow to be performed; wherein based on the desired distance being <NUM>, the workflow task is intended to be executed by the at least one of the media source or the media sink; wherein based on the desired distance being infinite, the workflow task is unable to be executed by the at least one of the media source or the media sink.

According to one or more embodiments, a workflow manager of a media system is provided. The workflow manager includes: at least one processor; and memory including computer code. The computer code includes: obtaining code configured to cause the at least one processor to obtain a network based media processing (NBMP) workflow including a plurality of workflow tasks and a plurality of proximity parameters which are numbers indicating a plurality of desired distances between all of the plurality of workflow tasks and all of a media source, a media sink and other entities, the other entities including at least one cloud element or network element; assigning code configured to cause the at least one processor to assign the plurality of workflow tasks to the media sink, the media source, and the at least one cloud element or network element, based on the plurality of desired distances; wherein the assigning is the one that optimizes splitting of the workflow, wherein the optimizing comprises calculating the average distance of all tasks of the workflow, selecting the workflow as the optimal one if the workflow has the minimum average distance, and assigning the plurality of workflow tasks of the workflow based on the minimum average distance; wherein a p-norm distance is used for calculating the average distance, the p-norm distance being pd = <MAT>, wherein di is the distance of task i to the sink and source that is allocated to, for all tasks in the workflow; and managing code configured to cause the at least one processor to manage the NBMP workflow according to the assigned plurality of workflow tasks; wherein managing code (<NUM>) configured to manage the assigned plurality of workflow tasks of the NBMP workflow by sending a message to one or more of the media processing entities, the message includes a message causing the workflow to be performed; wherein based on the desired distance being <NUM>, the workflow task is intended to be executed by the at least one of the media source or the media sink; wherein based on the desired distance being infinite, the workflow task is unable to be executed by the at least one of the media source or the media sink.

According to one or more embodiments, a non-transitory computer-readable medium storing computer code is provided. The computer code is configured to, when executed by at least one processor that implements a workflow manager of a media system, cause the at least one processor to: obtain a network based media processing (NBMP) workflow including a plurality of workflow tasks and a plurality of proximity parameters which are numbers indicating a plurality of desired distances between all of the plurality of workflow tasks and all of a media source, a media sink and other entities, the other entities including at least one cloud element or network element; assign the plurality of workflow tasks to the media sink, the media source, and the at least one cloud element or network element, based on the plurality of desired distances; wherein the assigning is the one that optimizes splitting of the workflow, wherein the optimizing comprises calculating the average distance of all tasks of the workflow, selecting the workflow as the optimal one if the workflow has the minimum average distance, and assigning the plurality of workflow tasks of the workflow based on the minimum average distance; wherein a p-norm distance is used for calculating the average distance, the p-norm distance being <MAT>, wherein di is the distance of task i to the sink and source that is allocated to, for all tasks in the workflow; and manage the NBMP workflow according to the assigned plurality of workflow tasks; wherein the managing includes managing the assigned plurality of workflow tasks of the NBMP workflow by sending a message to one or more of the media processing entities, the message includes a message causing the workflow to be performed; wherein based on the desired distance being <NUM>, the workflow task is intended to be executed by the at least one of the media source or the media sink; wherein based on the desired distance being infinite, the workflow task is unable to be executed by the at least one of the media source or the media sink.

<FIG> is a diagram of an environment <NUM> in which methods, apparatuses, and systems described herein may be implemented, according to embodiments. As shown in <FIG>, the environment <NUM> may include a user device <NUM>, a platform <NUM>, and a network <NUM>. Devices of the environment <NUM> may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections.

The user device <NUM> includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with platform <NUM>. For example, the user device <NUM> may include a computing device (e.g. a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g. a smart phone, a radiotelephone, etc.), a wearable device (e.g. a pair of smart glasses or a smart watch), or a similar device. In some implementations, the user device <NUM> may receive information from and/or transmit information to the platform <NUM>.

