Patent Publication Number: US-2022214903-A1

Title: Method for virtual machine migration with artificial intelligence accelerator status validation in virtualization environment

Description:
TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to one or more artificial intelligence accelerators coupled to a host of a virtual machine. More particularly, embodiments of the disclosure relate to migrating a virtual machine that uses the artificial accelerator. 
     BACKGROUND 
     Artificial intelligence (AI) models (also termed, “machine learning models”) have been widely utilized recently as AI technology has been deployed in a variety of fields such as image classification, medical diagnosis, or autonomous driving. Similar to an executable image or binary image of a software application, an AI model, when trained, can perform an inference based on a set of attributes to classify as features. Training of an AI model can require a substantial investment in collecting, collating, and filtering of data to generate an AI model that yields useful predictions. In addition, the predictions resulting from use of an AI model may contain personal, sensitive data that users would want protected. 
     Generating a prediction from an AI model can be a computationally intensive process. To provide adequate computational power for one or more users, one or more AI accelerators can be coupled to a host machine of one or more virtual machines. To provide sufficient computing power for a computationally intensive task, such as training an AI model, AI accelerators may be organized in a cluster, and then organized into a plurality of groups, and each group may be assigned to a single virtual machine. For less intensive tasks, a single virtual machine may have a single AI accelerator assigned to it. 
     For several well-known reasons, a virtual machine may need to be migrated to a different host. Virtual machine migration of the prior art does not protect the status of the one or more AI accelerators during migration. An AI application that generates one or more artificial intelligence tasks, at least some of which are performed on the AI accelerators, may fail or be interrupted after migration to another host. Failures can include failure to capture the configuration, memory content, and computing state of the AI accelerator and failure to capture a computing state of AI tasks within the VM. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the disclosure are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements. 
         FIG. 1  is a block diagram illustrating a secure processing system that can migrate a virtual machine with checkpoint authentication and/or artificial intelligence (AI) accelerator status validation, according to one embodiment. 
         FIGS. 2A and 2B  are a block diagrams illustrating a secure computing environment between one or more hosts and one or more artificial intelligence accelerators, according to one embodiment. 
         FIG. 3  is a block diagram illustrating a host controlling a cluster of artificial intelligence accelerators, each cluster having a virtual function that maps the resources of a group of AI accelerators within the cluster to a virtual machine, each artificial intelligence accelerator having secure resources and non-secure resources, according to an embodiment. 
         FIG. 4A  is a block diagram illustrating components of a data processing system having artificial intelligence (AI) accelerators to implement a method for virtual machine migration with checkpoint authentication in a virtualized environment, according to an embodiment. 
         FIG. 4B  is a block diagram illustrating components of a data processing system having artificial intelligence (AI) accelerators to implement a method for virtual machine migration with AI accelerator status validation in a virtualized environment, according to an embodiment. 
         FIG. 5A  illustrates a method for virtual machine migration of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, from the perspective of the hypervisor of a host of a source virtual machine to be migrated, according to an embodiment. 
         FIG. 5B  illustrates a method for virtual machine migration of a data processing system having AI accelerators using AI accelerator status validation in a virtualized environment, from the perspective of a hypervisor of a host of a source virtual machine to be migrated, according to an embodiment. 
         FIG. 6  illustrates a method of generating a checkpoint for use in a method for virtual machine migration with checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor on the host of the virtual machine to be migrated, according to an embodiment. 
         FIG. 7  illustrates a method of determining whether to a migrate virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. 
         FIG. 8  illustrates a method of migrating a virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. 
         FIG. 9  illustrates a method of performing post-migration clean-up of a source host computing device after migrating a virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, according to an embodiment. 
         FIG. 10  illustrates a method of migrating a virtual machine of a data processing system having AI accelerators with checkpoint authentication, and optionally AI accelerator status validation, in a virtualized environment, from the perspective of a target hypervisor on a host to the migrated virtual machine, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments and aspects of the disclosures will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present disclosures. 
     Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment. 
     The following embodiments relate to usage of an artificial intelligence (AI) accelerator to increase processing throughput of certain types of operations that may be offloaded (or delegated) from a host device to the AI accelerator. The host device hosts one or more virtual machines (VM). At least one VM on the host can be associated with a virtual function that maps resources of an AI accelerator to the VM via the virtual function. The virtual function enumerates the resources within the AI accelerator that are mapped to the VM and the configuration, within the accelerator, of those resources. A driver within the VM can track scheduling and computing status of tasks that are to be processed by the AI accelerator. The driver can also obtain the code, data, and memory of the AI accelerator mapped to the VM. 
     A “virtual function,” as used herein, is a mapping of a set of resources within an artificial intelligence (AI) accelerator or a group of AI accelerators in a cluster of AI accelerators to one virtual machine. The set of resources is referred to herein, individually and collectively, as “AI resources.” An AI accelerator or cluster of AI accelerators is referred to herein as an “AI accelerator” unless a distinction is being described between one AI accelerator and a cluster of AI accelerators. 
     An AI accelerator can be a general-purpose processing unit (GPU), an artificial intelligence (AI) accelerator, math coprocessor, digital signal processor (DSP), or other type of processor. An AI accelerator can be a proprietary design, such as a Baidu® AI accelerator, or another GPU, and the like. While embodiments are illustrated and described with host device securely coupled to one or more AI accelerators, the concepts described herein can be implemented more generally as a distributed processing system. 
     A plurality of AI accelerators can be linked in a cluster that is managed by a host device having a driver that translates application program processing requests into processing tasks for one or more of the AI accelerators. The host device may support one or more virtual machines (VMs) each having a user associated with the corresponding VM. The driver can implement the virtual function that maps resources of the AI accelerator to the VM. The driver can include a scheduler that schedules application processing requests from a plurality of VMs for processing by one or more of the AI accelerators. In one embodiment, the driver can analyze processing requests in the scheduler to determine how to group the one or more AI accelerators in the cluster, and whether to instruct one or more AI accelerators to unlink from a group and go into a low-power state to reduce heat and conserve energy. 
     The host device and the AI accelerator can be interconnected via a high-speed bus, such as a peripheral component interconnect express (PCIe), or other high-speed bus. The host device and AI accelerator can exchange keys and initiate a secure channel over the PCIe bus before performing operations of the aspects of the invention described below. Some of the operations include the AI accelerator using an artificial intelligence (AI) model to perform inferences using data provided by the host device. Before the AI model inferences are trusted by the host device, the host device can engage the AI accelerator to perform one or more validation tests, described below, including determining a watermark of the AI model. In some embodiments and operations, the AI accelerator is not aware that the host device is testing the validity of results produced by the AI accelerator. 
     A host device may include central processing unit (CPU) coupled to one or more AI accelerators. Each AI accelerator may be coupled to the CPU over a bus or interconnect. An AI accelerator may be implemented in a form of an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) device, or other forms of integrated circuits (ICs). Alternatively, the host processor may be a part of a primary data processing system while an AI accelerator may be one of many distributed systems as secondary systems that the primary system can offload its data processing tasks remotely over a network (e.g., cloud computing systems such as a software as a service (SaaS) system, or a platform as a service or (PaaS) system). A link between a host processor and an AI accelerator may be a peripheral component interconnect express (PCIe) link or a network connection such as Ethernet connection. Each AI accelerator can include one or more link registers that enable (link up) or disable (link down) a communication link with another AI accelerator. 
     In a first aspect, a computer-implemented method of migrating a source virtual machine (VM-S) that is executing an application that accesses a virtual function of an artificial intelligence (AI) accelerator can include storing a checkpoint of a state of the VM-S into a storage of a plurality of states of the VM-S. Each state of the VM-S can include a state of the resources of the VM-S, a state of the application, and a state of the virtual function of the AI accelerator that maps AI resources to the VM-S. In response to receiving a command to migrate the VM-S and the virtual function, and receiving a selection of a checkpoint of a state of the VM-S to use in performing the migration, the method can further include recording, then stopping, one or more executing AI tasks, and migrating the VM-S, application, the one or more AI tasks, and the virtual function to a target VM (VM-T) on a target host, using selected checkpoint. In response to receiving a notification from the target hypervisor that the checkpoint was successfully validated by the target hypervisor, and that the migration was successfully completed, the recorded one or more AI tasks and the application can be restarted on the VM-T. The virtual function maps resources of the AI accelerator to the VM-S, and a user of the VM-S is the only user that can access the resources of the AI accelerator whose resources are mapped by the virtual function to the VM-S. In an embodiment, the virtual function maps resources of a plurality of AI accelerators to the VM-S, and the checkpoint includes a communication configuration between the plurality of AI accelerators, and the user of the VM-S is the only user who can access the resources of the plurality of AI accelerators mapped by the virtual function to the VM-S. In an embodiment, the method further includes, receiving a notification from the target hypervisor that the migrating of the VM-S is complete and the one or more recorded AI tasks have been successfully restarted. In response to the notification, a post-migration clean-up of the VM-S can be performed. The post-migration clean-up can include erasing at least the secure memory of the AI accelerator, including any AI inferences, AI models, secure computations, or portions thereof, and erasing the memory of the VM-S associated with the AI virtual function, and any calls to the virtual function by the application. Validating the signature and freshness date of the checkpoint can include decrypting the signature of the checkpoint using a public key of the VM-S, determining that a date and time stamp of the checkpoint is within a threshold date and time range, and validating a hash of the checkpoint of the VM-S. In an embodiment, the checkpoint can include a recording of one or more executing AI tasks, configuration information of resources within the one or more AI accelerators communicatively coupled to the VM-S, a date and time stamp of the checkpoint, and a snapshot of memory of the VM-S, including the virtual function, scheduling information, and communication buffers within the one or more AI accelerators. 
