Patent Publication Number: US-2022214924-A1

Title: Scheduled data transfer

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This is a non-provisional application for patent entitled to a filing date and claiming the benefit of earlier-filed U.S. Provisional Patent Application No. 63/134,844, filed Jan. 7, 2021, herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Field of the Invention 
     The field of the invention is data processing, or, more specifically, methods, apparatus, autonomous vehicles, and products for scheduled data transfer. 
     Description of Related Art 
     In a tightly coupled real-time distributed system, multiple nodes may need to perform data transfers using a finite amount of memory resources. This may result in performance degradation as nodes compete for the use of these resources. 
     SUMMARY 
     Scheduled data transfer may include determining, based on a data transfer schedule for a plurality of nodes, a time to transfer data from a first node to a second node; and transferring, at the determined time, data from the first node to the second node. 
     The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows example views of an autonomous vehicle for scheduled data transfer. 
         FIG. 2  is block diagram of an autonomous computing system for scheduled data transfer. 
         FIG. 3  is a block diagram of a redundant power fabric for scheduled data transfer. 
         FIG. 4  is a block diagram of a redundant data fabric for scheduled data transfer. 
         FIG. 5  is an example view of process allocation across CPU packages for scheduled data transfer. 
         FIG. 6  is an example view of an execution environment for scheduled data transfer. 
         FIG. 7  is a flowchart of an example method for scheduled data transfer. 
         FIG. 8  is a flowchart of an example method for scheduled data transfer. 
         FIG. 9  is a flowchart of an example method for scheduled data transfer. 
     
    
    
     DETAILED DESCRIPTION 
     Scheduled data transfer may be implemented in an autonomous vehicle. Accordingly,  FIG. 1  shows multiple views of an autonomous vehicle  100  configured for scheduled data transfer according to embodiments of the present invention. Right side view  101   a  shows a right side of the autonomous vehicle  100 . Shown in the right side view  101   a  are cameras  102  and  103 , configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle  100  from the perspective of the right side of the car. Front view  101   b  shows a front side of the autonomous vehicle  100 . Shown in the front view  101   b  are cameras  104  and  106 , configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle  100  from the perspective of the front of the car. Rear view  101   c  shows a rear side of the autonomous vehicle  100 . Shown in the rear view  101   c  are cameras  108  and  110 , configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle  100  from the perspective of the rear of the car. Top view  101   d  shows a rear side of the autonomous vehicle  100 . Shown in the top view  101   d  are cameras  102 - 110 . Also shown are cameras  112  and  114 , configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle  100  from the perspective of the left side of the car. 
     Further shown in the top view  101   d  is an automation computing system  116 . The automation computing system  116  comprises one or more computing devices configured to control one or more autonomous operations (e.g., autonomous driving operations) of the autonomous vehicle  100 . For example, the automation computing system  116  may be configured to process sensor data (e.g., data from the cameras  102 - 114  and potentially other sensors), operational data (e.g., a speed, acceleration, gear, orientation, turning direction), and other data to determine a operational state and/or operational history of the autonomous vehicle. The automation computing system  116  may then determine one or more operational commands for the autonomous vehicle (e.g., a change in speed or acceleration, a change in brake application, a change in gear, a change in turning or orientation, etc.). The automation computing system  116  may also capture and store sensor data. Operational data of the autonomous vehicle may also be stored in association with corresponding sensor data, thereby indicating the operational data of the autonomous vehicle  100  at the time the sensor data was captured. 
     Although the autonomous vehicle  100  if  FIG. 1  is shown as car, it is understood that autonomous vehicles  100  configured for scheduled data transfer may also include other vehicles, including motorcycles, planes, helicopters, unmanned aerial vehicles (UAVs, e.g., drones), or other vehicles as can be appreciated. Moreover, it is understood that additional cameras or other external sensors may also be included in the autonomous vehicle  100 . 
     Scheduled data transfer in accordance with the present invention is generally implemented with computers, that is, with automated computing machinery. For further explanation, therefore,  FIG. 2  sets forth a block diagram of automated computing machinery comprising an exemplary automation computing system  116  configured for scheduled data transfer according to embodiments of the present invention. The automation computing system  116  of  FIG. 2  includes at least one computer Central Processing Unit (CPU) package  204  as well as random access memory  206  (‘RAM’) which is connected through a high speed memory bus  208  and bus adapter  210  to CPU packages  204  via a front side bus  211  and to other components of the automation computing system  116 . 
     A CPU package  204  may comprise a plurality of processing units. For example, each CPU package  204  may comprise a logical or physical grouping of a plurality of processing units. Each processing unit may be allocated a particular process for execution. Moreover, each CPU package  204  may comprise one or more redundant processing units. A redundant processing unit is a processing unit not allocated a particular process for execution unless a failure occurs in another processing unit. For example, when a given processing unit allocated a particular process fails, a redundant processing unit may be selected and allocated the given process. A process may be allocated to a plurality of processing units within the same CPU package  204  or different CPU packages  204 . For example, a given process may be allocated to a primary processing unit in a CPU package  204 . The results or output of the given process may be output from the primary processing unit to a receiving process or service. The given process may also be executed in parallel on a secondary processing unit. The secondary processing unit may be included within the same CPU package  204  or a different CPU package  204 . The secondary processing unit may not provide its output or results of the process until the primary processing unit fails. The receiving process or service will then receive data from the secondary processing unit. A redundant processing unit may then be selected and have allocated the given process to ensure that two or more processing units are allocated the given process for redundancy and increased reliability. 