The platform <NUM> includes one or more devices as described elsewhere herein. In some implementations, the platform <NUM> may include a cloud server or a group of cloud servers. In some implementations, the platform <NUM> may be designed to be modular such that software components may be swapped in or out depending on a particular need. As such, the platform <NUM> may be easily and/or quickly reconfigured for different uses.

In some implementations, as shown, the platform <NUM> may be hosted in a cloud computing environment <NUM>. Notably, while implementations described herein describe the platform <NUM> as being hosted in the cloud computing environment <NUM>, in some implementations, the platform <NUM> may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

The cloud computing environment <NUM> includes an environment that hosts the platform <NUM>. The cloud computing environment <NUM> may provide computation, software, data access, storage, etc. services that do not require end-user (e.g. the user device <NUM>) knowledge of a physical location and configuration of system(s) and/or device(s) that hosts the platform <NUM>. As shown, the cloud computing environment <NUM> may include a group of computing resources <NUM> (referred to collectively as "computing resources <NUM>" and individually as "computing resource <NUM>").

The computing resource <NUM> includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, the computing resource <NUM> may host the platform <NUM>. The cloud resources may include compute instances executing in the computing resource <NUM>, storage devices provided in the computing resource <NUM>, data transfer devices provided by the computing resource <NUM>, etc. In some implementations, the computing resource <NUM> may communicate with other computing resources <NUM> via wired connections, wireless connections, or a combination of wired and wireless connections.

As further shown in <FIG>, the computing resource <NUM> includes a group of cloud resources, such as one or more applications ("APPs") <NUM>-<NUM>, one or more virtual machines ("VMs") <NUM>-<NUM>, virtualized storage ("VSs") <NUM>-<NUM>, one or more hypervisors ("HYPs") <NUM>-<NUM>, or the like.

The application <NUM>-<NUM> includes one or more software applications that may be provided to or accessed by the user device <NUM> and/or the platform <NUM>. The application <NUM>-<NUM> may eliminate a need to install and execute the software applications on the user device <NUM>. For example, the application <NUM>-<NUM> may include software associated with the platform <NUM> and/or any other software capable of being provided via the cloud computing environment <NUM>. In some implementations, one application <NUM>-<NUM> may send/receive information to/from one or more other applications <NUM>-<NUM>, via the virtual machine <NUM>-<NUM>.

The virtual machine <NUM>-<NUM> includes a software implementation of a machine (e.g. a computer) that executes programs like a physical machine. The virtual machine <NUM>-<NUM> may be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by the virtual machine <NUM>-<NUM>. A system virtual machine may provide a complete system platform that supports execution of a complete operating system ("OS"). A process virtual machine may execute a single program, and may support a single process. In some implementations, the virtual machine <NUM>-<NUM> may execute on behalf of a user (e.g. the user device <NUM>), and may manage infrastructure of the cloud computing environment <NUM>, such as data management, synchronization, or long-duration data transfers.

The virtualized storage <NUM>-<NUM> includes one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of the computing resource <NUM>. In some implementations, within the context of a storage system, types of virtualizations may include block virtualization and file virtualization. Block virtualization may refer to abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without regard to physical storage or heterogeneous structure. The separation may permit administrators of the storage system flexibility in how the administrators manage storage for end users. File virtualization may eliminate dependencies between data accessed at a file level and a location where files are physically stored. This may enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations.

The hypervisor <NUM>-<NUM> may provide hardware virtualization techniques that allow multiple operating systems (e.g. "guest operating systems") to execute concurrently on a host computer, such as the computing resource <NUM>. The hypervisor <NUM>-<NUM> may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources.

The network <NUM> includes one or more wired and/or wireless networks. For example, the network <NUM> may include a cellular network (e.g. a fifth generation (<NUM>) network, a long-term evolution (LTE) network, a third generation (<NUM>) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g. the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks.

Additionally, or alternatively, a set of devices (e.g. one or more devices) of the environment <NUM> may perform one or more functions described as being performed by another set of devices of the environment <NUM>.

