Patent Publication Number: US-2023161623-A1

Title: Vehicle as a distributed computing resource

Description:
TECHNICAL FIELD 
     The disclosed subject matter relates to vehicles (e.g., transportation vehicles) and, more particularly, to leveraging of vehicles and associated components, such as autonomous driving components, in a distributed computing fabric. 
     BACKGROUND 
     As the world becomes increasingly digital, demand for data processing capabilities also increases. Similarly, the demand to transmit associated data also increases. Such demands can increase costs for associated hardware and services, as more resources often need to be added to a network in order to keep pace with demand. Compounding this problem, supply chains, at times, have experienced shortages, which have resulted in a periodic lack of availability of integrated circuits and other electronic components. Existing computing solutions do not efficiently distribute available computing resources, resulting in increased demand for even more integrated circuits (e.g., semiconductor chips), thus further constraining supplies, increasing costs, and wasting valuable resources. Additionally, increased abstraction of services can lead to increased demand in computational capacities. Further, existing cloud-based solutions can lead to increased carbon emissions when ramping-up cloud-based computing capacities. 
     The above-described background relating to distributed computing (e.g., of vehicles) is merely intended to provide a contextual overview of some current issues and is not intended to be exhaustive. Other contextual information may become further apparent upon review of the following detailed description. 
     SUMMARY 
     The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, devices, computer-implemented methods, and/or computer program products that facilitate vehicles as distributed computing resources. 
     As alluded to above, distributed computing (e.g., using an automobile) can be improved in various ways, and various embodiments are described herein to this end and/or other ends. 
     According to an embodiment, a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise: a request component that determines a compute request received via a network from a network device registered to use the system, and a resource component that, in response to a compute criterion associated with a vehicle communicatively coupled to the network being determined to be satisfied, allocates at least some compute resources of the vehicle to the compute request. 
     According to another embodiment, a non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network, and in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating at least some compute resources of the vehicle to the compute request. 
     According to yet another embodiment, a method can comprise: determining, by a device comprising a processor, a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network, and in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating, by the device, at least some compute resources of the vehicle to the compute request. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    illustrates a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  2    illustrates a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  3    illustrates a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  4    illustrates a depiction of an example, non-limiting driving scenario in accordance with one or more embodiments described herein. 
         FIG.  5    illustrates a depiction of an example, non-limiting scenario in accordance with one or more embodiments described herein. 
         FIG.  6    is an exemplary flowchart of a process associated with distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. 
         FIG.  7    illustrates a block flow diagram for a process associated with distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. 
         FIG.  8    illustrates a block flow diagram for a process associated with distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. 
         FIG.  9    is an example, non-limiting computing environment in which one or more embodiments described herein can be implemented. 
         FIG.  10    is an example, non-limiting networking environment in which one or more embodiments described herein can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section. 
     One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details. 
     It will be understood that when an element is referred to as being “coupled” to another element, it can describe one or more different types of coupling including, but not limited to, chemical coupling, communicative coupling, capacitive coupling, electrical coupling, electromagnetic coupling, inductive coupling, operative coupling, conductive coupling, acoustic coupling, ultrasound coupling, optical coupling, physical coupling, thermal coupling, and/or another type of coupling. As referenced herein, an “entity” can comprise a human, a client, a user, a computing device, a software application, an agent, a machine learning model, an artificial intelligence, and/or another entity. It should be appreciated that such an entity can facilitate implementation of the subject disclosure in accordance with one or more embodiments the described herein. 
     Autonomous driving systems require significant computing resources. Therefore, vehicles comprising such autonomous driving systems possess significant computing capabilities, which can sometimes be left idle (e.g., when autonomous driving is not in use or enabled). In this regard, such vehicles can be utilized in a distributed processing fabric in order to allocate unutilized or underutilized computing resources (e.g., of an autonomous driving system or other vehicle systems), thus reducing waste of available computing resources. For example, when a vehicle is engaged in autonomous driving, the vehicle will utilize its own resources (e.g., automated driving compute resources), and may not be able to allocate any extra resources. However, when such a vehicle is parked and/or charging, such resources can be in an idle state, and can therefore be leveraged as processing or worker nodes in a distributed processing network. 
     Turning now to  FIG.  1   , there is illustrated an example, non-limiting system  102  in accordance with one or more embodiments herein. System  102  can comprise a computerized tool, which can be configured to perform various operations relating to distributed computing (e.g., using a vehicle). The system  102  can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , and/or communication component  114 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , communication component  114 , device  118 , and/or vehicle  120  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  102 . 
     According to an embodiment, the request component  110  can determine a compute request received via a network (e.g., network  116 , using a communication component  114 ) from a network device (e.g., device  118 ) registered to use the system (e.g., system  102 ). In one or more embodiments, a vehicle (e.g., vehicle  120 ) (e.g., a car, truck, farm equipment, watercraft, aircraft, train, or another suitable vehicle) can comprise the system  102 . In further embodiments, the system  102  can comprise a vehicle (e.g., vehicle  120 ). It is noted that the device  118  can comprise one or more of a variety of devices communicatively coupled to the system  102  (e.g., over the network  116 ). For example, the device  118  can comprise another vehicle (e.g., comprising a similar system  102 ). In another example, the device  118  can comprise a server or another computing device or component (e.g., in a fixed location or portable). For example, such a device  118  can be located in a building, warehouse, home, or in another location. It is noted that determining a compute request (e.g., by the request component  110 ) can comprise determining a type of compute request that is requested and associated information (e.g., resources requested, length of time requested, associated location(s), associated inventive(s), or other suitable information). 
