Abstract: Systems and methods are provided for an integrated private cloud AI platform that is deployable at a customer site. The integrated private cloud AI platform can include components deployed at the customer site that utilize an embedding model that accesses/integrates with various knowledge bases and vector data stores locally at the customer site, and permit chatbot-accessible queries to an LLM that utilizes the previously-uploaded embeddings.

Abstract: Systems and methods are provided for an integrated private cloud AI platform that is deployable at a customer site. The integrated private cloud AI platform can include components deployed at the customer site that utilize an embedding model that accesses/integrates with various knowledge bases and vector data stores locally at the customer site, and permit chatbot-accessible queries to an LLM that utilizes the previously-uploaded embeddings.

Abstract: In certain implementations, computer-implemented method includes monitoring use of a first accelerator resource allocated to a first computing workload and determining, based on monitoring the use of the first accelerator resource allocated to the first computing workload, that the use of the first accelerator resource allocated to the first computing workload satisfies an idleness condition. The method further includes reallocating, based at least on determining that the use of the first accelerator resource allocated to the first computing workload satisfies the idleness condition, the first accelerator resource to a second computing workload, the second computing workload being a pending computing workload.

Abstract: Systems and methods are provided for creating a machine learning (ML) model staging repository, where ML models are stored as an open container image (OCI). The OCI comprises layers or portions of the complete ML model, so that the model can be stored separately and as a smaller files. The ML models may be pre-packaged for automated downloads and integration at the customer site. In some examples, the OCI can identify/store the model in a directory structure that defines the model and its profile. The OCI can comprise a combination of layers and profiles that allows the AI platform to optimize the storage and simplify the process of downloading or updating the given user namespace instead of downloading a single large file for the model.

Abstract: Systems and methods are provided for a unified and secure data access platform that generates an access token for the user that uniquely identifies the user in the platform. The user may be registered with the platform and associated with an access role, policy/access level, and the access token. The access token may be associated with a data record that is maintained at the policy server containing the information about the user (e.g., access role, policy/access level, etc.). Using the token, the platform can confirm authorization to access multiple points throughout the workload and data access to improve data security throughout the lifecycle.

Abstract: In some examples, a system receives, from a requester, a request to perform a first workload in a virtual computing environment, and determines a type of the requester, the determined type being one of a plurality of different requester types. The system receives metrics relating to resource usage in the virtual computing environment, and determines, using a machine learning model, an allocation of resources to the first workload based on the determined type of the requester and the metrics. The machine learning model adjusts the allocation of resources to the first workload based on further collected metrics relating to resource usage by the workload and based on a detected behavior of the requester while the first workload is performed in the virtual computing environment.

Abstract: Embodiments described herein are generally directed to a containerized application software deployment architecture. According to an example, a system includes multiple hosts that are part of aa stretch cluster spanning multiple data centers. Each of the hosts include a processing resource, a memory, and a storage device. Each host of a first subset of the multiple hosts runs multiple containerized instances of a component of a Security Information and Event Management (SIEM) application within respective containers. At least one host of the multiple hosts is separate from the first subset and is dedicated to running at least one containerized ingress gateway application operable to load balance requests directed to the SIEM application among the multiple containerized instances running on the first subset of hosts.

Abstract: Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.

Abstract: Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.

Abstract: Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a system is configured to mount storage repositories to a host. Once mounted the system may identify a request to initiate a virtual cluster on the host and may identify permissions associated with the virtual cluster. The system further determines at least one storage repository from the storage repositories for the virtual cluster based on the permissions and initiate execution of the virtual cluster with access to at least one file system corresponding to the at least one storage repository.

Abstract: Embodiments described herein are generally directed to a containerized application software deployment architecture. According to an example, a system includes multiple hosts that are part of aa stretch cluster spanning multiple data centers. Each of the hosts include a processing resource, a memory, and a storage device. Each host of a first subset of the multiple hosts runs multiple containerized instances of a component of a Security Information and Event Management (SIEM) application within respective containers. At least one host of the multiple hosts is separate from the first subset and is dedicated to running at least one containerized ingress gateway application operable to load balance requests directed to the SIEM application among the multiple containerized instances running on the first subset of hosts.

Abstract: Described herein are systems, methods, and software to migrate virtual nodes of a data processing cluster. In one implementation, a management system monitors an executing data processing cluster on one or more first hosts to determine when the data processing cluster satisfies migration criteria. Once satisfied, the management system selects one or more second hosts to support the data processing cluster based on accommodation data associated with the hosts. After selection, the management system may initiate operations to migrate the data processing cluster from the one or more first hosts to the one or more second hosts.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a system is configured to mount storage repositories to a host. Once mounted the system may identify a request to initiate a virtual cluster on the host and may identify permissions associated with the virtual cluster. The system further determines at least one storage repository from the storage repositories for the virtual cluster based on the permissions and initiate execution of the virtual cluster with access to at least one file system corresponding to the at least one storage repository.

Abstract: Embodiments described herein are generally directed to a containerized application software deployment architecture. According to an example, a system includes multiple hosts that are part of aa stretch cluster spanning multiple data centers. Each of the hosts include a processing resource, a memory, and a storage device. Each host of a first subset of the multiple hosts runs multiple containerized instances of a component of a Security Information and Event Management (SIEM) application within respective containers. At least one host of the multiple hosts is separate from the first subset and is dedicated to running at least one containerized ingress gateway application operable to load balance requests directed to the SIEM application among the multiple containerized instances running on the first subset of hosts.

Abstract: Embodiments described herein are generally directed to a containerized application software deployment architecture. According to an example, a system includes multiple hosts that are part of aa stretch cluster spanning multiple data centers. Each of the hosts include a processing resource, a memory, and a storage device. Each host of a first subset of the multiple hosts runs multiple containerized instances of a component of a Security Information and Event Management (SIEM) application within respective containers. At least one host of the multiple hosts is separate from the first subset and is dedicated to running at least one containerized ingress gateway application operable to load balance requests directed to the SIEM application among the multiple containerized instances running on the first subset of hosts.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a system is configured to mount storage repositories to a host. Once mounted the system may identify a request to initiate a virtual cluster on the host and may identify permissions associated with the virtual cluster. The system further determines at least one storage repository from the storage repositories for the virtual cluster based on the permissions and initiate execution of the virtual cluster with access to at least one file system corresponding to the at least one storage repository.

Abstract: Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.

Abstract: Described herein are systems, methods, and software to enhance the management of virtual nodes in a computing environment. In one example, a method includes identifying a transition event for a first version of a virtual node executing on a first host, identifying a second host to support a second version of the virtual node, and initiating execution of the second version of the virtual node on the second host. In some implementations, the virtual node comprises a non-persistent portion on the executing host and a persistent portion on a second storage element.