     The CPU packages  204  are communicatively coupled to one or more sensors  212 . The sensors  212  are configured to capture sensor data describing the operational and environmental conditions of an autonomous vehicle. For example, the sensors  212  may include cameras (e.g., the cameras  102 - 114  of  FIG. 1 ), accelerometers, Global Positioning System (GPS) radios, Lidar sensors, or other sensors as can be appreciated. As described herein, cameras may include a stolid state sensor  212  with a solid state shutter capable of measuring photons or a time of flight of photons. For example, a camera may be configured to capture or measure photons captured via the shutter for encoding as images and/or video data. As another example, a camera may emit photons and measure the time of flight of the emitted photons. Cameras may also include event cameras configured to measure changes in light and/or motion of light. 
     Although the sensors  212  are shown as being external to the automation computing system  116 , it is understood that one or more of the sensors  212  may reside as a component of the automation computing system  116  (e.g., on the same board, within the same housing or chassis). The sensors  212  may be communicatively coupled with the CPU packages  204  via a switched fabric  213 . The switched fabric  213  comprises a communications topology through which the CPU packages  204  and sensors  212  are coupled via a plurality of switching mechanisms (e.g., latches, switches, crossbar switches, field programmable gate arrays (FPGAs), etc.). For example, the switched fabric  213  may implement a mesh connection connecting the CPU packages  204  and sensors  212  as endpoints, with the switching mechanisms serving as intermediary nodes of the mesh connection. The CPU packages  204  and sensors  212  may be in communication via a plurality of switched fabrics  213 . For example, each of the switched fabrics  213  may include the CPU packages  204  and sensors  212 , or a subset of the CPU packages  204  and sensors  212 , as endpoints. Each switched fabric  213  may also comprise a respective plurality of switching components. The switching components of a given switched fabric  213  may be independent (e.g., not connected) of the switching components of other switched fabrics  213  such that only switched fabric  213  endpoints (e.g., the CPU packages  204  and sensors  212 ) are overlapping across the switched fabrics  213 . This provides redundancy such that, should a connection between a CPU package  204  and sensor  212  fail in one switched fabric  213 , the CPU package  204  and sensor  212  may remain connected via another switched fabric  213 . Moreover, in the event of a failure in a CPU package  204 , a processor of a CPU package  204 , or a sensor, a communications path excluding the failed component and including a functional redundant component may be established. 
     The CPU packages  204  and sensors  212  are configured to receive power from one or more power supplies  215 . The power supplies  215  may comprise an extension of a power system of the autonomous vehicle  100  or an independent power source (e.g., a battery). The power supplies  215  may supply power to the CPU packages  204  and sensors  212  by another switched fabric  214 . The switched fabric  214  provides redundant power pathways such that, in the event of a failure in a power connection, a new power connection pathway may be established to the CPU packages  204  and sensors  212 . 
     Stored in RAM  206  is an automation module  220 . The automation module  220  may be configured to process sensor data from the sensors  212  to determine a driving decision for the autonomous vehicle. The driving decision comprises one or more operational commands for an autonomous vehicle  100  to affect the movement, direction, or other function of the autonomous vehicle  100 , thereby facilitating autonomous driving or operation of the vehicle. Such operational commands may include a change in the speed of the autonomous vehicle  100 , a change in steering direction, a change in gear, or other command as can be appreciated. For example, the automation module  220  may provide sensor data and/or processed sensor data as one or more inputs to a trained machine learning model (e.g., a trained neural network) to determine the one or more operational commands. The operational commands may then be communicated to autonomous vehicle control systems  223  via a vehicle interface  222 . 
     In some embodiments, the automation module  220  may be configured to determine an exit path for an autonomous vehicle  100  in motion. The exit path includes one or more operational commands that, if executed, are determined and/or predicted to bring the autonomous vehicle  100  safely to a stop (e.g., without collision with an object, without violating one or more safety rules). The automation module  220  may determine a both a driving decision and an exit path at a predefined interval. The automation module  220  may then send the driving decision and the exit path to the autonomous vehicle control systems  223 . The autonomous vehicle control systems  223  may be configured to execute the driving decision unless an error state has been reached. If an error decision has been reached, therefore indicating a possible error in functionality of the automation computing system  116 ), the autonomous vehicle control systems  223  may then execute a last received exit path in order to bring the autonomous vehicle  100  safely to a stop. Thus, the autonomous vehicle control systems  223  are configured to receive both a driving decision and exit path at predefined intervals, and execute the exit path in response to an error. 