     In various embodiments herein, the vehicle  120  can comprise a plurality of resource components (e.g., compute resources  122 , such as autonomous driving components, processors or processing units, memory, network components, sensors such as radar sensors, lidar units, or cameras, or other suitable resource components). According to an embodiment, the resource component  112  can, in response to a compute criterion associated with a vehicle (e.g., vehicle  120 ) communicatively coupled to the network (e.g., network  116 ) being determined to be satisfied (e.g., by the resource component  112 ), allocate at least some compute resources (e.g., compute resources  122 ) of the vehicle to the compute request. It is noted that the “at least some compute resources” that are allocated by the resource component  112  can be commensurate with the compute request or with available compute resources (e.g., compute resources not in use by the vehicle  120 . In various embodiments, allocating at least some compute resources  122  of the vehicle  120  to the compute request can comprise allocating (e.g., by the resource component  112 ) at least one processing unit of the plurality of processing units. In further embodiments, allocating at least some compute resources  122  of the vehicle  120  to the compute request can comprise allocating (e.g., by the resource component  112 ) at least one processing thread of a plurality of processing threads. It is noted that allocating such compute resources of the vehicle  120  can be based on respective carbon emissions (e.g., of the vehicle  120  or other vehicles or entities). In various embodiments, the compute criterion herein can comprise a defined threshold utilization percentage of the compute resources  122  by the vehicle. For example, a defined threshold utilization percentage herein can comprise 50% of available computing resources of the vehicle. It is noted that such computing resources can comprise autonomous driving compute resources (e.g., or other associated autonomous driving resources) of the vehicle. Additionally/alternatively, resources can comprise media hardware, GPUs, or other suitable components of the vehicle. In this example, if a vehicle is using less than (e.g., or equal to) 50% of its available computing resources, the compute criterion herein can be determined (e.g., by the resource component  112 ) to be satisfied. It is noted, for example, that a ride-share entity can utilize resources to tailor respective route planning, resource distribution, unplanned maintenance handling, or other suitable operations. 
     In various embodiments, the resource component  112  can function as an orchestrator component (e.g., capable of task switching), which can be utilized to manage tasks associated with the compute resources  122 . In this regard, the resource component  112  can allocate one or more individual compute resources of the compute resources  122  (e.g., based on vehicle  120  needs, compute requests, or other suitable factors). 
     In some embodiments, the at least some compute resources  122  of the vehicle  120  can comprise a containerized worker node in a compute cluster (e.g., Kubernetes cluster). In this regard, the compute resources  122  of the vehicle can comprise virtual and/or physical compute resources (e.g., as managed by a control plane over the network  116 , in which the compute resources of the vehicle can be scheduled to execute a containerized task or application (e.g., in response to a compute criterion associated with the vehicle communicatively coupled to the network being determined to be satisfied). In further embodiments, said control plane can comprise the resource component  112 . In various embodiments, a non-containerized worker node (e.g., using a multiprocessing library) can be utilized (e.g., secured using blockchain technology or another secure security mechanism or component). 
     In various embodiments, compute resources  122  herein can comprise compute resources that are not presently in use by the vehicle  120  (e.g., in an idle state), such as media processing resources, navigation resources, autonomous driving resources, or other suitable resources. In further embodiments, the compute resources  122  herein can comprise compute resources that are not presently in use by a vehicle within a defined range of the vehicle  122 . For instance, such compute resources  122  (e.g., autonomous driving resources) can be in an idle state when the vehicle  120  is not engaged in autonomous driving or when the vehicle  120  is parked or charging. In other embodiments, the compute resources herein can comprise compute resources presently in use. In this regard, such compute resources can be reallocated (e.g., by the resource component  112  in response to an authorization, as later discussed in greater detail). It is noted that the compute criterion being determined (e.g., by the resource component  112 ) to be satisfied can comprise a determination (e.g., by the resource component  112 ) that the vehicle  120  is charging. In this regard, allocation of the compute resources  122  would not reduce a charge state of an associated vehicle  120  (e.g., an electric vehicle) as compute resources  122  (e.g., autonomous driving compute resources) are often idle when such an electric vehicle is charging (e.g., and not in a driving state). 
     According to an example, the vehicle  120  can comprise a first vehicle, and the network device  118  can comprise a second vehicle (see, e.g.,  FIG.  4   ). In this regard, the second vehicle can navigate behind the first vehicle on a same road as the first vehicle, and the compute request can comprise a road condition determination request. Such a road condition determination request can comprise a request for information regarding one or more of a traffic condition (e.g., road congestion, traffic speed, or other traffic information), a road quality (e.g., potholes, bumps, or other road quality information), road hazards (e.g., rain, snow, debris, obstacles, or other road hazard information), emergency vehicles (e.g., presence police cars or ambulances on the road, or other emergency vehicle information), an accident (e.g., a crash, or other accident-related information), or other suitable road condition information. In this regard, the first vehicle can determine such road condition information (e.g., using one or more sensors of the vehicle  120 ) and provide such road condition information to the second vehicle (e.g., via a communication component  114  and/or over the network  116 ). According to an example, such sensors can comprise one or more of radar sensors, lidar units, or cameras, or other suitable sensors. 
     In an implementation, the resource component  112  can, in response to a determination (e.g., by the resource component  112 ) that the vehicle  120  requires compute resources  122  of the vehicle  120  that are presently in use by the device (e.g., network device)  118 , revoke the allocation of the compute resources of the vehicle  120  presently in use by the network device (e.g., the device  118 ). For example, compute resources or other resources of the vehicle  120  can be previously allocated (e.g., to the device  118  by the resource component  112 ), and the vehicle  120  cab later (e.g., subsequent to the allocation) require said resources (e.g., to perform one or more tasks such as autonomous driving or other computing tasks associated with the vehicle). In this situation, the resource component  112  can revoke the allocation (e.g., terminate) of the compute resources of the vehicle  120  presently in use by the device  118  and reallocate said resources to the vehicle  120 . 
     According to an embodiment, the communication component  114  can determine network status information representative of a signal strength and connection speed between the vehicle  120  and the network  116 . In this regard, allocating at least some compute resources of the vehicle to the compute request can further comprise allocating (e.g., by the resource component  112 ) at least some compute resources of the vehicle  120  to the compute request in response the network status information being determined (e.g., by the communication component  114 ) to satisfy a network status threshold. For example, such a network status threshold can be associated with a defined signal strength, network connection type (e.g., 4G, 5G low band, 5G mid band, 5G mmWave, 6G, Bluetooth, UHF, VHF, AM, FM, etc.), network throughput, network latency, or another suitable network status threshold. It is noted that the communication component  114  can comprise the hardware required to implement a variety of communication protocols (e.g., infrared (“IR”), shortwave transmission, near-field communication (“NFC”), Bluetooth, Wi-Fi, long-term evolution (“LTE”), 3G, 4G, 5G, 6G, global system for mobile communications (“GSM”), code-division multiple access (“CDMA”), satellite, visual cues, radio waves, acoustic waves, ultrasound, L-band, etc.) In an embodiment, the resource component  112  can determine and/or estimate carbon emissions associated with utilizing compute resources described herein. For example, the resource component  112  can determine whether it would generate more carbon emissions to conduct compute functions locally (e.g., via a vehicle  120 ) or remotely. For example, if local resources are already active and/or at normal operating temperature, utilizing local resources can be determined to generate less carbon emission than initializing remote resources (e.g., in a cloud computing environment or of another vehicle). Further, such a determination can be based on emissions standards in one or more locations or jurisdictions. 