<FIG> is a block diagram of example components of one or more devices of <FIG>. The device <NUM> may correspond to the user device <NUM> and/or the platform <NUM>. As shown in <FIG>, the device <NUM> may include a bus <NUM>, a processor <NUM>, a memory <NUM>, a storage component <NUM>, an input component <NUM>, an output component <NUM>, and a communication interface <NUM>.

The bus <NUM> includes a component that permits communication among the components of the device <NUM>. The processor <NUM> is implemented in hardware, firmware, or a combination of hardware and software. The processor <NUM> is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor <NUM> includes one or more processors capable of being programmed to perform a function. The memory <NUM> includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g. a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by the processor <NUM>.

The storage component <NUM> stores information and/or software related to the operation and use of the device <NUM>. For example, the storage component <NUM> may include a hard disk (e.g. a magnetic disk, an optical disk, a magneto-optic disk, and/or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable medium, along with a corresponding drive.

The input component <NUM> includes a component that permits the device <NUM> to receive information, such as via user input (e.g. a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone). Additionally, or alternatively, the input component <NUM> may include a sensor for sensing information (e.g. a global positioning system (GPS) component, an accelerometer, a gyroscope, and/or an actuator). The output component <NUM> includes a component that provides output information from the device <NUM> (e.g. a display, a speaker, and/or one or more light-emitting diodes (LEDs)).

The communication interface <NUM> includes a transceiver-like component (e.g. a transceiver and/or a separate receiver and transmitter) that enables the device <NUM> to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface <NUM> may permit the device <NUM> to receive information from another device and/or provide information to another device. For example, the communication interface <NUM> may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

The device <NUM> may perform one or more processes described herein. The device <NUM> may perform these processes in response to the processor <NUM> executing software instructions stored by a non-transitory computer-readable medium, such as the memory <NUM> and/or the storage component <NUM>. A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices.

Software instructions may be read into the memory <NUM> and/or the storage component <NUM> from another computer-readable medium or from another device via the communication interface <NUM>. When executed, software instructions stored in the memory <NUM> and/or the storage component <NUM> may cause the processor <NUM> to perform one or more processes described herein.

In practice, the device <NUM> may include additional components, fewer components, different components, or differently arranged components than those shown in <FIG>. Additionally, or alternatively, a set of components (e.g. one or more components) of the device <NUM> may perform one or more functions described as being performed by another set of components of the device <NUM>.

In an embodiment of the present disclosure, an NBMP system <NUM> is provided. With reference to <FIG>, the NBMP system <NUM> includes an NBMP source <NUM>, an NBMP workflow manager <NUM>, a function repository <NUM>, one or more media processing entities (MPEs) <NUM>, a media source <NUM>, and a media sink <NUM>.

The NBMP source <NUM> may receive instructions from a third party entity, may communicate with the NBMP workflow manager <NUM> via an NBMP workflow API <NUM>, and may communicate with the function repository <NUM> via a function discovery API <NUM>. For example, the NBMP source <NUM> may send a workflow description document(s) (WDD) to the NBMP workflow manager <NUM>, and may read the function description of functions stored in the function repository <NUM>, the functions being media processing functions stored in memory of the function repository <NUM> such as, for example, functions of media decoding, feature point extraction, camera parameter extraction, projection method, seam information extraction, blending, post-processing, and encoding. The NBMP source <NUM> may include or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the NBMP source <NUM>.

The NBMP source <NUM> may request the NBMP workflow manager <NUM> to create workflow including tasks <NUM> to be performed by the one or more media processing entities <NUM> by sending the workflow description document, which may include several descriptors, each of which may have several parameters.