     The autonomous vehicle control systems  223  are configured to affect the movement and operation of the autonomous vehicle  100 . For example, the autonomous vehicle control systems  223  may activate (e.g., apply one or more control signals) to actuators or other components to turn or otherwise change the direction of the autonomous vehicle  100 , accelerate or decelerate the autonomous vehicle  100 , change a gear of the autonomous vehicle  100 , or otherwise affect the movement and operation of the autonomous vehicle  100 . 
     Further stored in RAM  206  is a data collection module  224  configured to process and/or store sensor data received from the one or more sensors  212 . For example, the data collection module  224  may store the sensor data as captured by the one or more sensors  212 , or processed sensor  212  data (e.g., sensor  212  data having object recognition, compression, depth filtering, or other processes applied). Such processing may be performed by the data collection module  224  in real-time or in substantially real-time as the sensor data is captured by the one or more sensors  212 . The processed sensor data may then be used by other functions or modules. For example, the automation module  220  may use processed sensor data as input to determine one or more operational commands. The data collection module  224  may store the sensor data in data storage  218 . 
     Also stored in RAM  206  is a data processing module  226 . The data processing module  226  is configured to perform one or more processes on stored sensor data (e.g., stored in data storage  218  by the data collection module  224 ) prior to upload to a execution environment  227 . Such operations can include filtering, compression, encoding, decoding, or other operations as can be appreciated. The data processing module  226  may then communicate the processed and stored sensor data to the execution environment  227 . 
     Further stored in RAM  206  is a hypervisor  228 . The hypervisor  228  is configured to manage the configuration and execution of one or more virtual machines  229 . For example, each virtual machine  229  may emulate and/or simulate the operation of a computer. Accordingly, each virtual machine  229  may comprise a guest operating system  216  for the simulated computer. The hypervisor  228  may manage the creation of a virtual machine  229  including installation of the guest operating system  216 . The hypervisor  228  may also manage when execution of a virtual machine  229  begins, is suspended, is resumed, or is terminated. The hypervisor  228  may also control access to computational resources (e.g., processing resources, memory resources, device resources) by each of the virtual machines. 
     Each of the virtual machines  229  may be configured to execute one or more of the automation module  220 , the data collection module  224 , the data processing module  226 , or combinations thereof. Moreover, as is set forth above, each of the virtual machines  229  may comprise its own guest operating system  216 . Guest operating systems  216  useful in autonomous vehicles in accordance with some embodiments of the present disclosure include UNIX™, Linux™, Microsoft Windows™, AIX™, IBM&#39;s i OS™, and others as will occur to those of skill in the art. For example, the autonomous vehicle  100  may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or even partially autonomous) driving mode and the autonomous vehicle  100  may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or even partially autonomous) driving mode. In such an example, the first operating system may be formally verified, secure, and operate in real-time such that data collected from the sensors  212  are processed within a predetermined period of time, and autonomous driving operations are performed within a predetermined period of time, such that data is processed and acted upon essentially in real-time. Continuing with this example, the second operating system may not be formally verified, may be less secure, and may not operate in real-time as the tasks that are carried out (which are described in greater detail below) by the second operating system are not as time-sensitive the tasks (e.g., carrying out self-driving operations) performed by the first operating system. 
     Readers will appreciate that although the example included in the preceding paragraph relates to an embodiment where the autonomous vehicle  100  may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or even partially autonomous) driving mode and the autonomous vehicle  100  may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or even partially autonomous) driving mode, other embodiments are within the scope of the present disclosure. For example, in another embodiment one CPU (or other appropriate entity such as a chip, CPU core, and so on) may be executing the first operating system and a second CPU (or other appropriate entity) may be executing the second operating system, where switching between these two modalities is accomplished through fabric switching, as described in greater detail below. Likewise, in some embodiments, processing resources such as a CPU may be partitioned where a first partition supports the execution of the first operating system and a second partition supports the execution of the second operating system. 
     The guest operating systems  216  may correspond to a particular operating system modality. An operating system modality is a set of parameters or constraints which a given operating system satisfies, and are not satisfied by operating systems of another modality. For example, a given operating system may be considered a “real-time operating system” in that one or more processes executed by the operating system must be performed according to one or more time constraints. For example, as the automation module  220  must make determinations as to operational commands to facilitate autonomous operation of a vehicle. Accordingly, the automation module  220  must make such determinations within one or more time constraints in order for autonomous operation to be performed in real time. The automation module  220  may then be executed in an operating system (e.g., a guest operating system  216  of a virtual machine  229 ) corresponding to a “real-time operating system” modality. Conversely, the data processing module  226  may be able to perform its processing of sensor data independent of any time constrains, and may then be executed in an operating system (e.g., a guest operating system  216  of a virtual machine  229 ) corresponding to a “non-real-time operating system” modality. 
     As another example, an operating system (e.g., a guest operating system  216  of a virtual machine  229 ) may comprise a formally verified operating system. A formally verified operating system is an operating system for which the correctness of each function and operation has been verified with respect to a formal specification according to formal proofs. A formally verified operating system and an unverified operating system (e.g., one that has not been formally verified according to these proofs) can be said to operate in different modalities. 