     Turning now to  FIG.  2   , there is illustrated an example, non-limiting system  202  in accordance with one or more embodiments herein. System  202  can comprise a computerized tool, which can be configured to perform various operations relating to distributed computing (e.g., using a vehicle). The system  202  can be similar to system  102 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , and/or communication component  114 . The system  202  can additionally comprise a location component  204 , route component  206 , and/or navigation component  208 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , communication component  114 , device  118 , vehicle  120 , location component  204 , route component  206 , and/or navigation component  208  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  202 . It is noted that, in various embodiments, system  202  or other systems herein can be utilized in connections with ride sharing entities and/or vehicle to vehicle platforms and associated systems. 
     According to an embodiment, the location component  204  can determine a location (e.g., a geographic location) of the vehicle  120 . In this regard, the compute criterion being determined to be satisfied can comprise a determination (e.g., by the location component  204 ) that the vehicle  120  is located within a threshold distance of the network device  118 . For example, the compute request can comprise a request to facilitate direct communication between the network device  116  and the vehicle  120  (e.g., via the communication component  114 ). In this regard, if the vehicle is within the threshold distance, the communication component  114  can facilitate the direct communication. Further in this regard, unprocessed data can be sent from the device  118  to the vehicle  120 , processed using the compute resources  122  of the vehicle  120 , and returned as processed data to the device  118 . In further embodiments, such communication can be conducted (e.g., using the communication component  114 ) over the network  116  (and/or between vehicles directly). Therefore, two-way communication between the vehicle  120  and other entities (e.g., device  118 ) is enabled herein. 
     In further embodiments, the route component  206  can determine route information representative of a route of the vehicle  120 . In this regard, the compute criterion being determined to be satisfied can further comprise a determination (e.g., by route component  206 ), based on the route information, that the vehicle  120  will remain within the threshold distance of the network device  118  for at least a threshold amount of time. Further in this regard, the route component  206  can predict the amount of time that the vehicle  120  will remain within the threshold distance of the network device  118 , for instance, based on a trajectory (e.g., speed, direction) of the vehicle  120 , a defined route (e.g., entered into a GPS of the vehicle  120 ), a schedule associated with the vehicle  120  or a user or mobile device associated with the vehicle  120 , or other suitable information on which to base such a prediction. For example, the route component  206  can access route information utilized by a GPS of the vehicle  120 . The route component  206  can analyze such route information, and determine and/or predict the amount of time that the vehicle  120  will remain within the threshold distance of the network device  118  (e.g., according to the compute request) (e.g., within a geofence). In this regard, if the vehicle is predicted to remain within the threshold distance for a threshold amount of time (e.g., according to the compute request), the communication component  114  can facilitate the direct communication. Further in this regard, unprocessed data can be sent from the device  118  to the vehicle  120 , processed using the compute resources  122  of the vehicle  120 , and return as processed data to the device  118 . In further embodiments, such communication can be conducted (e.g., using the communication component  114 ) over the network  116 . 
     According to an embodiment, the navigation component  208  can determine status information representative of a navigational status of the vehicle  120 , and in response to a status criterion being determined to be satisfied by the status information, autonomously navigate the vehicle to a location associated with the compute request. It is noted that the compute request can comprise location data representative of the location. Additionally, such a status criterion can comprise a vehicle being in an idle state (e.g., charging, parked, etc.) for a defined or predicted amount of time. In this regard, the navigation component  208  can autonomously navigate the vehicle to the location (e.g., in response to a determination by the navigation component  208  or another suitable component herein that vehicle  120  will be, or is predicted to be, in an idle state for a defined amount of time). Further, such navigation can be based on emissions output and/or based on a respective emission quota. In further embodiments, the compute request can comprise a network traffic request. In this regard, the location can comprise a defined region for cellular coverage to be supplemented via network hardware of the vehicle. For example, the vehicle  120  can be autonomously navigated (e.g., using the navigation component  208 ) to said location in order to supplement cellular coverage (e.g., by utilizing communication component(s) of the vehicle  120  and/or system  202 ). In this regard, the vehicle  120  can operate as a cellular node or signal booster in a cellular network or another suitable wireless network (e.g., network  116 ). In additional embodiments, a ride-share entity can utilize resources of a vehicle herein to tailor respective route planning, resource distribution, unplanned maintenance handling, or other suitable operations. 
     According to an embodiment, the compute request can comprise a mapping request. Such a mapping request can comprise a request to utilize sensor fusion (e.g., using mapping components of the vehicle  120 ) and make said mapping components (e.g., sensors) available to an entity (e.g., associated with the device  118 ). For example, the mapping request can be associated with a particular location (e.g., to performing mapping or street view photography at the location) in order to improve a map or street view (e.g., 360-degrees panorama view of a road or other location). In this regard, the location component  204  can determine a location of the vehicle  120  and/or the route component  206  can determine a route of the vehicle  120 . In this regard, in response to a determination (e.g., by the route component  206 ) that the route of the vehicle  120  satisfies a route criterion (e.g., travels along a road(s)), the navigation component  208  can allocate navigation hardware (e.g., mapping components) of the vehicle  120  to the mapping request. In this regard, the communication component  114  can send such data and/or images to the device  118  via the network  116 . In additional embodiments, such a computing request can be satisfied while the vehicle  120  is engaged in autonomous driving. 
     Turning now to  FIG.  3   , there is illustrated an example, non-limiting system  302  in accordance with one or more embodiments herein. System  302  can comprise a computerized tool, which can be configured to perform various operations relating to distributed computing (e.g., using a vehicle). The system  302  can be similar to system  202 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , communication component  114 , location component  204 , route component  206 , and/or navigation component  208 . The system  302  can additionally comprise an authorization component  304 , billing component  306 , and/or schedule component  308 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , request component  110 , resource component  112 , communication component  114 , device  118 , vehicle  120 , location component  204 , route component  206 , navigation component  208 , authorization component  304 , billing component  306 , and/or schedule component  308  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  302 . 