For example, the NBMP source <NUM> may select functions stored in the function repository <NUM> and send the workflow description document to the NBMP workflow manager <NUM> that includes a variety of descriptors for description details such as input and output data, required functions, and requirements for the workflow. The workflow description document may include a set of task descriptions and a connection map of inputs and outputs of tasks <NUM> to be performed by one or more of the media processing entities <NUM>. When the NBMP workflow manager <NUM> receives such information from the NBMP source <NUM>, the NBMP workflow manager <NUM> may create the workflow by instantiating the tasks based on function names and connecting the tasks in accordance with the connection map.

Alternatively or additionally, the NBMP source <NUM> may request the NBMP workflow manager <NUM> to create workflow by using a set of keywords. For example, NBMP source <NUM> may send the NBMP workflow manager <NUM> the workflow description document that may include a set of keywords that the NBMP workflow manager <NUM> may use to find appropriate functions stored in the function repository <NUM>. When the NBMP workflow manager <NUM> receives such information from the NBMP source <NUM>, the NBMP workflow manager <NUM> may create the workflow by searching for appropriate functions using the keywords that may be specified in a Processing Descriptor of the workflow description document, and use the other descriptors in the workflow description document to provision tasks and connect them to create the workflow.

The NBMP workflow manager <NUM> may communicate with the function repository <NUM> via a function discovery API <NUM>, which may be a same or different API from the function discovery API <NUM>, and may communicate with one or more of the media processing entities <NUM> via an API <NUM> (e.g. an NBMP task API). The NBMP workflow manager <NUM> may include or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the NBMP workflow manager <NUM>.

The NBMP workflow manager <NUM> may use the API <NUM> to setup, configure, manage, and monitor one or more tasks <NUM> of a workflow that is performable by the one or more media processing entities <NUM>. In an embodiment, the NBMP workflow manager <NUM> may use the API <NUM> to update and destroy the tasks <NUM>. In order to configure, manage, and monitor tasks <NUM> of the workflow, the NBMP workflow manager <NUM> may send messages, such as requests, to one or more of the media processing entities <NUM>, wherein each message may have several descriptors, each of which have several parameters. The tasks <NUM> may each include media processing functions <NUM> and configurations <NUM> for the media processing functions <NUM>.

In an embodiment, after receiving a workflow description document from the NBMP source <NUM> that does not include a list of the tasks (e.g. includes a list of keywords instead of a list of tasks), the NBMP workflow manager <NUM> may select the tasks based on the descriptions of the tasks in the workflow description document to search the function repository <NUM>, via the function discovery API <NUM>, to find the appropriate functions to run as tasks <NUM> for a current workflow. For example, the NBMP workflow manager <NUM> may select the tasks based on keywords provided in the workflow description document. After the appropriate functions are identified by using the keywords or the set of task descriptions that is provided by the NBMP source <NUM>, the NBMP workflow manager <NUM> may configure the selected tasks in the workflow by using the API <NUM>. For example, the NBMP workflow manager <NUM> may extract configuration data from information received from the NBMP source, and configure the tasks <NUM> based on the configuration data.

The one or more media processing entities <NUM> may be configured to receive media content from the media source <NUM>, process the media content in accordance with the workflow, that includes tasks <NUM>, created by the NBMP workflow manager <NUM>, and output the processed media content to the media sink <NUM>. The one or more media processing entities <NUM> may each include or be implemented by at least one processor and memory that stores code configured to cause the at least processor to perform the functions of the media processing entities <NUM>.

The media source <NUM> may include memory that stores media and may be integrated with or separate from the NBMP source <NUM>. In an embodiment, the NBMP workflow manager <NUM> may notify the NBMP source <NUM> when a workflow is prepared and the media source <NUM> may transmit media content to the one or more of the media processing entities <NUM> based on the notification that the workflow is prepared.

The media sink <NUM> may include or be implemented by at least one processor and at least one display that is configured to display the media that is processed by the one or more media processing entities <NUM>.

As discussed above, messages from the NBMP Source <NUM> (e.g. a workflow description document for requesting creation of a workflow) to the NBMP workflow manager <NUM>, and messages (e.g. for causing the workflow to be performed) from the NBMP workflow manager <NUM> to the one or more media processing entities <NUM> may include several descriptors, each of which may have several parameters. In cases, communication between any of the components of the NBMP system <NUM> using an API may include several descriptors, each of which may have several parameters.