     The automation module  220 , data collection module  224 , data collection module  224 , data processing module  226 , hypervisor  228 , and virtual machine  229  in the example of  FIG. 2  are shown in RAM  206 , but many components of such software typically are stored in non-volatile memory also, such as, for example, on data storage  218 , such as a disk drive. Moreover, any of the automation module  220 , data collection module  224 , and data processing module  226  may be executed in a virtual machine  229  and facilitated by a guest operating system  216  of that virtual machine  229 . 
     The automation computing system  116  of  FIG. 2  includes disk drive adapter  230  coupled through expansion bus  232  and bus adapter  210  to CPU package(s)  204  and other components of the automation computing system  116 . Disk drive adapter  230  connects non-volatile data storage to the automation computing system  116  in the form of data storage  218 . Disk drive adapters  230  useful in computers configured for scheduled data transfer according to embodiments of the present invention include Integrated Drive Electronics (‘IDE’) adapters, Small Computer System Interface (‘SCSI’) adapters, and others as will occur to those of skill in the art. Non-volatile computer memory also may be implemented for as an optical disk drive, electrically erasable programmable read-only memory (so-called ‘EEPROM’ or ‘Flash’ memory), RAM drives, and so on, as will occur to those of skill in the art. 
     The exemplary automation computing system  116  of  FIG. 2  includes a communications adapter  238  for data communications with other computers and for data communications with a data communications network. Such data communications may be carried out serially through RS-238 connections, through external buses such as a Universal Serial Bus (‘USB’), through data communications networks such as IP data communications networks, and in other ways as will occur to those of skill in the art. Communications adapters implement the hardware level of data communications through which one computer sends data communications to another computer, directly or through a data communications network. Examples of communications adapters useful in computers configured for scheduled data transfer according to embodiments of the present invention include modems for wired dial-up communications, Ethernet (IEEE 802.3) adapters for wired data communications, 802.11 adapters for wireless data communications, as well as mobile adapters (e.g., cellular communications adapters) for mobile data communications. For example, the automation computing system  116  may communicate with one or more remotely disposed execution environments  227  via the communications adapter  238 . 
     The exemplary automation computing system of  FIG. 2  also includes one or more Artificial Intelligence (AI) accelerators  240 . The AI accelerator  240  provides hardware-based assistance and acceleration of AI-related functions, including machine learning, computer vision, etc. Accordingly, performance of any of the automation module  220 , data collection module  224 , data processing module  226 , or other operations of the automation computing system  116  may be performed at least in part by the AI accelerators  240 . 
     The exemplary automation computing system of  FIG. 2  also includes one or more graphics processing units (GPUs)  242 . The GPUs  242  are configured to provide additional processing and memory resources for processing image and/or video data, including encoding, decoding, etc. Accordingly, performance of any of the automation module  220 , data collection module  224 , data processing module  226 , or other operations of the automation computing system  116  may be performed at least in part by the GPUs  242 . 
       FIG. 3  shows an example redundant power fabric for scheduled data transfer. The redundant power fabric provides redundant pathways for power transfer between the power supplies  215 , the sensors  212 , and the CPU packages  204 . In this example, the power supplies  215  are coupled to the sensors  212  and CPU packages via two switched fabrics  214   a  and  214   b . The topology shown in  FIG. 3  provides redundant pathways between the power supplies  215 , the sensors  212 , and the CPU packages  204  such that power can be rerouted through any of multiple pathways in the event of a failure in an active connection pathway. The switched fabrics  214   a  and  214   b  may provide power to the sensors  212  using various connections, including Mobile Industry Processor Interface (MIPI), Inter-Integrated Circuit (I2C), Universal Serial Bus (USB), or another connection. The switched fabrics  214   a  and  214   b  may also provide power to the CPU packages  204  using various connections, including Peripheral Component Interconnect Express (PCIe), USB, or other connections. Although only two switched fabrics  214   a  and  214   b  are shown connecting the power supplies  215  to the sensors  212  and CPU packages  204 , it is understood that the approach shown by  FIG. 3  can be modified to include additional switched fabrics  214 . 
       FIG. 4  is an example redundant data fabric for scheduled data transfer. The redundant data fabric provides redundant data connection pathways between sensors  212  and CPU packages  204 . In this example view, three CPU packages  204   a ,  204   b , and  204   c  are connected to three sensors  212   a ,  212   b , and  212   c  via three switched fabrics  213   a ,  213   b , and  213   c . Each CPU package  204   a ,  204   b , and  204   c  is connected to a subset of the switched fabrics  213   a ,  213   b , and  213   c . For example, CPU package  204   a  is connected to switched fabrics  213   a  and  213   c , CPU package  204   b  is connected to switched fabrics  213   a  and  213   b , and CPU package  204   c  is connected to switched fabrics  213   b  and  213   c . Each switched fabric  213   a ,  213   b , and  213   c  is connected to a subset of the sensors  212   a ,  212   b , and  212   c . For example, switched fabric  213   a  is connected to sensors  212   a  and  212   b , switched fabric  213   b  is connected to sensor  212   b  and  212   c , and switched fabric  213   c  is connected to sensors  212   a  and  212   c . Under this topology, each CPU package  204   a ,  204   b , and  204   c  has an available connection path to any sensor  212   a ,  212   b , and  212   c . It is understood that the topology of  FIG. 4  is exemplary, and that CPU packages, switched fabrics, sensors, or connections between components may be added or removed while maintaining redundancy as can be appreciated by one skilled in the art. 