     According to an embodiment, the authorization component  304  can determine (e.g., via an interface of the vehicle) an authorization to allocate at least some of the compute resources of the vehicle to the compute request. In this regard, the compute criterion can comprise and/or be associated with the authorization. For example, a vehicle  120  can be engaged in autonomous driving and can concurrently receive a request to allocate at least some of the compute resources  122  to a compute request. The authorization can be prompted to a user of the vehicle  120 , for instance, via a graphical user interface of the vehicle  120 , speaker/microphone of the vehicle  120  or otherwise presented to a user (e.g., of the vehicle  120 ). In this regard, in response to the user authorizing the allocation, the resource component  112  can allocate at least some compute resource (e.g., according to the authorization) to satisfy the compute request. It is noted that the authorization can comprise discontinuing autonomous driving and/or disabling other systems of the vehicle  120  in order to make said compute resources available to satisfy the compute request. 
     According to an embodiment, the billing component  306  can, in response to allocating the at least some of the compute resources of the vehicle to the compute request, automatically bill an entity associated with the compute request for the allocation of the at least some of the compute resources (e.g., in response to satisfaction of the compute request). In this regard, such a compute request can comprise an incentive (e.g., for a user of the vehicle  120 ) to authorize the compute request. For example, an immediate need for compute resources may arise (e.g., a natural disaster occurs, and additional compute resources are needed by a telecommunications entity, associated with a device  118 , to accommodate an influx of video calls). In this regard, the billing component  306  can automatically generate a bill or invoice, and send said bill or invoice to the entity. In further embodiments, the billing component  306  can automatically debit funds from the entity (e.g., according to the compute request and/or authorization) and deposit said funds into an account associated with the vehicle  120  and/or a user of the vehicle  120  registered with the system  302 . It is noted that the bill or invoice can comprise an amount defined according to the compute request and/or authorization, or can comprise a rate (e.g., based on time of resource allocation, amount of data computed and/or transmitted, or other suitable factors). For instance, the amount of the bill or invoice can be based on the amount of time that resources are allocated to the compute request and/or the amount of data computed and/or transmitted, associated with the compute request. In further embodiments, a social media entity (e.g., Facebook, Tik Tok, Instagram, Twitter, Snapchat, YouTube, Reddit, or another social media entity as would be understood by one skilled in the art) can experience an influx in user activity. Similarly, an internet provider can request that a vehicle  120  operate as an internet proxy. Likewise, a streaming provider entity (e.g., Netflix, Amazon Prime Video, Hulu, or another suitable streaming provider entity as would be understood by one skilled in the art) can request that the vehicle  120  operate as a streaming proxy. In these examples, compute resources, network equipment and/or other components or resources (e.g., compute resources  122 ) of the vehicle  120  and associated systems can be allocated to an entity (e.g., associated with the device  118 ) and associated compute request. In another example, a compute request can comprise an incentive for a vehicle to move into a high-speed data coverage zone (e.g., of the network  116 ), so that data can be transmitted more rapidly (e.g., as compared a lower-speed data coverage zone). For example, the compute request can comprise an incentive for the vehicle  120  to navigate (e.g., autonomously) into a 5G mmWave coverage area (e.g., from a 4G LTE coverage area) to facilitate more rapid data transmission via the communication component  114  over the network  116 . In another example, a compute request can comprise an incentive for the vehicle  120  to navigate to a lower cost region (e.g., based on data costs, energy costs, parking costs, or other suitable costs). Further, the resource component  112  can determine and/or estimate carbon emissions associated with utilizing compute resources described herein. For example, the resource component  112  can determine whether it would generate more carbon emissions to conduct compute functions locally or remotely. For example, if local resources are already active and/or at normal operating temperature, utilizing local resources can be determined to generate less carbon emission than initializing remote resources (e.g., in a cloud computing environment or of another vehicle). Further, such a determination can be based on emissions standards in one or more locations or jurisdictions. 
     According to an embodiment, the schedule component  308  can determine schedule information associated with a vehicle  120 . In this regard, the schedule component  308  can determine schedule information representative of a schedule of a mobile device communicatively coupled to the vehicle  120 , service (e.g., repair) schedule information associated with the vehicle  120 , or other suitable schedule information. In this regard, a compute criterion being determined to be satisfied can comprise a determination (e.g., by the schedule component  308 ) that the schedule information representative of a schedule associated with the vehicle (e.g., or associated mobile device, user, or other entity) indicates that the vehicle will not be engaged in driving for a period of time that satisfies the compute request. For example, the schedule component  308  can determine that the vehicle  120  will be parked for approximately two hours while a user of the vehicle  120  is at a restaurant eating dinner (e.g., according to a calendar of a mobile device associated with the user and/or the vehicle  120 ). In this example, the defined period of time can comprise one hour, and thus said schedule information can thus satisfy the compute criterion. Further, the schedule component  306  can reschedule various operations herein (e.g., resource sharing, charging) based on carbon emission considerations associated with such operations (e.g., as determined by the resource component  112 ). 
     It is noted that one or more components herein (e.g., the request component  110 , resource component  112 , communication component  114 , location component  204 , route component  206 , navigation component  208 , authorization component  304 , billing component  306 , schedule component  308 , or other suitable components) can leverage artificial intelligence and/or machine learning in order to make various determinations, predictions, data acquisitions, or estimations herein. Further, various defined thresholds herein can be determined using such machine learning (e.g., based on past information). 
     Artificial-intelligence or machine learning systems and techniques can be employed to facilitate learning user behavior, context-based scenarios, load habits, preferences, etc. in order to facilitate taking automated action with high degrees of confidence. Utility-based analysis can be utilized to factor benefit of taking an action against cost of taking an incorrect action. Probabilistic or statistical-based analyses can be employed in connection with the foregoing and/or the following. 
     According to an embodiment, components herein can comprise and/or employ an artificial intelligence (AI) model and/or a machine learning (ML) model that can learn to perform the above or below described functions (e.g., via training using historical training data and/or feedback data). 
     In some embodiments, components herein can comprise an AI and/or ML model that can be trained (e.g., via supervised and/or unsupervised techniques) to perform the above-described functions using historical training data comprising various context conditions. In this example, such an AI and/or ML model can further learn (e.g., via supervised and/or unsupervised techniques) to perform the above-described functions using training data comprising feedback data, where such feedback data can be collected and/or stored (e.g., in memory  104 ). In this example, such feedback data can comprise the various instructions described above/below that can be input over time in response to observed/stored context-based information. 