In embodiments, each workflow or task may provide a proximity parameter per source or sink, to indicate the desired/required proximity to that source, as shown for example in Table <NUM>:.

The distance may be defined as a number showing the relative distance of a workflow or task to each source and sink, as shown in Table <NUM>:.

Therefore a distance of a task having a distance of 2N from S2 has twice the distance from S1 if its distance to S1 is N. The distance of <NUM> means there is no distance between task and sink/source.

To signal the distance of each workflow/task, a new array of object, proximity, may be added to General Descriptor, as shown for example in Table <NUM>:.

In the above Table <NUM>, and in other Tables shown herein, added elements are indicated as italicized.

This parameter may be added to the general descriptor in the form of an array of a JSON object. The JSON object may have two parameters: sink/source identifier (id) and the distance, as shown for example in Table <NUM>:
<IMG>.

An NBMP Source <NUM> may assign a relative distance of a workflow to each source and/or sink in the given workflow description. The , by looking at the distance array can decide whether the entire workflow or parts of workflow to be implemented in the cloud platform or network elements that are closer to the sources or sinks with smaller distance values in the workflow description. The exact allocation depends on the availability of cloud or network resources. The optimization may be performed by the workflow manager <NUM> and a Cloud Manager.

In embodiments in which the workflow is given by NBMP Source <NUM>, the workflow description document (WDD) may contain the connection map. Each function instance may have a function restriction in which the general descriptor is used. The proximity object in that descriptor may be used to describe the desired distance of the function instance to the sources and sinks.

When the workflow manager <NUM> instantiates each task for each function instance, it uses the proximity array to allocate the best cloud/network resources based on the indicated desired distances for those tasks.

In embodiments, the workflow may be derived by the workflow manager <NUM>. the workflow manager <NUM> may provide the WDD to NBMP Source <NUM> including the connection map and task restrictions. The NBMP Source <NUM> can update the WDD, by adding the proximity object to each task restrictions. Then the workflow manager <NUM> with the help of Cloud Manager may want to reallocate the task to various cloud/network resources that satisfy the proximity requirements described by the updated WDD. Finally, the workflow manager <NUM> can return the updated WDD with the actual updated relative distance of the tasks to each source/sink.

Embodiments provide a method for describing the relative distance of a workflow or a task from a plurality of sources or sinks and sources, including assigning a number to a respective source or sink of the plurality of sources or sinks, wherein the number indicates a relative distance from the workflow or the task to the respective source or sink compared to distances from the workflow or the task to other sources or sinks among the plurality of sources or sinks.

Embodiments may provide a method of signaling, by a Network Based Media Processing (NBMP) sink, a target proximity of the workflow or the task to the plurality of sources and sinks using the assigned number.

Embodiments may provide a method of determining proximity information of allocated cloud or network resources to each task based on the number assigned to the assigned number; using, by a NBMP workflow manager <NUM>, the proximity information of the allocated cloud or network resources to accommodate the target proximity; and providing an update to the NBMP sink.

In embodiments, the update may include how the workflow or the task is implemented according to the target proximity.

Embodiments may relate to a new logical entity referred to as a Task Group. A Task Group may be a collection of tasks or function instances that are expected to run on the same cloud node/cluster. A Task Group may be identified with a unique identifier that is unique between Task Groups and Tasks.

A Workflow Description with a collection of functions or tasks, may have a table that defines the Task Groups as shown in Table <NUM>:.

The current NBMP TuC defines a distance table between tasks, MPEs, Sources, and Sinks, as shown in Table <NUM> below:.

Embodiments may relate to an extension of Table <NUM> to include also the Task Groups (TG) as shown in Table <NUM> below:.

Note that a distance table may be defined for each Task, or extended for Task Groups. Also, each Task Group's distance table may include columns for one or more Task Groups.