     In a second aspect, a method of migrating a source virtual machine (VM-S) that is executing an application that accesses a virtual function (VF) of an artificial intelligence (AI) accelerator includes receiving, by a hypervisor of a target host, a checkpoint from the source virtual machine (VM-S) associated with the virtual function (VF) that maps artificial intelligence (AI) processor resources to the VM-S, and receiving a request to host the VM-S as a target virtual machine (VM-T). The hypervisor of the target host allocates and configures resources for hosting VM-S and the VF of VM-S, as VM-T in accordance with the checkpoint. The hypervisor of the target host receives frames of data of the VM-S and stores the frames of data to generate the VM-T. The hypervisor of the target host receives a recorded state of unfinished AI tasks of the VM-S and restarts the unfinished AI tasks on VM-T. In an embodiment, validating the checkpoint of the VM-S and VF includes decrypting a signature of the checkpoint with a public key of the VM-S, determining that a date and time stamp of the checkpoint falls within a predetermined range, and recomputing a hash of the checkpoint and determining whether the recomputed hash matches the hash stored in the checkpoint. In response to successful validation of the checkpoint, migrating of the VM-S to the hypervisor of the target host proceeds, generating VM-T at the target host. 
     In a third aspect, a computer-implemented method of migrating a source virtual machine (VM-S) that is executing an application that accesses a virtual function of an artificial intelligence (AI) accelerator includes: in response to receiving a command to migrate the VM-S and the virtual function, and in response to receiving a selection of a checkpoint of the VM-S and virtual function to use in performing the migration, recording, then stopping, one or more executing AI tasks of the application. The method further includes generating, or selecting, a status of the AI accelerator associated with the virtual function, then transmitting the checkpoint and the status of the AI accelerator to a hypervisor of a target host to generate the migrated target virtual machine (VM-T). 
     In response to receiving a notification that the target host validated the checkpoint and AI accelerator status, and the target host has generated and configured resources for generating VM-T, the target host migrates the VM-S and virtual function to the VM-T. The migrating includes the target host loading the AI accelerator with data from the AI accelerator status frame. In an embodiment, the method further includes, in response to receiving a notification that the VM-T has restarted the application and AI tasks, performing a post-migration cleanup of the VM-S and virtual function. The post-migration clean-up at VM-S can include (1) erasing at least the secure memory of the AI accelerator, including any AI inferences, AI models, intermediate results of secure computations, or portions thereof; and (2) erasing the memory of the VM-S associated with the virtual function, and any calls to the virtual function by the application. In an embodiment, storing a checkpoint of a state of the VM-S and virtual function can include storing a checkpoint of a state of the VM-S and VF into a storage of a plurality of checkpoints of the VM-S. Each checkpoint of the VM-S can include a state of the resources of the VM-S, a state of the application, and a state of the virtual function associated with the resources of AI accelerator. In an embodiment, the checkpoint can further include a recording of one or more executing AI tasks, configuration information of resources within the AI accelerator that is communicatively coupled to the VM-S, a snapshot of memory of the VM-S. The checkpoint can further include the virtual function scheduling information and communication buffers within the one or more AI accelerators, and a date and time stamp of the checkpoint. In an embodiment, generating a status of the AI accelerator can include: (1) storing, in the AI accelerator status, a date and time stamp of the status, (2) storing, in the AI accelerator status, contents of memory within the AI accelerator, including one or more registers associated with a processor of the AI accelerator, and a cache, queue, or pipeline of pending instructions to be processed by the AI accelerator, and (3) generating a hash of the status of the AI accelerator, and digitally signing the status, hash, and date and time stamp. In an embodiment, the AI accelerator status can further include one or more register settings indicating one or more other AI accelerators in a cluster of AI accelerators that the AI accelerator is configured to communicate with. In an embodiment, validating the signature and freshness of the AI accelerator status can include decrypting the signature of the AI status using a public key of the VM-S, determining that a date and time stamp of the AI accelerator status is within a threshold date and time range, and validating a hash of the AI accelerator status. 
     Any of the above functionality can be programmed as executable instructions onto one or more non-transitory computer-readable media. When the executable instructions are executed by a processing system having at least one hardware processor, the processing systems causes the functionality to be implemented. Any of the above functionality can be implemented by a processing system having at least one hardware processor, coupled to a memory programmed with executable instructions that, when executed, cause the processing system to implement the functionality. 
       FIG. 1  is a block diagram illustrating a secure processing system  100  that can migrate a virtual machine with checkpoint authentication and/or artificial intelligence (AI) accelerator status validation, according to one embodiment. Referring to  FIG. 1 , system configuration  100  includes, but is not limited to, one or more client devices  101 - 102  communicatively coupled to a source data processing (DP) server  104 -S (e.g. host), and a target data DP server  104 -T, over network  103 . DP server  104 -S can host one or more clients. The one or more clients can be virtual machines. Any virtual machine on DP server  104 -S can be migrated to a target DP server  104 -T, as described herein. 
     Client devices  101 - 102  may be any type of client devices such as a personal computer (e.g., desktops, laptops, and tablets), a “thin” client, a personal digital assistant (PDA), a Web enabled appliance, a Smart watch, or a mobile phone (e.g., Smartphone), etc. Alternatively, client devices  101 - 102  may be virtual machines on a DP server  104 -S or  104 -T. Network  103  may be any type of network such as a local area network (LAN), a wide area network (WAN) such as the Internet, a high-speed bus, or a combination thereof, wired or wireless. 
     Server(s) (e.g., host)  104 -S and  104 -T (collectively, DP servers  104 , unless otherwise stated) may be any kind of servers or a cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. Server  104  further includes an interface (not shown) to allow a client such as client devices  101 - 102  to access resources or services (such as resources and services provided by AI accelerators via server  104 ) provided by server  104 . For example, server  104  may be a cloud server or a server of a data center that provides a variety of cloud services to clients, such as, for example, cloud storage, cloud computing services, artificial intelligence training services, data mining services, etc. Server  104  may be configured as a part of software-as-a-service (SaaS) or platform-as-a-service (PaaS) system over the cloud, which may be a private cloud, public cloud, or a hybrid cloud. The interface may include a Web interface, an application programming interface (API), and/or a command line interface (CLI). 
     For example, a client can be a user application of client device  101  (e.g., Web browser, an application). The client may send or transmit an instruction (e.g., AI training, AI inference instruction, etc.) for execution to server  104  and the instruction is received by server  104  via the interface over network  103 . In response to the instruction, server  104  communicates with AI accelerators  105 - 107  to fulfill the execution of the instruction. A source DP server  104 -S can be communicatively coupled to one or more AI accelerators. A client virtual machine hosted by DP server  104 -T that runs an application that uses one or more of AI accelerators  105 -T . . .  107 -T can be migrated to target DP server  104 -T to run on corresponding AI accelerators  105 -T . . .  107 -T. In some embodiments, the instruction is a machine learning type of instruction where AI accelerators, as dedicated machines or processors, can execute the instruction many times faster than execution by general purpose processor. Server  104  thus can control/manage an execution job for the one or more AI accelerators in a distributed fashion. Server  104  then returns an execution result to a client device  101 - 102  or a virtual machine on the server  104 . An AI accelerator may include one or more dedicated processors such as a Baidu® artificial intelligence (AI) chipset available from Baidu, Inc.® or alternatively, the AI accelerator may be an AI chipset from another AI chipset provider. 
     According to one embodiment, each of the applications accessing any of AI accelerators  105 -S . . .  107 -S or  105 -T . . .  107 -T (collectively,  105  . . .  107 , unless otherwise indicated) hosted by data processing server  104  (also referred to as a host) may verify that the application is provided by a trusted source or vendor. Each of the applications may be launched and executed within a user memory space and executed by a central processing unit (CPU) of host  104 . When an application is configured to access any one of the AI accelerators  105 - 107 , an obscured connection can be established between host  104  and the corresponding one of the AI accelerator  105 - 107 , such that the data exchanged between host  104  and AI accelerators  105 - 107  is protected against attacks from malware/intrusions. 
       FIG. 2A  is a block diagram illustrating a secure computing environment  200  between one or more hosts and one or more artificial intelligence (AI) accelerators, according to some embodiments. In one embodiment, system  200  provides a protection scheme for obscured communications between host  104  and AI accelerators  105 - 107  with or without hardware modifications to the AI accelerators  105 - 107 . Host machine or server  104  can be depicted as a system with one or more layers to be protected from intrusion such as user application(s)  205 , runtime libraries  206 , driver(s)  209 , operating system  211 , hypervisor  212 , and hardware  213  (e.g., central processing unit (CPU)  201  and storage device(s)  204 ). Below the applications  205  and run-time libraries  206 , one or more drivers  209  can be installed to interface to hardware  213  and/or to AI accelerators  105 - 107 . 