     One or more components herein can initiate an operation based on a defined level of confidence determined using information (e.g., feedback data). For instance, based on learning to perform such functions described above using the above defined feedback data, one or more components herein can determine appropriate corresponding actions. 
     In an embodiment, components herein can perform a utility-based analysis that factors cost of initiating the above-described operations versus benefit. In this embodiment, a component herein can use one or more additional context conditions to determine whether any action should be taken. In another embodiment, components herein can perform a utility-based analysis that factors an environmental cost (e.g., carbon emissions or other environmental costs) of initiating the above-described operations versus benefit. In this embodiment, a component herein can use one or more of the additional context conditions to determine whether any action should be taken. 
     To facilitate the above-described functions, components herein can perform classifications, correlations, inferences, and/or expressions associated with principles of artificial intelligence. Additionally, components herein can enable automatic control (e.g., of a vehicle herein). For instance, components herein can employ an automatic classification system and/or an automatic classification. In one example, a component herein can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to learn and/or generate inferences. A component herein can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, a component herein can employ expert systems, fuzzy logic, support vector machines (SVMs), Hidden Markov Models (HMMs), greedy search algorithms, generative adversarial networks, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, and/or the like. In another example, a component herein can perform a set of machine learning computations. For instance, a component herein can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, ensemble learning operations, voting classifiers, and/or a set of different machine learning computations. 
       FIG.  4    illustrates an example, non-limiting driving scenario  400  in accordance with one or more embodiments described herein. In driving scenario  400 , vehicle  404  can comprise a first vehicle, and vehicle  406  can comprise a second vehicle. In various embodiments, the vehicle  404  can be similar to the vehicle  120 , and the vehicle  406  can comprise the device  118  and/or also be similar to the vehicle  120 . In an implementation, the second vehicle can be navigating behind the first vehicle (e.g., on the same road  402  and/or respective road condition). In this regard, a compute request (e.g., from the second vehicle) can comprise a road determination request (e.g., sent to the first vehicle). Such a road condition determination request can comprise a request for information regarding one or more of a traffic condition, a road quality (e.g., potholes, bumps), road hazards (e.g., rain, snow, debris, obstacles), emergency vehicles (e.g., police cars or ambulances on the road), an accident (e.g., a crash), or other suitable road condition information. In this regard, the first vehicle can determine such road condition information (e.g., using one or more sensors of the vehicle  120 ) and provide such road condition information to the second vehicle (and/or provide resources needed to discern such information). In an example, resources of vehicle  406  can be made available, for instance, to vehicle  404  for autonomous driving compute functions or other suitable functions. Similarly, resources of vehicle  404  can be made available to vehicle  406  for autonomous driving compute functions or other suitable functions. 
       FIG.  5    illustrates an example, non-limiting scenario  500  in accordance with one or more embodiments described herein. In this regard, one or more of vehicle  502 , vehicle  504 , vehicle  506 , vehicle  508 , and/or vehicle  510  can be communicatively connected to a network (e.g., network  116 ). In this regard, one or more of the vehicle  502 , vehicle  504 , vehicle  506 , vehicle  508 , and/or vehicle  510  can be similar to the vehicle  120 . Further in this regard, the entity  512  can comprise the device  118 . For example, the entity  512  can generate and send a compute request to be communicated over the network  116 . The compute request can be received by one or more of the vehicles  502 ,  504 ,  506 ,  508 , and/or  510 . In this regard, one or more of said vehicles can allocate respective compute resources to the compute request from the entity  512 . Such vehicle(s) can be selected based on a highest respective percentage or volume of available resources, respective length of time capable of allocating resources, lowest respective cost associated with allocating resources (e.g., to be billed to the entity  512 ), lowest computational latency, or other suitable factors. It is noted that one or more proxy components or relay components can be utilized in the transmission of requests and associated data herein. 
     Turning now to  FIG.  6   , there is illustrated a flowchart of a process  600  relating to distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. It is noted that various embodiments herein can utilize blockchain-based recording technologies. At  602  a request (e.g., a compute request) can be received (e.g., via a communication component  114 ). At  604 , a handshake can be determined. For example, such a handshake can comprise a blockchain-based handshake. Such a handshake can be utilized in order to determine whether the request received at  602  comprises a legitimate or authorized request. At  606 , if the handshake is satisfied, the process can proceed to  608 . Otherwise, the request can be ignored and/or revoked, and the process can return to  602 . At  608 , the compute request can be determined (e.g., by a request component  110 ). For example, the request component  110  can determine a type (and/or respective size or complexity) of compute request that is requested and associated information (e.g., resources requested, length of time requested, associated location(s), associated inventive(s), or other suitable information). In another example, the request component  110  can determine whether the compute request comprises a split request, with some computations occurring being requested to occur at the vehicle  112  and other computations occurring elsewhere (e.g., cloud-based). At  610 , the resource component  112  can determine whether the compute request is satisfied (e.g., or can be satisfied). For example, the resource component  112  can determine whether the vehicle (e.g., a vehicle  120 ) comprises sufficient available resources to satisfy the request. Similarly, the resource component can determine whether the vehicle is busy or whether the vehicle can accept a query. In further embodiments, a location component  204  can determine whether a vehicle is, or predict that the vehicle will be, within a threshold distance of a location (e.g., and/or of a device  118 ) or within a geofence. In additional embodiments, the route component  206  can determine whether the vehicle  120  will remain within a threshold distance (e.g., of the location or device  118 ) or within the geofence for at least as threshold amount of time (e.g., according to the compute request). In further embodiments, the compute request can comprise a request to autonomously navigate (e.g., using the navigation component  208 ) the vehicle  120  to a defined location and/or allocate navigation resources (e.g., to the device  118 ). It is noted that the determination of whether/where to allocate resources of the vehicle  120  (e.g., allocate to the vehicle  120 , or decline to allocate to the vehicle  120  and thus facilitate the compute request via external resources) can be based in part on carbon emissions and/or respective quotas associated with the resources. For example, based on charge levels, energy sources, or other factors, the resource component  112  can determine carbon emissions or other suitable environmental factors associated with the facilitation of the request (e.g., via the vehicle  120  or elsewhere). At  610 , if the compute request is determined or predicted to be satisfied, the process can