In embodiments, if a Task Group is included in the above table, each member of the Task Group that is not explicitly included in the table inherits the distance of the Task Group.

In embodiments, if a Task Group has the above table, each member of the Task Group inherits the entries of the TaskGroup Table, unless it has an explicit column for a Task or Task Group.

In embodiments, in a Task's distance table, if there is a distance for the Task Group that the Task belongs to, then that distance indicates the distance of that Task to all other Tasks in the Task Group.

In embodiments, in a Task's distance table, if there is a distance for any Task belonging to the same Task Group of the Task, then this value supersedes the distance of the Task Group in the same table.

The task grouping may be implemented in JSON as shown in Table <NUM> below:
<IMG>.

Embodiments may provide a method for describing groups of Task or Function instances which enables defining logical group of Task or Function instances to be implemented together wherein the distance of a group of Task or function instances are described from a source, a sink, an MPE, other tasks, or others task groups, wherein the distance of task groups are defined together as well as the distance of each task or function instance inside a task group is defined from the other tasks/function instances of the same group and therefore a detailed description of the distances as well as a logical grouping of the functions are shown.

In embodiments, each workflow or task may provide a distance from source or sink or MPE, or any other network element, to indicate the desired/required proximity to that source, as shown in Table <NUM>:.

The distance is defined as a number showing the relative distance of a workflow or task to each source and sink, as shown in Table <NUM>:.

To signal that a task cannot be run on a network entity or source or sink, embodiments define an infinite distance:
If a resource (source/sink/MPE) is incapable of running a task, a largest unsigned integer (INF) can be assigned for the task's distance to that resource. Therefore, when the workflow manager <NUM> wants to split the tasks among different resources, it shall not assign a task to a resource that has INF value.

Difference schemes can be used to split a workflow between multiple sources, sinks, MPEs, and other network entities. The best split-rendering scheme is the one that reduces the average distance of all tasks of a workflow.

For instance, the average p-norm distance is defined as shown in Equation <NUM>, below: <MAT>.

Where d i is the distance of task i to the sink/source/MPE that is allocated to, for n tasks in the workflow.

If the tasks proximity parameters are given by the NBMP Source <NUM>, the NBMP Source <NUM> may also provide the distance function so that the workflow manager <NUM> can optimize the workflow split based on that function.

To signal this metric, embodiments may add an object in the General Descriptor, as shown in Tables <NUM>-<NUM> below:.

The above parameters may be optional, but at least one of them may exist in the proximity-metric object. The default equation may be <NUM>-norm in the above object.

Embodiments may provide a method for describing the infinite distance between a task and a device or network entity, wherein the infinite distance means that the task cannot be run on that device or network entity.

Embodiments provide a method for describing the split efficiency by introducing the average distance of all tasks of the workflow to the entity they are assigned to be run, wherein the smaller average distance indicate a more efficient splitting of the workflow, wherein in a particular case, a p-norm distance is used for the calculating split efficiency.

Embodiments may provide a method of signaling the equation used for calculating the split efficiency in workflow description, wherein the information can be exchanged between an NBMP Source <NUM> and workflow manager <NUM>/Cloud Platform, and any equation can be implemented, in particular, the p-norm distance.

<FIG> is a flowchart illustrating example process <NUM> for managing an NBMP workflow. In some implementations, one or more process blocks of <FIG> may be performed by, for example, workflow manager <NUM>.

As shown in <FIG>, process <NUM> may include obtaining, by a workflow manager, a network based media processing (NBMP) workflow including a plurality of workflow tasks and a plurality of proximity parameters which indicate a plurality of desired distances between the plurality of workflow tasks and at least one of a media source and a media sink (block <NUM>).

As further shown in <FIG>, process <NUM> includes assigning the plurality of workflow tasks to the media sink, the media source, and at least one cloud element or network element, based on the plurality of desired distances (block <NUM>).