     A driver  209  can include a scheduler  209 A that schedules processing tasks requested by one or more user applications  205 . Driver  209  can further include an analyzer  209 B with logic that analyzes processing tasks scheduled for execution on the AI accelerators  105 - 107  to determine how to best configure the AI accelerators  105 - 107  based upon scheduling criteria such as processing throughput, energy consumption, and heat generated by the AI accelerators. Driver  209  can further include one or more policies directed to configuring the AI accelerators to achieve the scheduling criteria. Configuring AI accelerators can include grouping AI accelerators into one or more groups, removing one or more AI accelerators from one or more groups. Driver  209  can further include checkpointer  209 C. Checkpointer  209 C can snapshot a state of user application  205 , memory within VM  201 , scheduler  209 A state, analyzer  209 B state, and a configuration of a virtual function within VM  201 . A virtual function, as used herein, is a mapping of a set of resources within an artificial intelligence (AI) accelerator, e.g.  105 , or a cluster of AI accelerators  105  . . .  107  to a virtual machine. The virtual function is described below with reference to  FIGS. 3, 4A, and 4B . 
     An AI accelerator that is not assigned to a group of AI accelerators within a cluster of AI accelerators can be set to a low-power state to save energy and reduce heat. A low-power state can include reducing a clock speed of the AI accelerator or entering a standby state wherein the AI accelerator is still communicatively coupled to the host device and can be brought to a run state wherein the AI accelerator is ready to receive processing tasks from the host device. AI accelerators not assigned to a group in a cluster can alternatively be left in a powered-up state such that driver  209  can assign work to a single AI accelerator that is not a member of a group of AI accelerators. 
     Configuring AI accelerators can further include instructing one or more AI accelerator to generate a communication link (link up) with one or more other AI accelerators to form a group of AI accelerators within a cluster of AI accelerators. Configuring AI accelerators can further include instructing one or more DP accelerate to break a communication link (link down) between the AI accelerator and one or more other AI accelerators. Link up and link down of an AI accelerator can be governed by one or more link registers in each AI accelerator. 
     In a policy-based partition embodiment, an AI accelerator configuration policy is a single policy that describes communication links (up or down) for each AI accelerator. While configuration of each AI accelerator can be (and, typically will be) different from the other AI accelerators, the configuration of each AI accelerator is included within the single policy, and each AI accelerator in the cluster receives the same policy. Each AI accelerator then configures itself according to the portion of the policy that describes configuring the AI accelerator. Policy-based partition can be based on an analysis of processing tasks in the scheduler  209 A. The analysis can determine an optimal allocation of AI accelerators into groups. In one embodiment, time-share processing tasks within a group of processors, or across multiple groups of processors, to optimize throughput, minimize energy consumption and generated heat. Advantages of policy-based partitioning of AI accelerators into groups include fast partitioning of AI accelerators, flexible scheduling of processing tasks within, or across, groups, time-sharing of AI accelerators and time-sharing of groups. 
     In a dynamic partition embodiment, an AI accelerator policy is generated for each AI accelerator. The driver  209  can dynamically change the configuration of each AI accelerator, including reorganizing groups of AI accelerators, removing one or more AI accelerators from all groups and setting those AI accelerators to a low-power state. In the dynamic partition embodiment, each group of AI accelerators is assigned to a single user rather than time-sharing the AI accelerators between users. Driver  209  can include analyzer  209 B that analyzes processing tasks within the scheduler  209 A to determine an optimal grouping of AI accelerators. The analysis can generate a configuration for one or more AI accelerators and the configuration can be deployed to each such AI accelerator to be reconfigured. Advantages of dynamic partitioning include energy saving through setting one or more processors to a low-power state, and user-dedicated processing for an AI accelerator or a group of AI accelerators, rather than time-slicing between users. 
     Hardware  213  can include a processing system  201  having one or more processor(s)  201 . Hardware  213  can further include and storage device(s)  204 . Storage device(s)  204  can include one or more artificial intelligence (AI) models  202 , and one or more kernels  203 . Kernels  203  can include signature kernels, watermark-enabled kernels, encryption and/or decryption kernels, and the like. A signature kernel, when executed, can digitally sign any input in accordance with the programming of the kernel. A watermark-enabled kernel can extract a watermark from a data object (e.g. an AI model or other data object). A watermark-enabled kernel can also implant a watermark into an AI model, an inference output, or other data object. 
     A watermark kernel (e.g. a watermark inherited kernel) can inherit a watermark from another data object and implant that watermark into a different object, such as an inference output or an AI model. A watermark, as used herein, is an identifier associated with, and can be implanted into, an AI model or an inference generated by an AI model. For example, a watermark may be implanted in one or more weight variables or bias variables. Alternatively, one or more nodes (e.g., fake nodes that are not used or unlikely used by the artificial intelligence model) may be created to implant or store the watermark. 
     Host machine  104  may be a CPU system which can control and manage execution of jobs on the host machine  104  and/or AI accelerators  105 - 107 . In order to secure/obscure a communication channel  215  between AI accelerators  105 - 107  and host machine  104 , different components may be required to protect different layers of the host system that are prone to data intrusions or attacks. 
     System  200  includes host system  104  and AI accelerators  105 - 107  according to some embodiments. There can be any number of AI accelerators. AI accelerators can include Baidu® AI chipsets or another AI chipset such as a graphical processing units (GPUs) that can perform artificial intelligence (AI)-intensive computing tasks. In one embodiment, host system  104  includes a hardware that has one or more CPU(s)  213  optionally equipped with a security module (such as an optional trusted platform module (TPM)) within host machine  104 . The optional TPM is a specialized chip on an endpoint device that stores cryptographic keys (e.g., RSA cryptographic keys) specific to the host system for hardware authentication. Each TPM chip can contain one or more RSA key pairs (e.g., public and private key pairs) called endorsement keys (EK) or endorsement credentials (EC), i.e., root keys. The key pairs are maintained inside the optional TPM chip and cannot be accessed by software. Critical sections of firmware and software can then be hashed by the EK or EC before they are executed to protect the system against unauthorized firmware and software modifications. The optional TPM chip on the host machine can thus be used as a root of trust for secure boot. 
     The optional TPM chip can also secure driver(s)  209  and operating system (OS)  211  in a working kernel space to communicate with the AI accelerators  105 - 107 . Here, driver  209  is provided by an AI accelerator vendor and can serve as a driver  209  for the user applications  205  to control a communication channel(s)  215  between host and AI accelerators. Because the optional TPM chip and secure boot processor protects the OS  211  and drivers  209  in their kernel space, TPM also effectively protects the driver  209  and OS  211 . 
     Since communication channels  215  for AI accelerators  105 - 107  may be exclusively used by the OS  211  and driver  209 , communication channels  215  can be secured through the optional TPM chip. In one embodiment, communication channels  215  include a peripheral component interconnect (PCI) or peripheral component interconnect express (PCIE) channel. In one embodiment, communication channels  215  are obscured communication channels. 
     Runtime libraries  206  can convert application programming interface (API) calls to commands for execution, configuration, and/or control of the AI accelerators. In one embodiment, runtime libraries  206  provide a predetermined set of (e.g., predefined) kernels for execution by the user applications. In one embodiment, the kernels may be stored in storage device(s)  204  as kernels  203 . 
     The operating system(s)  211  may be any Linux® distributions, UNIX®, Windows® OS, or Mac® OS, or other operating system. 
     The system can boot up through an optional TPM-based secure boot. The optional TPM secure boot ensures only a signed/certified operating system  211  and accelerator driver  209  are launched in a kernel space that provides the accelerator services. In one embodiment, the operating  211  system can be loaded through a hypervisor ( 212 ). A hypervisor or a virtual machine manager  212  is a computer software, firmware, or hardware that creates and runs virtual machines. A kernel space is a declarative region or scope where kernels (i.e., a predetermined set of (e.g., predefined) functions for execution) are identified to provide functionalities and services to user applications. In the event that integrity of the system is compromised, optional TPM secure boot may fail to boot up and instead shuts down the system. 
     After booting, runtime libraries  206  run a user application  205 . In one embodiment, user application  205  and runtime libraries  206  are statically linked and launched together. In another embodiment, runtime library  206  is launched first and then user application  205  is dynamically loaded. Statically linked libraries are libraries linked to an application at compile time. Dynamic loading can be performed by a dynamic linker. Dynamic linker loads and links shared libraries for running user applications at runtime. Here, user applications  205  and runtime libraries  206  are visible to each other at runtime, e.g., all process data are visible to each other. 
     In one embodiment, the user application  205  can only call a kernel from a set of kernels as predetermined by runtime libraries  206 . In another aspect, user application  205  and runtime libraries  206  are hardened with a side channel free algorithm to defend against side channel attacks such as cache-based side channel attacks. A side channel attack is any attack based on information gained from the implementation of a computer system, rather than weaknesses in the implemented algorithm itself (e.g. cryptanalysis and software bugs). Examples of side channel attacks include cache attacks which are attacks based on an attacker&#39;s ability to monitor a cache of a shared physical system in a virtualized environment or a cloud environment. Hardening can include masking of the cache, outputs generated by the algorithms to be placed on the cache. Next, when the user application finishes execution, the user application terminates its execution and exits. 