proceed to  612 . Otherwise, the process can return to  602 . At  612 , the resource component  112  can determine which compute resources (e.g., of the compute resources  122 ) to allocate to the compute request. In various embodiments, the vehicle  120  can comprise defined allocation limits. For example, said defined allocation limits can be according to a task being conducted (or to be conducted) by the vehicle  120  (e.g., such as autonomous driving). In this regard, if the vehicle  120  is engaged in autonomous driving, the vehicle  120  can be limited to allocating zero or a defined limit (e.g., quantity, percentage, etc.) of said resources. At  614 , an authorization can be determined (e.g., by the authorization component  304 ). At  616 , If the authorization is received (e.g., via a graphical user interface of the vehicle  120 , via a mobile device communicatively coupled to the vehicle  120 , or otherwise received), the process can proceed to  620 . If at  616 , the authorization is not received, the compute request can be revoked or denied at  618 . At  620 , resources (e.g., as determined in step  612 ) can be allocated to the compute request (e.g., by the resource component  112 ). If at  622 , the allocated compute resources are determined (e.g., by the resource component  112 ) to be required by the vehicle  120  (e.g., after the allocation to the device  118 ), the allocation of the resources can be revoked at  618 . Otherwise, the allocation can continue (e.g., according to the compute request) until completed, and the process can return to  602 . It is noted that the resource check conducted at  622  can be conducted at any point in the process  600  (e.g., during steps  620 - 634  or during other steps). At  624 , the request (e.g., the request that was received at  602 ) can be executed. At  626 , results associated with the request (e.g., completion of execution of the request) can be determined. At  628 , the results determined at  626  can be transmitted (e.g., using a communication component  114 ) to an entity associated with the request. At  630 , “clean-up” can occur. In this regard, memory allocated to the request can be unallocated. It is noted that process  600  can additionally comprise generating/sending (e.g., using the billing component  306 ) a bill (e.g., associated with the compute request). For example, if the compute request (e.g., the request received at  602 ) comprised an incentive (e.g., Y at  632 ), the process can proceed to  634  at which a bill can be generated and/or sent (e.g., using the billing component  306 ) to an entity (e.g., and/or device  118 ) associated with the compute request. In further embodiments, the billing component  306  can automatically debit funds from the entity (e.g., according to the compute request and/or authorization) and deposit said funds into an account associated with the vehicle  120  and/or a user of the vehicle  120  registered with the system  302 . It is noted that the bill or invoice can comprise an amount defined according to the compute request and/or authorization, or can comprise a rate (e.g., based on time of resource allocation, amount of data computed and/or transmitted, or other suitable factors). It is noted that in some embodiments, the bill or invoice can be based in part on associated carbon emissions generated as a consequence of the facilitation of the compute request. If at  632  no fee/incentive is provided, the process  600  can return to  602 . 
       FIG.  7    illustrates a block flow diagram for a process  700  associated with distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. At  702 , the process  700  can comprise determining a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network. At  704 , the process  700  can comprise, in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating at least some compute resources of the vehicle to the compute request. 
       FIG.  8    illustrates a block flow diagram for a process  800  associated with distributed computing (e.g., using a vehicle) in accordance with one or more embodiments described herein. At  802 , the process  800  can comprise determining, by a device comprising a processor, a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network. At  802 , the process  800  can comprise, in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating, by the device, at least some compute resources of the vehicle to the compute request. 
     Systems described herein can be coupled (e.g., communicatively, electrically, operatively, optically, inductively, acoustically, etc.) to one or more local or remote (e.g., external) systems, sources, and/or devices (e.g., electronic control systems (ECU), classical and/or quantum computing devices, communication devices, etc.). For example, system  102  (or other systems, controllers, processors, etc.) can be coupled (e.g., communicatively, electrically, operatively, optically, etc.) to one or more local or remote (e.g., external) systems, sources, and/or devices using a data cable (e.g., High-Definition Multimedia Interface (HDMI), recommended standard (RS), Ethernet cable, etc.) and/or one or more wired networks described below. 
     In some embodiments, systems herein can be coupled (e.g., communicatively, electrically, operatively, optically, inductively, acoustically, etc.) to one or more local or remote (e.g., external) systems, sources, and/or devices (e.g., electronic control units (ECU), classical and/or quantum computing devices, communication devices, etc.) via a network. In these embodiments, such a network can comprise one or more wired and/or wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet), and/or a local area network (LAN). For example, system  102  can communicate with one or more local or remote (e.g., external) systems, sources, and/or devices, for instance, computing devices using such a network, which can comprise virtually any desired wired or wireless technology, including but not limited to: powerline ethernet, VHF, UHF, AM, wireless fidelity (Wi-Fi), BLUETOOTH®, fiber optic communications, global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra-mobile broadband (UMB), high speed packet access (HSPA), Zigbee and other 802.XX wireless technologies and/or legacy telecommunication technologies, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, L-band voice or data information, 6LoWPAN (IPv6 over Low power Wireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB) standard protocol, and/or other proprietary and non-proprietary communication protocols. In this example, system  102  can thus include hardware (e.g., a central processing unit (CPU), a transceiver, a decoder, an antenna (e.g., a ultra-wideband (UWB) antenna, a BLUETOOTH® low energy (BLE) antenna, etc.), quantum hardware, a quantum processor, etc.), software (e.g., a set of threads, a set of processes, software in execution, quantum pulse schedule, quantum circuit, quantum gates, etc.), or a combination of hardware and software that facilitates communicating information between a system herein and remote (e.g., external) systems, sources, and/or devices (e.g., computing and/or communication devices such as, for instance, a smart phone, a smart watch, wireless earbuds, etc.). 
     System herein can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by processor (e.g., a processor  106  which can comprise a classical processor, a quantum processor, etc.), can facilitate performance of operations defined by such component(s) and/or instruction(s). Further, in numerous embodiments, any component associated with a system herein, as described herein with or without reference to the various figures of the subject disclosure, can comprise one or more computer and/or machine readable, writable, and/or executable components and/or instructions that, when executed by a processor, can facilitate performance of operations defined by such component(s) and/or instruction(s). Consequently, according to numerous embodiments, system herein and/or any components associated therewith as disclosed herein, can employ a processor (e.g., processor  106 ) to execute such computer and/or machine readable, writable, and/or executable component(s) and/or instruction(s) to facilitate performance of one or more operations described herein with reference to system herein and/or any such components associated therewith. 