As further shown in <FIG>, process <NUM> may include managing the NBMP workflow according to the assigned plurality of workflow tasks (block <NUM>).

In embodiments, the NBMP workflow may be provided by at least one from among the workflow manager or an NBMP source.

In embodiments, a proximity parameter from among the plurality of proximity parameters may include a number indicating a desired distance between a workflow task of the plurality of workflow tasks and the at least one of the media source and the media sink.

In embodiments, based on the desired distance being <NUM>, the workflow task is intended to be executed by the at least one of the media source or the media sink.

In embodiments, based on the desired distance being infinite, the workflow task is unable to be executed by the at least one of the media source or the media sink.

In embodiments, the NBMP workflow may correspond to a workflow description document, and the plurality of proximity parameters may be included in at least one general descriptor included in the workflow description document.

In embodiments, the NBMP workflow may include a task group which includes at least one of the plurality of workflow tasks.

In embodiments, a proximity parameter of the plurality of proximity parameters may indicate a desired distance between the task group and the at least one of the media source and the media sink.

In embodiments, the at least one of the plurality of workflow tasks included in the task group may inherit the desired distance.

In embodiments, an average of the plurality of desired distances may be used to determine an efficiency of the NBMP workflow.

In embodiments, with reference to <FIG>, computer code <NUM> may be implemented in the NBMP system <NUM>. For example, the computer code may be stored in memory of the NBMP workflow manager <NUM> and may be executed by at least one processor of the NBMP workflow manager <NUM>. The computer code may include, for example, obtaining code <NUM>, assigning code <NUM>, and managing code <NUM>.

The workflow obtaining code <NUM>, assigning code <NUM>, and managing code <NUM> may be configured to cause the at least one processer of the NBMP workflow manager <NUM> to perform the aspects of the process described above with reference to <FIG>, respectively.

The obtaining code <NUM> may be configured to cause the at least one processor to obtain a network based media processing (NBMP) workflow including a plurality of workflow tasks and a plurality of proximity parameters which indicate a plurality of desired distances between the plurality of workflow tasks and at least one of a media source and a media sink.

The assigning code <NUM> may be configured to cause the at least one processor to assign the plurality of workflow tasks to the media sink, the media source, and at least one cloud element or network element, based on the plurality of desired distances.

The managing code <NUM> may be configured to cause the at least one processor to manage the NBMP workflow according to the assigned plurality of workflow tasks.

Embodiments of the present disclosure may be used separately or combined in any order. Further, each of the embodiments (and methods thereof) may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium.

Even though combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations.

Claim 1:
A method performed by at least one processor, the method comprising:
obtaining (<NUM>), by a workflow manager, a network based media processing NBMP, workflow including a plurality of workflow tasks and a plurality of proximity parameters which are numbers indicating a plurality of desired distances between all of the plurality of workflow tasks and all of a media source, a media sink and other entities, the other entities including at least one cloud element or network element;
assigning (<NUM>) the plurality of workflow tasks to the media sink, the media source, and the at least one cloud element or network element, based on the plurality of desired distances;
wherein the assigning is the one that optimizes splitting of the workflow, wherein the optimizing comprises calculating the average distance of all tasks of the workflow, selecting the workflow as the optimal one if the workflow has the minimum average distance, and assigning the plurality of workflow tasks of the workflow based on the minimum average distance; wherein a p-norm distance is used for calculating the average distance, the p-norm distance being <MAT>, wherein di is the distance of task i to the sink and source that is allocated to, for all tasks in the workflow;
managing (<NUM>) the NBMP workflow according to the assigned plurality of workflow tasks; wherein the managing includes managing the assigned plurality of workflow tasks of the NBMP workflow by sending a message to one or more of the media processing entities, the message includes a message causing the workflow to be performed;
wherein based on the desired distance being <NUM>, the workflow task is intended to be executed by the at least one of the media source or the media sink;
wherein based on the desired distance being infinite, the workflow task is unable to be executed by the at least one of the media source or the media sink.