     In one embodiment, the set of kernels  203  include obfuscation kernel algorithms. In one embodiment, the obfuscation kernel algorithms can be symmetric or asymmetric algorithms. A symmetric obfuscation algorithm can obfuscate and de-obfuscate data communications using a same algorithm. An asymmetric obfuscation algorithm requires a pair of algorithms, where a first of the pair is used to obfuscate and the second of the pair is used to de-obfuscate, or vice versa. In another embodiment, an asymmetric obfuscation algorithm includes a single obfuscation algorithm used to obfuscate a data set but the data set is not intended to be de-obfuscated, e.g., there is absent a counterpart de-obfuscation algorithm. 
     Obfuscation refers to obscuring of an intended meaning of a communication by making the communication message difficult to understand, usually with confusing and ambiguous language. Obscured data is harder and more complex to reverse engineering. An obfuscation algorithm can be applied before data is communicated to obscure (cipher/decipher) the data communication reducing a chance of eavesdrop. In one embodiment, the obfuscation algorithm can further include an encryption scheme to further encrypt the obfuscated data for an additional layer of protection. Unlike encryption, which may be computationally intensive, obfuscation algorithms may simplify the computations. 
     Some obfuscation techniques can include but are not limited to, letter obfuscation, name obfuscation, data obfuscation, control flow obfuscation, etc. Letter obfuscation is a process to replace one or more letters in a data with a specific alternate letter, rendering the data meaningless. Examples of letter obfuscation include a letter rotate function, where each letter is shifted along, or rotated, a predetermine number of places along the alphabet. Another example is to reorder or jumble up the letters based on a specific pattern. Name obfuscation is a process to replace specific targeted strings with meaningless strings. Control flow obfuscation can change the order of control flow in a program with additive code (insertion of dead code, inserting uncontrolled jump, inserting alternative structures) to hide a true control flow of an algorithm/AI model. 
     In summary, system  200  provides multiple layers of protection for AI accelerators (for data transmissions including machine learning models, training data, and inference outputs) from loss of data confidential and integrity. System  200  can include an optional TPM-based secure boot protection layer and a kernel validation/verification layer. System  200  can include applications that use side-channel free algorithms so as to defend against side channel attacks, such as cache based side channel attacks. 
     Runtime  206  can provide obfuscation kernel algorithms to obfuscate data communication between a host  104  and AI accelerators  105 - 107 . In one embodiment, the obfuscation can be paired with a cryptography scheme. In another embodiment, the obfuscation is the sole protection scheme and cryptography-based hardware is rendered unnecessary for the AI accelerators. 
       FIG. 2B  is a block diagram illustrating a secure computing environment between one or more hosts and one or more artificial intelligence (AI) accelerators, according to one embodiment. In one embodiment, host channel manager (HCM)  250  includes optional authentication module  251 , optional termination module  252 , optional key manager  253 , optional key(s) store  254 , and optional cryptography engine  255 . Optional authentication module  251  can authenticate a user application running on host server  104  for permission to access or use a resource of an AI accelerator  105 . HCM  250  can communicate with accelerator channel manager (ACM)  280  of AI accelerator  215  via communication channel  215 . 
     Optional termination module  252  can terminate a connection (e.g., channels associated with the connection would be terminated). Optional key manager  253  can manage (e.g., create or destroy) asymmetric key pairs or symmetric keys for encryption/decryption of one or more data packets for different secure data exchange channels. Here, each user application (as part of user applications  205  of  FIG. 2A ) can correspond or map to different secure data exchange channels, on a one-to-many relationship, and each data exchange channel can correspond to an AI accelerator  105 . Each application can utilize a plurality of session keys, where each session key is for a secure channel corresponding to an AI accelerator (e.g., accelerators  105 - 107 ). Optional key(s) store  254  can store encryption asymmetric key pairs or symmetric keys. Optional cryptography engine  255  can encrypt or decrypt a data packet for the data exchanged through any of the secure channels. Note that some of these modules can be integrated into fewer modules. 
     In one embodiment, AI accelerator  105  includes ACM  280 , non-sensitive resources  290 , and sensitive resources  270 . ACM  280  is a counterpart module corresponding to HCM  250  responsible for managing communications between host  104  and AI accelerator  105  such as, for example, resource access control. ACM  280  includes a link configuration module  281  that cooperates with HCM  250  of host server  104  to establish communication channel  215  between host server  104  and AI accelerator  105 . ACM  280  further includes resource manager  282 . Resource manager  282  enforces restricted access to sensitive resources  270  and non-sensitive resources  290 . In one embodiment, sensitive resources  270  occupy a first range of address space within AI accelerator  105 . Non-sensitive resources  290  occupy a second range of address space within AI accelerator  105 . In one embodiment, the first and second address spaces are mutually exclusive and non-overlapping. In one embodiment, resource manager  282  further contains logic (e.g., access control logic) that permits access by host server  104  to both sensitive resources  270  and non-sensitive resources  280 . In one embodiment, resource manager  282  enforces an access and configuration policy received from host server  104 , as further described below. 
     Sensitive resources  270  can include optional key manager  271 , optional key(s) store  272 , true random number generator  273 , optional cryptography engine  274 , and memory/storage  277 . Optional key manager  271  can manage (e.g., generate, safe keep, and/or destroy) asymmetric key pairs or symmetric keys. Optional key(s) store  272  can store the cryptography asymmetric key pairs or symmetric keys in secure storage within the sensitive resources  270 . True random number generator  273  can generate seeds for key generation and cryptographic engine  274  uses, such as authenticating a linked AI accelerator. Optional cryptography engine  274  can encrypt or decrypt key information or data packets for data exchanges. Memory/storage  277  can include storage for AI models  275  and kernels  276 . Kernels  276  can include watermark kernel(s) (including inherited watermark kernels watermark-enabled kernels, watermark-signature kernels, et al.), encryption and decryption kernels, and associated data. 
     AI accelerator  105  can further include non-sensitive resources  290 . Non-sensitive resources  290  can include one or more processors or processing logic  291  and memory/storage  292 . Processor or processing logic  192  is capable of executing instructions or programs to perform a variety of processing tasks such as AI tasks (e.g., machine-learning processes). 
     Link configuration module  281  is responsible for establishing or connecting a link or path from an AI accelerator to another AI accelerator, or terminating or disconnecting a link or path from an AI accelerator to another AI accelerator. In one embodiment, in response to a request (e.g., from a host) to join a group of AI accelerators, link configuration module  281  establish a link or path from the corresponding AI accelerator to at least some of the AI accelerators in the group or cluster, such that the AI accelerator can communicate with other AI accelerators, for example, accessing resources of the other AI accelerators for AI processing. Similarly, in response to a request to switch from a first group of AI accelerators to a second group of AI accelerators, link configuration module  281  terminates an existing link of the corresponding AI accelerator from the first group, and establish a new link to the second group of AI accelerators. 
     In one embodiment, AI accelerator  105  further includes an AI processing unit (not shown), which may include an AI training unit and an AI inference unit. The AI training and inference units may be integrated into a single unit in sensitive resources  270 . The AI training module is configured to train an AI model using a set of training data. The AI model to be trained and the training data may be received from host system  104  via communication link  215 . In one embodiment, training data can be stored in non-sensitive resources  290 . The AI model inference unit can be configured to execute a trained artificial intelligence model on a set of input data (e.g., set of input features) to infer and classify the input data. For example, an image may be input to an AI model to classify whether the image contains a person, a landscape, etc. The trained AI model and the input data may also be received from host system  104  via interface  140  over communication link  215 . 
     In one embodiment, a watermark unit (not shown) in sensitive resources  270  may include a watermark generator, and a watermark inscriber (also termed, “watermark implanter”). Watermark unit (not shown) may include a watermark kernel executor or kernel processor (not shown) of sensitive resources  270  to execute a kernel  276 . In an embodiment, a kernel may be received from host  104 , or retrieved from persistent or non-persistent storage, and executed in kernel memory  276  in sensitive resources  270  of AI accelerator  105 . The watermark generator is configured to generate a watermark using a predetermined watermark algorithm. Alternatively, the watermark generator can inherit a watermark from an existing watermark or extract a watermark from another data structure or data object, such as an artificial intelligence model or a set of input data, which may be received from host system  104 . The watermark implanter is configured to inscribe or implant a watermark into a data structure such as an artificial intelligence model or output data generated by an artificial intelligence model. The artificial intelligence model or output data having a watermark implanted therein may be returned from AI accelerator  105  to host system  104  over communication link  215 . Note that AI accelerators  105 - 107  have the identical or similar structures or components and the description concerning an AI accelerator would be applicable to all AI accelerators throughout this application. 
       FIG. 3  is a block diagram illustrating a host  104  controlling a cluster  310  of artificial intelligence accelerators, each cluster having a virtual function that maps the resources of a group  311  of AI accelerators within the cluster to a virtual machine on the host, each artificial intelligence accelerator having secure resources and non-secure resources, according to an embodiment. 
     Host  104  can include application(s)  205 , e.g. artificial intelligence (AI) applications, runtime library  206 , one or more drivers  209 , an operating system  211 , and hardware  213 , each of which has been described above with reference to  FIGS. 2A and 2B  and will not be repeated here. In a virtual computing embodiment, host  104  can further include a hypervisor  212 , such as VMware® vSphere/ESXi® or Microsoft® Hyper-V®. Hypervisor  212  can be a type 1 “bare metal” or “native” hypervisor that runs directly on a physical server. In one embodiment, hypervisor  212  can be a type 2 hypervisor that loads inside an operating system  211  like any other application and is managed by the operating system  211 . In either case, hypervisor  212  can support one or more virtual machines (not shown) on the host  104 . In such an aspect, the virtual machines (not shown) can be treated as the client devices  101  and  102  of  FIG. 1 . 
     Artificial intelligence (AI) accelerator cluster  310  can comprise AI accelerators  105 - 107  described above with reference to  FIGS. 2A and 2B . With reference to  FIG. 3 , AI accelerator cluster  310  can include, e.g., eight (8) AI accelerators labeled A through H. Each AI accelerator in the accelerator cluster  310  can have one or more communication links  215  to one or more other AI accelerators in the accelerator cluster  310 . AI accelerator communication links  215  are described above with reference to  FIGS. 2A and 2B . Each AI accelerator in the cluster  310  is configured according to a policy received from host  104  driver  209 . Each AI accelerator in the cluster  310  can have sensitive resources  270  and non-sensitive  290  resources. 
     In the example shown in  FIG. 3 , AI accelerators A-D are configured as a first group  311  of four (4) AI accelerators. The resources of AI accelerators in first group  311  are configured and managed by virtual function VF 1  and are associated with a first virtual machine. AI accelerators E-H are configured in a second group  312  of four (4) AI accelerators. The resources of AI accelerators in second group  312  are configured and managed by virtual function VF 2  and are associated with a second virtual machine. Resources of the two groups  311  and  312  are mutually exclusive and a user of either of the two groups cannot access resources of the other in the two groups. In the first group  311  of AI accelerators, each AI accelerator has a communication link either directly to another accelerator, e.g. A-B, A-C, B-D, and C-D, or has a communication path to another accelerator via one or more intervening accelerators, e.g. A-B-D, A-C-D, etc. The second group  312  is shown as having direct communication links between each AI accelerators in the second group  312  and each other AI accelerator in the second group  312 . Driver  209  can generate a policy wherein each AI accelerator in a group has a direct communication link with each or some of other AI accelerators in the group. In the case of the first group  311 , driver  209  can generate a policy that further includes, e.g., instructions for AI accelerator A and D to generate a communication link with one another and AI accelerators B and C to generate a communication link with one another. There can be any number of AI accelerators in a cluster  310 , configured into any number of groups. 
     In a static policy-based embodiment, a single policy which defines the configuration for each of the AI accelerators and is transmitted from the driver  209  to the all AI accelerators in the cluster  310 . In an embodiment, the driver  209  can transmit the policy in a single broadcast message to all AI accelerators in the cluster. Each AI accelerator reads the policy and makes (link up) or breaks (link down) a communication link with one or more AI accelerators in the cluster  310 , thereby configuring the AI accelerators into one or more groups. In  FIG. 3 , there are eight (8) AI accelerators configured as a two groups of four (4) AI accelerators. Each AI accelerator in a group has either a direct communication link to each AI accelerator in the group or has an indirect communication path to each AI accelerator in the group via one or more AI accelerators to which the AI accelerator has a direct communication link. In the static policy-based environment, scheduler  209 A of driver  209  can schedule processing tasks on one or more groups of the cluster  310  using time-slicing between applications  205  and/or users of virtual machines. In an embodiment, each group of accelerators in the accelerator cluster  310  can have a distinct and separate scheduler  209 A. The static policy can be changed by the driver  209  generating a new policy that describes configuration for each of the AI accelerators in cluster  310 . 
     Each AI accelerator (e.g., link configuration module  281  and/or resource manager  282 ) in the cluster  310  reconfigures itself in accordance with the policy, making (link up) or breaking (link down) communication links between the AI accelerator and one or more other AI accelerators in the cluster  310 . Static policy-based configuration is fast in that the configuration is transmitted in a single, e.g. broadcast, message, and each AI accelerator configures itself substantially in parallel with other AI accelerators in the cluster  310 . Since the policy for all AI accelerators is transmitted to all AI accelerators at a same time, configuration can occur very quickly. For example, if the policy includes instructions to AI accelerator “A” to generate a link AI accelerator “B”, then the policy also has instructions that AI accelerator B is to generate a link to AI accelerator A. Each AI accelerator can open their own end of a link substantially simultaneously thereby opening the link between AI accelerator A and AI accelerator B very quickly. In one embodiment, the single policy can be expressed as an adjacency table of AI accelerators. 
     Static policy-based configuration is also efficient in that it supports time-slice scheduling between different users and supports allocating processing tasks of a user to more than one AI accelerator group in the cluster  310 . The static policy can be generated from the analyzer  209 B determining characteristics of the processing tasks in the scheduler  209 A. For example, scheduler  209 A may include a large number of tasks that use a same AI model to perform inferences or further train the AI model. Analyzer can generate a policy that configures a number of AI accelerators to prepare for performing inferences or training on the AI model. Configuration can include identifying a grouping of AI accelerators, and loading one or more AI models into sensitive memory of one or more of the AI accelerators in preparation for the processing tasks in the scheduler  209 A. 
     In a dynamic policy-based embodiment, driver  209  can individually configure each AI accelerator in the cluster  310  to achieve configuration of the AI accelerators. A policy is transmitted separately to each AI accelerator. In practice, in dynamic policy based embodiment, the policies transmitted to each AI accelerator typically differ from one another. The AI accelerator receives the policy and configures itself in accordance with the policy. Configuration includes an AI accelerator configuring itself into, or out of, a group in the cluster  310 . An AI accelerator configures itself into a group by making a communication link (link up) with at least one AI accelerator in the group, in accordance with the policy. An AI accelerator leaves a group by breaking a communication link (link down) between the AI accelerator and all AI accelerators in the group. After configuration, if an AI accelerator is not a member of any group of AI accelerators, the AI accelerator can be set to a low-power model to reduce heat and conserve energy. In one embodiment, scheduler  209 A assigns an AI accelerator or AI accelerator group to each user or application for which scheduler  209 A is scheduling processing tasks to the cluster  310 . 
       FIG. 4A  is a block diagram illustrating components of a data processing system having artificial intelligence (AI) accelerators to implement a method for virtual machine migration with checkpoint authentication in a virtualized environment, according to an embodiment. 
     A source host (HOST-S)  401  can support a plurality of virtual machines (VM&#39;s) such as a first (source) VM to be migrated (VM 1 -S) to a target host (HOST-T)  451 , via network  103 . Network  103  can be any network, as described with reference to  FIG. 1 , above. HOST-S  401  can also support additional VM&#39;s, such as VM 2  and VM 3 . Virtual machines VM 1 -S, VM 2 , and VM 3  (each labeled “ 402 ”) can each include at least one application  403  and at least one driver  404 . Driver  404  can include one or more function libraries and application programming interfaces (API&#39;s) that enable the VM  402  containing the driver  404  to communicate with one or more artificial intelligence (AI) accelerators  410  that are communicatively coupled with the VM  402  via hypervisor  405 , CPU  406 , and bus  407 . 
     Hypervisor X  405  can be any type of hypervisor, including a “bare metal” hypervisor running on HOST-S  401 &#39;s hardware, or the hypervisor can run an operating system (not shown) of the HOST-S  401  executing on the host&#39;s hardware, such as CPU  406  and memory (not shown). CPU  406  can be any type of CPU, such general purpose processor, a multi-core processor, a pipelined processor, a parallel processor, and the like. Bus  407  can be any type of high-speed bus such as peripheral component interconnect express (PCIe), a fiber-optic bus, or other type of high-speed bus. As described above with reference to  FIGS. 2A, 2B, and 3 , communication channel  215 , the communication over bus  407  can be encrypted. Bus  407  communicatively couples CPU  406  to the one or more artificial intelligence (AI) accelerators  410 . Each VM can have a separately encrypted communication channel  215  that uses one or more different keys that the encrypted communication channel  215  of each of the other VMs. 
     Each AI accelerator  410  can host one or more virtual functions, such as VF 1 , VF 2 , VFn, each labeled with reference  411  in  FIG. 4 . A virtual function  411  maps resources  412 , e.g. RES 1 , RES 2 , . . . RESn of accelerators ACC 1   410  to a particular host virtual machine  402 . Each virtual machine  402  has a user. A virtual function  411  associated with a particular VM  402  (e.g. VM 1 -S) can only be accessed by the user of the particular VM  402  (e.g. VM 1 -S). Virtual machine resources are each labeled with reference  412  in  FIG. 4 . Virtual machine resources  412  are described above with reference to  FIG. 2B , and include such resources as non-sensitive resources  290  (including processing login  291  and memory/storage  292 ), an accelerator channel manage  280  (including link configuration  281  and resource manager  282 ), and sensitive resources  270  (including AI model(s)  275 , kernel(s)  276 , in memory/storage  277 , and key manger  271 , key(s) store  272 , true random number generator  273 , and cryptographic engine  274 . As described more fully below, after a virtual machine, e.g. VM 1 -S is migrated to a target host, e.g. HOST-T  451 , at least the sensitive resources should be erased so that after the migrated virtual function of VM 1 -S is migrated to the target host HOST-T  451 , and the now-unused resources of the migrated virtual function of VM 1 -S are assigned to a new VM, that the sensitive data of the migrated VM 1 -S and the sensitive data associated with the virtual function associated with the VM 1 -S will not be accessible to the new VM. 
     A target host, e.g. HOST-T  451 , can be of a same or similar hardware and software configuration as HOST-S  401 . Accelerators  410  and accelerators  460  should be of a same or similar type, such as having a compatible instruction set for their respective processors. The HOST-T  451  should have, quantitatively, sufficient resources available as may be required by VM-S so that VM 1 -S may be migrated to VM 1 -T. Qualitatively, HOST-S  401  and HOST T- 451  should have compatible operating hardware and software. For example, HOST-S  401  accelerators  410  may be of a same manufacturer, and a compatible model, as the accelerators ACC 2   460  on HOST-T  451 , else the migration may not be successful. 
     Checkpoint  420  is a snapshot of a state of VM 1 -S, up to and including the virtual function  411  (e.g. VF 1 ) that is being migrated as a part of the migration of VM 1 -S from HOST-S  401  to HOST-T  451 . The checkpoint of VM 1 -S, and associated virtual function, can include the following information. In an embodiment, the checkpoint does not include information contained within the resources  412  inside the accelerator  410 . The following list of information that is included in a checkpoint is by way of illustration, not limitation. One of skill in the art could add to, or delete from, the following table of information for a checkpoint  420  of a virtual machine and virtual function to be migrated. 
     