     Systems herein can comprise any type of system, device, machine, apparatus, component, and/or instrument that comprises a processor and/or that can communicate with one or more local or remote electronic systems and/or one or more local or remote devices via a wired and/or wireless network. All such embodiments are envisioned. For example, a system (e.g., a system  302  or any other system or device described herein) can comprise a computing device, a general-purpose computer, field-programmable gate array, AI accelerator application-specific integrated circuit, a special-purpose computer, an onboard computing device, a communication device, an onboard communication device, a server device, a quantum computing device (e.g., a quantum computer), a tablet computing device, a handheld device, a server class computing machine and/or database, a laptop computer, a notebook computer, a desktop computer, wearable device, internet of things device, a cell phone, a smart phone, a consumer appliance and/or instrumentation, an industrial and/or commercial device, a digital assistant, a multimedia Internet enabled phone, a multimedia players, and/or another type of device. 
     In order to provide additional context for various embodiments described herein,  FIG.  9    and the following discussion are intended to provide a brief, general description of a suitable computing environment  900  in which the various embodiments of the embodiment described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the various methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers (e.g., ruggedized personal computers), field-programmable gate arrays, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data. 
     Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory, or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. 
     Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries, or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. 
     Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, optic, infrared, and other wireless media. 
     With reference again to  FIG.  9   , the example environment  900  for implementing various embodiments of the aspects described herein includes a computer  902 , the computer  902  including a processing unit  904 , a system memory  906  and a system bus  908 . The system bus  908  couples system components including, but not limited to, the system memory  906  to the processing unit  904 . The processing unit  904  can be any of various commercially available processors, field-programmable gate array, AI accelerator application-specific integrated circuit, or other suitable processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit  904 . 
     The system bus  908  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  906  includes ROM  910  and RAM  912 . A basic input/output system (BIOS) can be stored in a nonvolatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  902 , such as during startup. The RAM  912  can also include a high-speed RAM such as static RAM for caching data. It is noted that unified Extensible Firmware Interface(s) can be utilized herein. 
     The computer  902  further includes an internal hard disk drive (HDD)  914  (e.g., EIDE, SATA), one or more external storage devices  916  (e.g., a magnetic floppy disk drive (FDD)  916 , a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive  920  (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD  914  is illustrated as located within the computer  902 , the internal HDD  914  can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment  900 , a solid-state drive (SSD) could be used in addition to, or in place of, an HDD  914 . The HDD  914 , external storage device(s)  916  and optical disk drive  920  can be connected to the system bus  908  by an HDD interface  924 , an external storage interface  926  and an optical drive interface  928 , respectively. The interface  924  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein. 
     The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  902 , the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein. 
     A number of program modules can be stored in the drives and RAM  912 , including an operating system  930 , one or more application programs  932 , other program modules  934  and program data  936 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  912 . The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems. 
     Computer  902  can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system  930 , and the emulated hardware can optionally be different from the hardware illustrated in  FIG.  9   . In such an embodiment, operating system  930  can comprise one virtual machine (VM) of multiple VMs hosted at computer  902 . Furthermore, operating system  930  can provide runtime environments, such as the Java runtime environment or the .NET framework, for applications  932 . Runtime environments are consistent execution environments that allow applications  932  to run on any operating system that includes the runtime environment. Similarly, operating system  930  can support containers, and applications  932  can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application. 
     Further, computer  902  can be enabled with a security module, such as a trusted processing module (TPM). For instance, with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer  902 , e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution. 
     A user can enter commands and information into the computer  902  through one or more wired/wireless input devices, e.g., a keyboard  938 , a touch screen  940 , and a pointing device, such as a mouse  942 . Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit  904  through an input device interface  944  that can be coupled to the system bus  908 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc. 
     A monitor  946  or other type of display device can be also connected to the system bus  908  via an interface, such as a video adapter  948 . In addition to the monitor  946 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  902  can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  950 . The remote computer(s)  950  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  902 , although, for purposes of brevity, only a memory/storage device  952  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  954  and/or larger networks, e.g., a wide area network (WAN)  956 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  902  can be connected to the local network  954  through a wired and/or wireless communication network interface or adapter  958 . The adapter  958  can facilitate wired or wireless communication to the LAN  954 , which can also include a wireless access point (AP) disposed thereon for communicating with the adapter  958  in a wireless mode. 
     When used in a WAN networking environment, the computer  902  can include a modem  960  or can be connected to a communications server on the WAN  956  via other means for establishing communications over the WAN  956 , such as by way of the Internet. The modem  960 , which can be internal or external and a wired or wireless device, can be connected to the system bus  908  via the input device interface  944 . In a networked environment, program modules depicted relative to the computer  902  or portions thereof, can be stored in the remote memory/storage device  952 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     When used in either a LAN or WAN networking environment, the computer  902  can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices  916  as described above. Generally, a connection between the computer  902  and a cloud storage system can be established over a LAN  954  or WAN  956  e.g., by the adapter  958  or modem  960 , respectively. Upon connecting the computer  902  to an associated cloud storage system, the external storage interface  926  can, with the aid of the adapter  958  and/or modem  960 , manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface  926  can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer  902 . 
     The computer  902  can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
     Referring now to  FIG.  10   , there is illustrated a schematic block diagram of a computing environment  1000  in accordance with this specification. The system  1000  includes one or more client(s)  1002 , (e.g., computers, smart phones, tablets, cameras, PDA&#39;s). The client(s)  1002  can be hardware and/or software (e.g., threads, processes, computing devices). The client(s)  1002  can house cookie(s) and/or associated contextual information by employing the specification, for example. 
     The system  1000  also includes one or more server(s)  1004 . The server(s)  1004  can also be hardware or hardware in combination with software (e.g., threads, processes, computing devices). The servers  1004  can house threads to perform transformations of media items by employing aspects of this disclosure, for example. One possible communication between a client  1002  and a server  1004  can be in the form of a data packet adapted to be transmitted between two or more computer processes wherein data packets may include coded analyzed headspaces and/or input. The data packet can include a cookie and/or associated contextual information, for example. The system  1000  includes a communication framework  1006  (e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s)  1002  and the server(s)  1004 . 
     Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s)  1002  are operatively connected to one or more client data store(s)  1008  that can be employed to store information local to the client(s)  1002  (e.g., cookie(s) and/or associated contextual information). Similarly, the server(s)  1004  are operatively connected to one or more server data store(s)  1010  that can be employed to store information local to the servers  1004 . Further, the client(s)  1002  can be operatively connected to one or more server data store(s)  1010 . 