       FIG. 5  is an example view of process allocation across CPU packages for scheduled data transfer. Shown are three CPU packages  204   a ,  204   b , and  204   c . Each CPU package  204   a  includes a processing unit that has been allocated (e.g., by a hypervisor  228  or other process or service) primary execution of a process and another processing unit that has been allocated secondary execution of a process. As set forth herein, primary execution of a process describes an executing instance of a process whose output will be provided to another process or service. Secondary execution of the process describes executing an instance of the process in parallel to the primary execution, but the output may not be output to the other process or service. For example, in CPU package  204   a , processing unit  502   a  has been allocated secondary execution of “process B,” denoted as secondary process B  504   b , while processing unit  502   b  has been allocated primary execution of “process C,” denoted as primary process C  506   a.    
     CPU package  204   a  also comprises two redundant processing units that are not actively executing a process A, B, or C, but are instead reserved in case of failure of an active processing unit. Redundant processing unit  508   a  has been reserved as “A/B redundant,” indicating that reserved processing unit  508   a  may be allocated primary or secondary execution of processes A or B in the event of a failure of a processing unit allocated the primary or secondary execution of these processes. Redundant processing unit  508   b  has been reserved as “A/C redundant,” indicating that reserved processing unit  508   b  may be allocated primary or secondary execution of processes A or C in the event of a failure of a processing unit allocated the primary or secondary execution of these processes. 
     CPU package  204   b  includes processing unit  502   c , which has been allocated primary execution of “process A,” denoted as primary process A  510   a , and processing unit  502   d , which has been allocated secondary execution of “process C,” denoted as secondary process C  506   a . CPU package  204   b  also includes redundant processing unit  508   c , reserved as “A/B redundant,” and redundant processing unit  508   d , reserved as “B/C redundant.” CPU package  204   c  includes processing unit  502   e , which has been allocated primary execution of “process B,” denoted as primary process B  504   a , and processing unit  502   f , which has been allocated secondary execution of “process A,” denoted as secondary process A  510   b . CPU package  204   c  also includes redundant processing unit  508   e , reserved as “B/C redundant,” and redundant processing unit  508   f , reserved as “A/C redundant.” 
     As set forth in the example view of  FIG. 5 , primary and secondary instances processes A, B, and C are each executed in an allocated processing unit. Thus, if a processing unit performing primary execution of a given process fails, the processing unit performing secondary execution may instead provide output of the given process to a receiving process or service. Moreover, the primary and secondary execution of a given process are executed on different CPU packages. Thus, if an entire processing unit fails, execution of each of the processes can continue using one or more processing units handling secondary execution. The redundant processing units  508   a - f  allow for allocation of primary or secondary execution of a process in the event of processing unit failure. This further prevents errors caused by processing unit failure as parallel primary and secondary execution of a process may be restored. One skilled in the art would understand that the number of CPU packages, processing units, redundant processing units, and processes may be modified according to performance requirements while maintaining redundancy. 
     For further explanation,  FIG. 6  sets forth a diagram of an execution environment  227  accordance with some embodiments of the present disclosure. The execution environment  227  depicted in  FIG. 6  may be embodied in a variety of different ways. The execution environment  227  may be provided, for example, by one or more physical or virtual machine components consisting of bare-metal applications, operating systems such as Android, Linux, Real-time Operating systems (RTOS), Automotive RTOS, such as AutoSAR, and others, including combinations thereof. The execution environment  227  may also be provided by cloud computing providers such as Amazon AWS, Microsoft Azure, Google Cloud, and others, including combinations thereof. Alternatively, the execution environment  227  may be embodied as a collection of devices (e.g., servers, storage devices, networking devices) and software resources that are included in a computer or distributed computer or private data center. Readers will appreciate that the execution environment  227  may be constructed in a variety of other ways and may even include resources within one or more autonomous vehicles or resources that communicate with one or more autonomous vehicles. 
     The execution environment  227  depicted in  FIG. 6  may include storage resources  608 , which may be embodied in many forms. For example, the storage resources  608  may include flash memory, hard disk drives, nano-RAM, 3D crosspoint non-volatile memory, MRAM, non-volatile phase-change memory (‘PCM’), storage class memory (‘SCM’), or many others, including combinations of the storage technologies described above. Readers will appreciate that other forms of computer memories and storage devices may be utilized as part of the execution environment  227 , including DRAM, SRAM, EEPROM, universal memory, and many others. The storage resources  608  may also be embodied, in embodiments where the execution environment  227  includes resources offered by a cloud provider, as cloud storage resources such as Amazon Elastic Block Storage (‘EBS’) block storage, Amazon S3 object storage, Amazon Elastic File System (‘EFS’) file storage, Azure Blob Storage, and many others. The example execution environment  227  depicted in  FIG. 6  may implement a variety of storage architectures, such as block storage where data is stored in blocks, and each block essentially acts as an individual hard drive, object storage where data is managed as objects, or file storage in which data is stored in a hierarchical structure. Such data may be saved in files and folders, and presented to both the system storing it and the system retrieving it in the same format. 