       
         
           
               
            
               
                   
               
               
                 Exemplary Contents of a Checkpoint Frame 420 
               
            
           
           
               
               
               
            
               
                 Subsystem 
                 Resource 
                 Checkpoint Data 
               
               
                   
               
               
                   
                   
                 Date and time stamp 
               
               
                   
                   
                 Virtual machine identifier 
               
               
                 Driver 209 
                 Scheduler 209 
                 List of scheduled AI tasks for  
               
               
                   
                   
                 AI accelerator(s) 
               
               
                 Oper. System  
                 Scheduler  
                 Pending tasks of AI application  
               
               
                 211 
                 (not shown) 
                 205 
               
               
                 Cluster 310 
                 AI Accelerators 105 
                 Number and type (mfr./model)  
               
               
                   
                   
                 allocated 
               
               
                 Cluster 310 
                 AI Accelerators 105 
                 Comm. config. 215 of allocated  
               
               
                   
                   
                 accelerators 
               
               
                 Sensitive 270 
                 Memory/storage 277 
                 Memory type (static, dynamic),  
               
               
                   
                   
                 amount, address 
               
               
                 Sensitive 270 
                 Key generation 271 
                 Enabled? 
               
               
                 Sensitive 270 
                 Key store 272 
                 No. and use for keys, key type 
               
               
                   
                   
                 (symm/asymm). 
               
               
                 Sensitive 270 
                 Rand. No. Gen. 273 
                 Enabled? 
               
               
                 Sensitive 270 
                 Crypto-engine 274 
                 Digest, signature, encrypt,  
               
               
                   
                   
                 decrypt enabled? 
               
               
                 Non-sensitive  
                 Processor(s) 
                 Number of cores, clock speed,  
               
               
                 290 
                   
                 priority, slices 
               
               
                 Non-sensitive  
                 Memory 
                 Memory type (static, dynamic),  
               
               
                 290 
                   
                 amount, address 
               
               
                   
               
            
           
         
       
     
     Checkpointer  209 C can generate a checkpoint frame  420  according to events described in  FIG. 6 . Checkpoint frame  420  may be generated, e.g., at a specified increment of time, upon detection of system exception or fault, or upon receipt of an instruction to take a checkpoint frame  420 . Such an instruction may come from a user, such as an administrator or end-user. Each checkpoint frame  420  may be on the order of, e.g., 1 gigabyte (GB) in size. In an embodiment, checkpointer  209  may include a circular buffer that stores up to a specified number, k, checkpoint frames  420 . When the buffer is full, a next added frame overwrites the oldest checkpoint frame  420 . When it is time to migrate the virtual machine and virtual function, a user may select a particular checkpoint frame  420  to use to perform the migration, representing a known state of the running application  403  that the user prefers for the migration. In embodiment, a migration uses the most recent checkpoint frame  420  by default. In an embodiment, the checkpoint frame  420 , a hash of the checkpoint frame  420 , and a date and time stamp of the checkpoint frame  420  can be digitally signed before transmitting the checkpoint frame  420  from the source VM 1 -S to a hypervisor of a target host HOST-T  451  during a migration of the source VM 1 -S. 
     When the hypervisor  455  of the target host HOST-T  451  receives the checkpoint frame  420 , the hypervisor  455  can decrypt the checkpoint frame  420  using a public key of the VM 1 -S, validate that the date and time stamp falls within a predetermined window of time, and verify the hash of the checkpoint frame. Verifying the date and time stamp validates a freshness of the checkpoint frame  420 . If the hypervisor  455  of the target HOST-T  451  validates the checkpoint frame  420 , then the hypervisor  455  of HOST-T  451  can allocate resources for the source VM 1 -S at HOST-T  451  to generate VM 1 -T  452 . 
     Referring now to  FIG. 4B , checkpointer  209  can further obtain an AI accelerator status frame  421 . An AI accelerator status frame  421  differs from a checkpoint frame  420  in that the AI accelerator status frame  421  captures information inside the AI accelerator(s)  410 . Captured content of an AI accelerator status frame can include contents of one or more registers inside the AI accelerator, contents of secure memory and non-secure memory containing, e.g. AI models, kernels, intermediate inference calculations, and the like. The AI accelerator status frame  421  may be taken synchronously with a checkpoint frame  420 , so that information obtained AI accelerator status frame  421  is “fresh” (current) with respect to the most recent checkpoint frame  420  of the VM 1 -S to be migrated, and its associated virtual function that maps allocation of AI accelerator  410  resources to a virtual machine, e.g. VM 1 -S. In an embodiment, the AI accelerator status frame  421  can be taken after a checkpoint frame  420 , and after pending AI tasks of the executing application  403  have been stopped. Such an embodiment avoids the AI accelerator status frame  421  storing a status of the AI accelerator that corresponds to partial, in-progress processes or threads that may be difficult to reliably restart after migration. 
     An AI accelerator status frame  421  can contain the following information. The following information is by way of example and not limitation. One of skill in the art could add to, or delete from, the information in the table for a particular system installation. The AI accelerator status frame  421 , a hash of the frame, and a data and time stamp of the frame, can be digitally signed with a private key of the AI accelerator  410 , or a private key of the virtual machine VM 1 -S before transmitting the frame to a hypervisor  455  of a target host HOST-T  451  during a migration of the VM 1 -S. When it is time to migrate the virtual machine VM 1 -S and virtual function, a user may select a particular AI accelerator status frame  421 , or the frame  421  may be generated in response to a selection of a checkpoint frame  420  and in response to receiving an instruction to migrate the source VM 1 -S to the target HOST-T  451 . In embodiment, a migration uses the AI accelerator status frame  421  associated with most recent checkpoint frame  420  by default. In an embodiment, the AI accelerator status frame  421 , a hash of the AI accelerator status frame  421 , and a date and time stamp of the AI accelerator status frame  421  can be digitally signed before transmitting the AI accelerator status frame  421  from the source VM 1 -S to a hypervisor  455  of a target host HOST-T  451  during a migration of the source VM 1 -S. 
     When the hypervisor  455  of the target host receives the AI accelerator status frame  421 , the hypervisor can decrypt the AI accelerator status frame  421  using a public key of the VM 1 -S, or, in an embodiment, a public key of the AI accelerator  410  of VM 1 -S to validate that the date and time stamp falls within a predetermined window of time, and verify the hash of the AI accelerator status frame  421 . The check on the date and time stamp validates a freshness of the AI accelerator status frame  421 . If the hypervisor  455  of the target HOST-T  451  validates the AI accelerator status frame  421 , then the hypervisor  455  of HOST-T  451  can copy in the contents of the AI accelerator status frame to the AI accelerator ACC 2   460  on VM 1 -T  452 . 
     