     In one exemplary implementation, a client  1002  can transfer an encoded file, (e.g., encoded media item), to server  1004 . Server  1004  can store the file, decode the file, or transmit the file to another client  1002 . It is noted that a client  1002  can also transfer uncompressed file to a server  1004  and server  1004  can compress the file and/or transform the file in accordance with this disclosure. Likewise, server  1004  can encode information and transmit the information via communication framework  1006  to one or more clients  1002 . 
     The illustrated aspects of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     The above description includes non-limiting examples of the various embodiments. It is, of course, not possible to describe every conceivable combination of components or methods for purposes of describing the disclosed subject matter, and one skilled in the art can recognize that further combinations and permutations of the various embodiments are possible. The disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. 
     With regard to the various functions performed by the above-described components, devices, circuits, systems, etc., the terms (including a reference to a “means”) used to describe such components are intended to also include, unless otherwise indicated, any structure(s) which performs the specified function of the described component (e.g., a functional equivalent), even if not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, such feature can be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. 
     The terms “exemplary” and/or “demonstrative” as used herein are intended to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent structures and techniques known to one skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements. 
     The term “or” as used herein is intended to mean an inclusive “or” rather than an exclusive “or.” For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Additionally, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless either otherwise specified or clear from the context to be directed to a singular form. 
     The term “set” as employed herein excludes the empty set, i.e., the set with no elements therein. Thus, a “set” in the subject disclosure includes one or more elements or entities. Likewise, the term “group” as utilized herein refers to a collection of one or more entities. 
     The description of illustrated embodiments of the subject disclosure as provided herein, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as one skilled in the art can recognize. In this regard, while the subject matter has been described herein in connection with various embodiments and corresponding drawings, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below. 
     Further aspects of the invention are provided by the subject matter of the following clauses: 
     1. A system, comprising: 
     a memory that stores computer executable components; and 
     a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: 
     a request component that determines a compute request received via a network from a network device registered to use the system; and 
     a resource component that, in response to a compute criterion associated with a vehicle communicatively coupled to the network being determined to be satisfied, allocates at least some compute resources of the vehicle to the compute request. 
     2. The system of any preceding clause, wherein the compute criterion comprises a threshold utilization percentage of the compute resources by the vehicle. 
     3. The system of any preceding clause, wherein the compute resources comprise compute resources not presently in use by the vehicle. 
     4. The system of any preceding clause, wherein the computer executable components further comprise: 
     a location component that determines a location of the vehicle, wherein the compute criterion being determined to be satisfied comprises a determination that the vehicle is located within a threshold distance of the network device. 
     5. The system of any preceding clause, wherein the computer executable components further comprise: 
     a route component that determines route information representative of a route of the vehicle, wherein the compute criterion being determined to be satisfied further comprises a determination, based on the route information, that the vehicle will remain within the threshold distance of the network device for at least a threshold amount of time. 
     6. The system of any preceding clause, wherein the resource component, in response to a determination that the vehicle requires compute resources of the vehicle presently in use by the network device, revokes the allocation of the compute resources of the vehicle presently in use by the network device. 
     7. The system of any preceding clause, wherein the at least some compute resources of the vehicle comprise a containerized worker node in a compute cluster. 
     8. The system of any preceding clause, wherein the computer executable components further comprise: 
     an authorization component that determines, via an interface of the vehicle, an authorization to allocate at least some of the compute resources of the vehicle to the compute request, wherein the compute criterion comprises the authorization. 
     9. The system of any preceding clause, wherein the computer executable components further comprise: 
     a navigation component that determines status information representative of a navigational status of the vehicle, and in response to a status criterion being determined to be satisfied by the status information, autonomously navigates the vehicle to a location associated with the compute request, wherein the compute request comprises location data representative of the location. 
     10. The system of any preceding clause, wherein the compute request comprises a network traffic request, and wherein the location comprises a defined region for cellular coverage to be supplemented via network hardware of the vehicle. 
     11. The system of any preceding clause, wherein the computer executable components further comprise: 
     a billing component that, in response to allocating the at least some of the compute resources of the vehicle to the compute request, automatically bills an entity associated with the compute request for the allocation of the at least some of the compute resources. 
     12. The system of clause 1 above with any set of combinations of the systems 2-11 above. 
     13. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: 
     determining a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network; and 
     in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating at least some compute resources of the vehicle to the compute request. 
     14. The non-transitory machine-readable medium of any preceding clause, wherein the compute request comprises a mapping request, and wherein the operations further comprise: 
     determining a route of a vehicle; and 
     in response to a determination that the route of the vehicle satisfies a route criterion, allocating navigation hardware of the vehicle to the mapping request. 
     15. The non-transitory machine-readable medium of any preceding clause, wherein the operations further comprise: 
     determining network status information representative of a signal strength and connection speed between the vehicle and the network, wherein the allocating at least some compute resources of the vehicle to the compute request further comprises allocating at least some compute resources of the vehicle to the compute request in response the network status information being determined to satisfy a network status threshold. 
     16. The non-transitory machine-readable medium of any preceding clause, wherein the vehicle comprises a plurality of processing units, and wherein the allocating at least some compute resources of the vehicle to the compute request comprise allocating at least one processing unit of the plurality of processing units. 
     17. The non-transitory machine-readable medium of any preceding clause, wherein the vehicle comprises a first vehicle, and wherein the network device comprises a second vehicle. 
     18. The non-transitory machine-readable medium of any preceding clause, wherein the second vehicle is navigating behind the first vehicle on a same road as the first vehicle, and wherein the compute request comprises a road condition determination request. 
     19. The non-transitory machine-readable medium of clause 13 above with any set of combinations of the non-transitory machine-readable mediums 14-18 above. 
     20. A method, comprising: 
     determining, by a device comprising a processor, a compute request received via a network from a network device registered with a vehicle communicatively coupled to the network; and in response to a compute criterion associated with the vehicle being determined to be satisfied, allocating, by the device, at least some compute resources of the vehicle to the compute request. 
     21. The method of any preceding clause, wherein the compute criterion being determined to be satisfied comprises a determination that the vehicle is charging. 
     22. The method of any preceding clause, wherein the compute criterion being determined to be satisfied comprises a determination that scheduling information representative of a schedule associated with the vehicle indicates that the vehicle will not be engaged in driving for a period of time that satisfies the compute request. 
     23. The method of clause 20 above with any set of combinations of the methods of clauses 21-22 above.