     The execution environment  227  depicted in  FIG. 6  also includes communications resources  610  that may be useful in facilitating data communications between components within the execution environment  227 , as well as data communications between the execution environment  227  and computing devices that are outside of the execution environment  227 . Such communications resources may be embodied, for example, as one or more routers, network switches, communications adapters, and many others, including combinations of such devices. The communications resources  610  may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications. For example, the communications resources  610  may utilize Internet Protocol (‘IP’) based technologies, fibre channel (‘FC’) technologies, FC over ethernet (‘FCoE’) technologies, InfiniBand (‘IB’) technologies, NVM Express (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologies, and many others. The communications resources  610  may also be embodied, in embodiments where the execution environment  227  includes resources offered by a cloud provider, as networking tools and resources that enable secure connections to the cloud as well as tools and resources (e.g., network interfaces, routing tables, gateways) to configure networking resources in a virtual private cloud. Such communications resources may be useful in facilitating data communications between components within the execution environment  227 , as well as data communications between the execution environment  227  and computing devices that are outside of the execution environment  227  (e.g., computing devices that are included within an autonomous vehicle). 
     The execution environment  227  depicted in  FIG. 6  also includes processing resources  612  that may be useful in useful in executing computer program instructions and performing other computational tasks within the execution environment  227 . The processing resources  612  may include one or more application-specific integrated circuits (‘ASICs’) that are customized for some particular purpose, one or more central processing units (‘CPUs’), one or more digital signal processors (‘DSPs’), one or more field-programmable gate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or other form of processing resources  612 . The processing resources  612  may also be embodied, in embodiments where the execution environment  227  includes resources offered by a cloud provider, as cloud computing resources such as one or more Amazon Elastic Compute Cloud (‘EC2’) instances, event-driven compute resources such as AWS Lambdas, Azure Virtual Machines, or many others. 
     The execution environment  227  depicted in  FIG. 6  also includes software resources  613  that, when executed by processing resources  612  within the execution environment  227 , may perform various tasks. The software resources  613  may include, for example, one or more modules of computer program instructions that when executed by processing resources  612  within the execution environment  227  are useful in generating data transfer schedules facilitating scheduled data transfer within various nodes of the autonomous vehicle. For example, a scheduling module  614  may generate data transfer schedules based on particular autonomous vehicle configurations, calculation dependencies, and other criteria as will be described in further detail below. 
     The software resources  613  may include, for example, one or more modules of computer program instructions that when executed by processing resources  612  within the execution environment  227  are useful in deploying software resources or other data to autonomous vehicles  100  via a network  618 . For example, a deployment module  616  may provide software updates, neural network updates, or other data to autonomous vehicles  100  to facilitate autonomous vehicle control operations. 
     For further explanation,  FIG. 7  sets forth a flow chart illustrating an exemplary method for scheduled data transfer that includes determining  702  (e.g., by an automation computing system  116 ), based on a data transfer schedule for a plurality of nodes, a time to transfer data from a first node to a second node. The nodes may comprise CPU packages  204  or other distributed components of the automation computing system  116 . The nodes may also comprise sensors  212  in communication with the automation computing system  116 . As described herein, in some embodiments, a transfer of data from a first node to a second node may include a copying of data from memory of the first node to memory of the second node. Memory may be considered “of” a given node where the memory is included in the hardware of the node (e.g., memory within a sensor  212 ), allocated for use by the given node as part of shared memory, or otherwise dedicated to the use of a particular node. 
     The data transfer schedule specifies a time at which a particular node of a plurality of nodes is allowed to transfer data. The data transfer schedule may be encoded as a lookup table or otherwise encoded. In some embodiments, the data transfer schedule specifies one or more times at which the particular node is allowed to perform a data transfer to a specified other node. In other embodiments, the data transfer schedule specifies one or more times at which the particular node is allowed to perform any data transfers independent of the receiving node. One skilled in the art will appreciate that, in some embodiments, the nodes are restricted from performing data transfers violating their allocated times in the data transfer schedule unless overridden by some other policy or exception. The data transfer schedule may be generated according to various criteria as will be described in further detail below. Such criteria may include a frequency at which a particular sending or receiving node performs a particular computation, a time for a particular sending or receiving node to perform a particular computation, one or more computation dependencies for the plurality of nodes, or an amount of load on transfer media between the plurality of nodes. 