       
         
           
               
            
               
                   
               
               
                 Exemplary AI Accelerator Status Frame 421 
               
            
           
           
               
               
               
            
               
                 Subsystem 
                 Resource 
                 AI Accelerator Status 
               
               
                   
               
               
                   
                   
                 Data and time of frame 
               
               
                   
                   
                 Hash of frame 
               
               
                 Channel Mgr.  
                 Link. Config. 281 
                 Configuration of links to other  
               
               
                 280 
                   
                 accelerators 
               
               
                 Sensitive 270 
                 Memory/storage  
                 Checkpoint memory content (AI  
               
               
                   
                 277 
                 models/kernels) 
               
               
                 Sensitive 270 
                 Key generation 271 
                 Is key generation enabled? 
               
               
                 Sensitive 270 
                 Key store 272 
                 Checkpoint the keys in the keystore 
               
               
                 Sensitive 270 
                 Random no. gen.  
                 Is random number generator  
               
               
                   
                 273 
                 enabled? 
               
               
                 Sensitive 270 
                 Crypto-engine 274 
                 Is cryptographic engine enabled? 
               
               
                 Non-sensitive  
                 Processor(s) 291 
                 Number of cores, clock speed,  
               
               
                 290 
                   
                 priority, slices 
               
               
                 Non-sensitive  
                 Processor(s) 291 
                 Checkpoint of processor  
               
               
                 290 
                   
                 cache/pipeline 
               
               
                 Non-sensitive 
                 Memory 292 
                 Checkpoint non-sensitive memory  
               
               
                 290 
                   
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       FIG. 5A  illustrates a method  500  for virtual machine migration of a data processing system having AI accelerators using checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. Method  500  can be practiced on a source virtual machine, e.g. VM 1 -S, to be migrated to a target host, e.g. HOST-T  451  as migrated virtual machine VM 1 -T. 
     In operation  600 , logic of VM 1 -S can determined whether to store a checkpoint frame  420  of the VM 1 -S that is running an application  403  that utilizes one or more artificial intelligence (AI) accelerators, e.g. ACC 1   410 . The checkpoint frame  420  contains a snapshot of VM 1 -S, including the application  403 , threads executing of the application, a scheduler  209 A containing the executing threads, memory allocated by the VM 1 -S related to the application, and a virtual function that maps the resources of one or more AI accelerators to the VM 1 -S, as described above with reference to  FIG. 4A . In an embodiment, optionally, generating the checkpoint frame  420  can also trigger taking an AI accelerator status frame  421 . In an embodiment, the AI accelerator status frame  421  can be generated and stored after one or more AI tasks associated with the application  403  have been paused or stopped in operation  800 , below. Operation  600  is described in detail below, with reference to  FIG. 6 . 
     In operation  700 , the VM 1 -S can determine whether to migrate the VM 1 -S. The decision can be based upon receipt of a user command, such as from an administrator or an end-user. In an embodiment, the decision to migrate VM 1 -S can be based upon an exception or fault threshold being above a threshold value. Operation  700  is described in detail below with reference to  FIG. 7 . 
     In operation  800 , in response to receiving a command to migrate the VM 1 -S, application, and virtual function for the associated AI accelerators to a target host  451 , and in response to receiving a selection of a checkpoint frame  420  to use in performing the migration, checkpointer  209 C records a state of one or more executing AI tasks related to the running application, then stops or pauses the one or more executing AI tasks. VM 1 -S then begins a process  800  for migrating the VM 1 -S and virtual function to the target host. Operation  800  is described below with reference to  FIG. 8 . 
     In operation  900 , in response to the VM 1 -S receiving a notification from a hypervisor  455  of the target host  451  that the hypervisor  455  has successfully validated the checkpoint  420 , and that the migration is complete, the hypervisor of the source host instructs the hypervisor  455  on target host  451  to restart the migrated application and the recorded tasks in VM 1 -T. Optionally, VM 1 -S performs a post-migration clean-up of the VM 1 -S and the one more AI accelerators associated with the VM 1 -S through the virtual function. Method  900  is described below with reference to  FIG. 9 . Method  500  ends. 
       FIG. 5B  illustrates a method  550  for virtual machine migration on a data processing system having AI accelerators using AI accelerator status validation in a virtualized environment, from the perspective of the source hypervisor that hosts the source virtual machine to be migrated, according to an embodiment. Method  550  can be practiced on a source virtual machine, e.g. VM 1 -S, to be migrated to a target host, e.g. HOST 2   451 , as migrated virtual machine VM 1 -T. 
     In operation  800 , in response to receiving a command to migrate the VM 1 -S, an application running on VM 1 -S, and virtual function for the associated AI accelerators, to a target host  451 , and in response to receiving a selection of a checkpoint frame  420  to use in performing the migration, checkpointer  209 C records a state of one or more executing AI tasks related to the running application, then stops or pauses the one or more executing AI tasks. VM 1 -S then begins a process  800  for migrating the VM 1 -S and virtual function to the target host. Operation  800  is described below with reference to  FIG. 8 . 
     In operation  551 , after the checkpoint frame  420  is selected, then VM 1 -S generates, or selects, a status frame of the AI accelerators  421  associated with the virtual function of VM 1 -S. The AI accelerator status frame  421  is described above with reference to  FIG. 4B . A hash of the AI accelerator status frame  421  is generated, a data and time stamp of the AI accelerator status frame  421  is generated, and the AI accelerator status frame  421 , hash, and date and time stamp are digitally signed with a private key of the VM 1 -S, or, in an embodiment, a private key of the AI accelerator  410  associated with the virtual function that maps AI resources to the VM 1 -S. The digitally signed AI accelerator status frame  421  is transmitted to the hypervisor  455  of the target host  451 . 
     In operation  900 , in response to receiving a notification from the hypervisor  455  of the target host  451  that the checkpoint frame  420  and AI accelerator status frame  421  were successfully validated, and that the migration is complete, the hypervisor  455  on the target host  541  restarts the application and the recorded AI tasks within the migrated virtual machine VM 1 -T. Optionally, VM 1 -S can perform a post-migration clean-up. Operation  900 , including the post-migration clean-up of the VM 1 -S and the one or more AI accelerators associated with the VM 1 -S through the virtual function, is described below in detail with reference to  FIG. 9 . Method  550  ends. 
       FIG. 6  illustrates a method  600  of generating a checkpoint frame for use in a method for virtual machine migration with checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. 
     In operation  601 , hypervisor  405  in host  401  monitors a source virtual machine&#39;s (e.g. VM 1 -S) states, network status, AI accelerator status, and job completion progress. 
     In operation  602 , it is determined whether a time increment for generating a checkpoint frame  420  has expired. The time increment can be set by a user or an administrator and may be dynamically adjusted based upon conditions. In an embodiment, a user adjusts the time increment, such as in anticipation of a need to migrate VM 1 -S, such as if an application running on VM 1 -S is not making sufficient progress, or other reason. In an embodiment, a time increment is fixed. In an embodiment, the time increment is increased or decreased dynamically with respect to a frequency of faults, or lack of faults such that checkpoint frames  420  are generated more frequently if faults are increasing, or less frequently if faults are decreasing. If it is time to generate a checkpoint frame  420 , then method  600  continues at operation  605 , otherwise method  600  continues at operation  603 . 
     In operation  603 , it is determined whether an exception or fault has occurred. A fault counter can be configured one or more types of different of faults having differing significance. A processor exception be far more significant than, e.g., a network fault in a network that supports retries of failed sends or receives. Thus a processor fault may trigger generation of a checkpoint frame  420  at a lower count than network fault counts. If an exception or fault occurs above a configured fault count for the exception or fault type, then method  600  continues at operation  605 , otherwise method  600  continues at operation  604 . 
     In operation  604 , it is determined whether job progress is less than a threshold progress percent of completion. In an embodiment, a job progress can have multiple types of job progress counters. Each job progress counter type can be, e.g., triggered by invocation of a particular piece of source code, or a by a call to a particular AI functionality within an AI accelerator, such as a job progress counter for training an AI model or a counter for an AI inference. The counter can be based on expected execution time vs. actual execution time, or other measure. If a job progress counter indicates that progress is less than a threshold percent for the progress counter type, then method  600  continues at operation  605  otherwise method  600  ends. 
     In operation  605 , VM 1 -S generates a checkpoint frame  420  of VM 1 -S, the running application, and the virtual function that maps AI accelerator resources to the VM 1 -S. 
     In operation  606 , optionally, an AI accelerator status frame  421  can be generated after the checkpoint frame  420  is generated. Method  600  ends. 
       FIG. 7  illustrates a method  700  of determining whether to migrate virtual machine of a data processing system having AI accelerators with checkpoint authentication, and/or AI accelerator status validation, in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. 
     In operation  701 , a flag indicating whether to migrate the virtual machine (VM) is set to false. 
     In operation  702 , it is determined whether VM logic has received a user command to migrate the VM. In an embodiment, the command to migrate can originate from a user of the VM that may be monitoring the progress of an executing AI application. Reasons that a user may elect to migrate a VM can be as are known in the art: e.g. that a process is not making sufficient progress as might be expected, that a particular host machine is heavily loaded or has limited resources, and is contributing to the lack of progress, and the like. If a user command is received to migrate the VM, method  700  continues at operation  705 , otherwise method  700  continues at operation  703 . 
     In operation  703 , it can be determined whether command to migrate a VM has been received from an administrator. An administrator may regularly monitor loads on servers, progress of one or more applications, and available resource levels. An administrator may elect to send a migrate command in response to a user request, or on the administrator&#39;s own judgment. If an administrator issued a command to migrate the VM, then method  700  continues at operation  705 , otherwise method  700  continues at operation  704 . 
     In operation  704 , it can be determined whether a count of a type of exception or fault has exceeded a threshold amount. There can be a different threshold for different types of faults. For example, a count of processor exceptions may be very low, and count of network faults may be—by comparison to processor faults—much higher before an automatic migration is triggered based upon fault counts. In an embodiment, instead of automatically initiating a migration of the VM based on automatically detected conditions, a notification may be sent to an administrator advising that the VM be migrated based upon detected faults. If any type of fault or exception occurs more times that a threshold value associated with that fault or exception type, then method  700  continues at operation  705  otherwise method  700  ends. 
     In operation  705 , the migration flag is set to true. A selection of a checkpoint to use for migration is also received. In the case of a user command or administrator command to initiate the migration, the command may also include the checkpoint frame  420  to use for the migration. In the case of an automatically initiated migration command, a checkpoint frame  420  may be automatically generated, or the most recent checkpoint frame  420  can be selected. In an embodiment, a new checkpoint frame  420  is generated if the most recent stored checkpoint frame  420  is older than a threshold amount of time. 
     In operation  706 , optionally, an AI accelerator status frame  421  can be generated. In the case of a migration command that was automatically generated, based upon fault conditions, an AI accelerator status frame  421  may be automatically generated and can be used with the migration. If an AI accelerator status frame is either selected or generated, then method  550  ( FIG. 5B ) can be performed. Otherwise, method  500  ( FIG. 5A ) is performed. Method  700  ends. 
       FIG. 8  illustrates a method  800  of migrating a virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, from the perspective of the source hypervisor that hosts the virtual machine to be migrated, according to an embodiment. 
     In operation  801 , a selection of a target (destination) server, e.g. host  451 , is received that will host the migrating virtual machine, e.g. VM 1 -S. 
     In operation  802 , one or more running AI tasks of the running application on the VM 1 -S are stopped or paused. In an embodiment, one or more of the running AI tasks are allowed to finish, and others are paused or stopped. 
     In operation  803 , a selected checkpoint frame  420  is transferred to the target host  451 . The hypervisor  405  of VM 1 -S waits from a response from the target host that a validation of the signature, date and time stamp, and hash of the checkpoint frame  420  has been validated. 
     In operation  804 , hypervisor  405  or driver  209  records the AI application running on the VM 1 -S, and any associated unfinished AI tasks, and all unfinished AI tasks are stopped. 
     In operation  805 , VM 1 -S hypervisor  405  sends the recorded state of the unfinished AI jobs to the hypervisor  455  of the target host  451 . Method  800  ends. 
       FIG. 9  illustrates a method  900  of performing post-migration clean-up of a source virtual machine after migrating the virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, according to an embodiment. 
     In operation  901 , the hypervisor  405  of the source virtual machine (VM 1 -S) receives notification from the hypervisor  455  of the target host  451  that a signature, a date and time stamp, and a hash of the checkpoint frame  420  have all been validated. In an embodiment, the notification can also include an indication that the signature, date and time stamp, and hash of an AI accelerator status frame  421  have been validated. The notification may further indicate that the VM 1 -S migration to target host  451  is complete, and the application and unfinished AI tasks have been restarted at the migrated virtual machine on the target host  451  as VM 1 -T. 
     In operation  902 , hypervisor  405  and/or driver  404  of the source host  401  can erase at least the secure memory of AI accelerators that were used by the source VM 1 -S. Hypervisor  405  and/or driver  404  can also erase memory used by the application on VM 1 -S that made calls to APIs or drivers that use the AI accelerators associated with the application via the virtual function associated with VM 1 -S. 
     In operation  903 , hypervisor  405  of source host  401  can deallocate resources of the VM 1 -S, including deallocating AI accelerator resources used by the VM 1 -S and associated with the virtual function that maps the AI accelerator resources to the VM-S. Method  900  ends. 
       FIG. 10  illustrates a method  1000  of migrating a virtual machine of a data processing system having AI accelerators with checkpoint authentication in a virtualized environment, from the perspective of a target hypervisor of a host that will host the migrated virtual machine, according to some embodiments. 
     In operation  1001 , a hypervisor  455  of the target host machine  451  receives a checkpoint frame  420  from a source virtual machine, e.g., VM 1 -S, associated with a virtual function that maps AI processor resources to the VM 1 -S. The hypervisor  455  also receives a request to host the VM 1 -S as migrated virtual machine (VM 1 -T) on target host  451 . 
     In operation  1002 , hypervisor  455  on host  451  calculates and reserves resources for generating VM 1 -S on host  451  as VM 1 -T. Hypervisor  455  allocates and configures resources for hosting VM 1 -S, and its associated virtual function, in accordance with the received checkpoint frame  420 . 
     In operation  1003 , hypervisor  455  at target host  451  receives, and acknowledges to hypervisor  405  at source host  401 , frames of data receives as a part of migrating VM 1 -S to VM 1 -T. Hypervisor  455  stores the received frames on host  451  so that hypervisor  455  may generate VM 1 -T. 
     In operation  1004 , optionally, hypervisor  455  at target host  451  receives a signed AI accelerator status frame  421  from hypervisor  505  at source host  401 . Hypervisor  455  decrypts the signed AI accelerator frame  421  using a public key of VM 1 -S, or a public key of an accelerator of VM 1 -S. Hypervisor  455  validates the date and time stamp in the frame  421 , and validates the digest of the frame  421 . If the signed AI accelerator status frame  421  is successfully validated, then hypervisor  455  loads data from the AI accelerators status frame  421  into the AI accelerator and configures the AI accelerators in accordance with the data in the AI accelerator status frame  421 . 
     In operation  1005 , hypervisor  455  of target host  451  receives the recorded state of unfinished AI tasks of the application running on VM 1 -S. VM 1 -T restarts the application and the unfinished AI tasks on VM 1 -T. 
     In operation  1006 , hypervisor  455  on target host  451  sends a notification to the source hypervisor  405  on source host  401 , indicating that the restarting of the application and unfinished AI tasks was successful and the migration of VM 1 -S to VM 1 -T was successful. 
     Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Embodiments of the disclosure also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices). 
     The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially. 
     Embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the disclosure as described herein. 
     In the foregoing specification, embodiments of the disclosure have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the disclosure as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.