     In some embodiments, the time at which a node is allowed to perform a data transfer is encoded as a specific time of day (e.g., a specific hour, minute, second, fraction of a second, etc.). Accordingly, determining  702  the time to transfer the data includes identifying, within the data transfer schedule, the specific time of day at which the desired data transfer is allowable. In other embodiments, the time at which a node is allowed to perform a data transfer is encoded relative to a repeating or rolling counter or time window. For example, assume that the data transfer schedule describes all data transfers allowable within a rolling one second time window. Thus, determining  702  the time to transfer the data includes determining a next occurring time that the transfer may be performed (e.g., in the current time window, the next time window, etc.). In further embodiments, the time at which a node is allowed to perform a data transfer is encoded as a particular frequency or time interval. For example, the data transfer schedule may indicate that a particular node may transfer data to another node every N milliseconds. Accordingly, in such an embodiment, the particular intervals at which the nodes are scheduled to transfer data may be of different lengths and staggered (e.g., beginning at different offsets) to reduce or eliminate overlap in data transfer as desired. In embodiments where the data transfer schedule indicates times for data transfer using intervals or rolling time windows the plurality of nodes may be synchronized and periodically resynchronized to a clock or counter to ensure that all data transfers comply with the data transfer schedule. 
     In some embodiments, determining  702  a time to transfer data from a first node to a second node is performed by the first node. For example, the first node has stored or has access to the data transfer schedule and queries the data transfer schedule to determine  702  the time to perform a particular data transfer. Accordingly, in some embodiments, each node of the plurality of nodes has stored or has access to the entire data transfer schedule. In other embodiments each node of the plurality of nodes has stored or has access to a subset of the data transfer schedule. For example, in some embodiments, each given node has stored or has access to a portion of the data transfer that indicates the permissible data transfer times for that given node. 
     In other embodiments, determining  702  the time to transfer the data is performed by a centralized entity implemented in the automation computing system  116  and configured to manage the data transfers between the plurality of nodes. Such an entity is hereinafter referred to as a copy engine. For example, the copy engine may include a remote direct memory access (RDMA) engine. In some embodiments, a copy engine is configured to manage the data transfers between all nodes of the plurality of nodes. Accordingly, such a copy engine stores or has access to the entire data transfer schedule in order to make such a determination. In other embodiments, the copy engine is one of a plurality of copy engines each configured to manage the data transfers performed by a particular subset of nodes. Accordingly, each copy engine may have stored or access to the entire data transfer schedule, or a portion of the data transfer schedule applicable to the particular nodes managed by a given copy engine. 
     The method of  FIG. 7  also includes transferring  704 , at the determined time, data from the first node to the second node. In some embodiments, transferring  704  data from the first node to the second node includes performing an RDMA copy from memory of the first node to memory of the second node. In other embodiments, transferring  704  data from the first node to the second node includes transferring  704  the data via a network fabric connecting the first node to the second node. 
     In some embodiments, transferring  704  the data from the first node to the second node is initiated by the first node. For example, the first node  702  determines the time for transferring the data based on at least a portion of the data transfer schedule accessible to the first node and then transfers  704  the data at the determined time. In other embodiments, transferring  704  the data from the first node to the second node is facilitated by a copy engine. For example, in some embodiments, transferring  704  the data includes indicating, by the copy engine, to the first node, a time determined by the copy engine. In other embodiments, transferring  704  the data includes sending, by the copy engine at the determined time, to the first node, an indication for the first node to transfer the data to the second node. In another embodiment, transferring  704  the data includes the copy engine performing an RDMA copy of the data from memory of the first node to memory or the second node. One skilled in the art that other approaches for transferring the data from the first node to the second node may also be used. 
     As each node of the plurality of nodes performs their required data transfers using the data transfer schedule, the stability and performance of a real-time computing system such as the automation computing system  116  is improved. For example, the likelihood of overburdening memory bandwidth or conflicting memory accesses is reduced due to a controlled schedule for data transfers. Moreover, the likelihood of overburdening the memory bandwidth or conflicting access is reduced because such transfers are now predictable and deterministic. Moreover, the transfer schedule may be optimized and computed relative to other transfers. As each node in the real-time system uses finite memory and data transfer resources, the use of scheduled data transfer ensures that each consumer node may receive, from their respective producer nodes, the data required for their computations. 
     For further explanation,  FIG. 8  sets forth a flow chart illustrating an exemplary method for scheduled data transfer that includes determining  702  (e.g., by an automation computing system  116 ), based on a data transfer schedule for a plurality of nodes, a time to transfer data from a first node to a second node; and transferring  704 , at the determined time, data from the first node to the second node. 
     Assume that the data transfer includes first validation data indicating a validity of a data payload and second validation data indicating validity of a transfer of the data. The method of  FIG. 8  differs from  FIG. 7  in that transferring  704 , at the determined time, data from the first node to the second node includes transferring  802  the first validation data and the second validation data as a last transferred portion of the data. 
     The first validation data indicates whether a data payload included in the transfer is valid. The data payload is considered valid if it is free from corruption, data loss, or other faults that may occur during data payload generation, storage, or transfer. Accordingly, the first validation data may include parity bits, cyclic redundancy checks (CRCs), hash values, signatures, or other data that may be used to validate the integrity of a data payload. The second validation data indicates validity of the transfer of the data in that the second validation data indicates that the appropriate data transmission was received at the appropriate time and/or from the appropriate source. For example, the second validation data may include an identifier of the first node or a process associated with the first node that generated the data payload. The second validation data may also include a timestamp or sequence identifier generated by the first node. As the timestamp or sequence identifier are incremented at predictable intervals, the second node may validate that the correct transmission of data was received based on the second validation data. The first validation data and second validation data are transferred last so that they may indicate the end of the data transfer to the second node. As the remainder of the data payload has been received, the second node may then validate the data payload using the first validation data and validate the transfer of the data using the second validation data. 
     Where either the first validation data or the second validation data fail their respective validation, the second node may perform one or more remedial actions. For example, in embodiments where the first node and second node are included in a real-time system such as the automation computing system  116 , the time constraints associated with real-time computations may prevent the first node from retransferring the data to the second node. Accordingly, in some embodiments, the second node may effectively “skip” any actions that were to be performed on the transferred data and resume such actions when data is later transferred by the first node (e.g., at a subsequent interval or time window). For example, the second node may provide an indication to any nodes dependent on computations of the second node that valid data was not received from the first node, thereby cascading such indications throughout any chain of dependent nodes. In other embodiments, the second node may perform computations based on last received data from the first node (e.g., last received valid data at a previous time interval). The second node may also provide previously generated results based on the last received data to any dependent nodes. In some embodiments, the second node may send an indication to the automation computing system  116  to switch to another data source (e.g., replace the first node transferring data to the second node with another node). For example, assuming that the first node is a sensor  212  and the second node is a CPU package  204  receiving sensor data, the second node may indicate to the automation computing system  116  that another sensor  212  in the same sensing space as the first node should instead provide data to the second node. 
     For further explanation,  FIG. 9  sets forth a flow chart illustrating an exemplary method for scheduled data transfer that includes determining  702  (e.g., by an automation computing system  116 ), based on a data transfer schedule for a plurality of nodes, a time to transfer data from a first node to a second node; and transferring  704 , at the determined time, data from the first node to the second node. 
     The method of  FIG. 9  differs from  FIG. 7  in that the method of  FIG. 9  includes generating  902  the data transfer schedule. In some embodiments, the data transfer schedule is generated  902  by the automation computing system  116 . In other embodiments, the data transfer schedule is generated by an execution environment  227  and provided to the automation computing system  116 . 
     In some embodiments, the data transfer schedule is generated  902  based on a particular configuration of an autonomous vehicle  100 . For example, the data transfer schedule is generated  902  based on a particular hardware configuration of an automation computing system  116  and a sensor  212  package installed on the autonomous vehicle  100  such that any nodes in the autonomous vehicle  100  are included in the data transfer schedule. 
     In some embodiments, the data transfer schedule is generated based on one or more computation frequencies. Computation frequencies describe a frequency at which a given node performs a computation that generates data to be transferred to another node. In some embodiments, the data transfer schedule is generated based on one or more estimated computation times. As an example, assume that a given node performs a calculation every N milliseconds and that such a calculation takes M milliseconds to complete. Accordingly, the data transfer schedule may indicate that the given node is scheduled to transfer data to a consumer node at N+M milliseconds, and then every subsequent N milliseconds. 
     The data transfer schedule may also be generated based on one or more overlapping or conflicting data transfers. Continuing with the example above, assume that another data transfer in the data transfer schedule will be using memory or other hardware resources that would conflict with the above data transfer. Thus, the above data transfer may be delayed by C milliseconds to allow the conflicting data transfer to resolve. Thus, the above data transfer will begin at N+M+C milliseconds, and then every subsequent N milliseconds. 
     The data transfer schedule may also be generated based on one or more computation dependencies. For example, assume a first sensor node generates data for a second CPU package node executing a model, and that the second CPU package provides the output of the model to another CPU package node executing another model. Assume that the first sensor node takes M 1  milliseconds to generate sensor data for providing to the second CPU package node. Assume that the second CPU package node can generate the model output in M 2  milliseconds. As the second node is dependent on the first node and the third node is dependent on the second node, the data transfer schedule must be generated to account for these dependencies. For example, the data transfer schedule may be generated such that the first node transfers data to the second node at M1 milliseconds and the second node transfers its data to the third node at M 1 +M 2  milliseconds. One skilled in the art that the data transfer schedule may be further configured to reflect a frequency (e.g., an interval) at which such data transfers should be repeated. 
     In view of the explanations set forth above, readers will recognize that the benefits of scheduled data transfer according to embodiments of the present invention include:
         Improved performance of a real-time computing system by ensuring that data transfers occur according to a predefined schedule, avoiding performance degradation caused by uncontrolled data transfers.       

     Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for scheduled data transfer. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     It will be understood that any of the functionality or approaches set forth herein may be facilitated at least in part by artificial intelligence applications, including machine learning applications, big data analytics applications, deep learning, and other techniques. Applications of such techniques may include: machine and vehicular object detection, identification and avoidance; visual recognition, classification and tagging; algorithmic financial trading strategy performance management; simultaneous localization and mapping; predictive maintenance of high-value machinery; prevention against cyber security threats, expertise automation; image recognition and classification; question answering; robotics; text analytics (extraction, classification) and text generation and translation; and many others. 
     It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims.