Abstract: An information processing apparatus is described for processing a workload. The information processing apparatus comprises a processor and a memory element connected to the processor via a data link. In advance of processing a workload, the information processing apparatus estimates an access time required to transfer an amount of the workload that is to be transferred from the external memory element to the processor, and estimates a processing time for the processor to process the workload. A processing rate characteristic of the processor and/or a data transfer rate between the memory and the processor is set in dependence upon the estimated processing time and estimated access time. Methods for varying a quality of service (QoS) value of requests to the external memory element are also described.

Abstract: A computer-implemented method according to one embodiment includes identifying a job creation request within a system; determining a current amount of available resources within the system; and conditionally deploying a reusable group of containers for the job, based on the current amount of available resources for the system.

Abstract: A computer program product may cause a processor to perform various operations in a composable computing system. The operations may include accessing a system resource database including, for each of a plurality of disaggregated computing hardware resources within the composable computing system, data corresponding to hardware characteristics for the hardware resource and a power domain characteristic of a power domain that supplies power to the hardware resource. The operations may further include identifying a workload request that requests performance of a workload and identifying a power domain requirement for the workload.

Abstract: Certain aspects of the disclosure provide systems and methods for receiving a request to process a workload on a remote processing system; determining one or more workload requirements associated with processing the workload; and processing the one or more workload requirements with a contextual bandit machine learning model to generate a processing configuration for the remote processing system. The remote processing system provisions resources based on the processing configuration and processes the workload.

Abstract: There is provided a distributed computation system that establishes a consensus related to a computational value of a computational task, wherein the distributed computation system includes a plurality of computing nodes.

Abstract: Switches and methods for utilizing the switches are described. The switch has ports, a memory and a scheduler. Packets ingress and egress the switch through the ports. Each packet is divisible into packet segments. The memory includes banks. The scheduler is coupled with the ports and the memory. The scheduler is configured to allocate memory to store the packet segments in the banks such that a beginning packet segment of a packet is stored in a selected bank and each subsequent packet segment in the packet is stored in order in a next adjacent bank.

Abstract: A system and corresponding method employ an object-oriented memory device. The object-oriented memory device includes at least one physical memory and a hardware controller. The hardware controller is coupled intra the object-oriented memory device to the at least one physical memory. The hardware controller (i) decodes an object-oriented message received from a hardware client of the object-oriented memory device and (ii) performs an action for the hardware client based on the object-oriented message received and decoded. The object-oriented message is associated with an object instantiated or to-be-instantiated in the at least one physical memory. The action is associated with the object. The object-oriented memory device alleviates the hardware client(s) from having to manage structure of respective data stored in the at least one physical memory, obviating duplication of code among the hardware clients for managing same and efforts for design and verification thereof.

Abstract: Systems and methods for optimal load distribution and data processing of a plurality of files in anti-malware solutions are provided herein. In some embodiments, the system includes: a plurality of node processors; a control processor programmed to: receiving a plurality of files used for malware analysis and training of anti-malware ML models; separating the plurality of files into a plurality of subsets of files based on byte size of each of the files, such that processing of each subset of files produces similar workloads amongst all available node processors; distributing the plurality of subsets of files amongst all available node processors such that each node processor processes its respective subset of files in parallel and within a similar timeframe as the other node processors; and receiving, by the control processor, a report of performance and/or anti-malware processing results of the subset of files performed from each node processor.

Abstract: A method for creating a virtual machine includes receiving a virtual machine creation request comprising parameter information of a virtual network interface card occupied by a to-be-created virtual machine, obtaining current resource usage information of the network interface card resource pools of at least one computing node, wherein the at least one computing node is deployed on a cloud platform, each computing node comprises a network interface card resource pool comprising physical network interface cards, determining a target computing node, in the at least one computing node based on the parameter information and the current resource usage information, and invoking the target computing node to create the virtual machine.

Abstract: A request to execute a workload that utilizes an amount of resources to be executed is received from a client device. Corresponding resources that are available at multiple non-uniform memory access (NUMA) nodes are received from one or more host systems. A particular NUMA node of the multiple NUMA nodes is identified in view of the particular NUMA node having available resources that are greater than the amount of resources to execute the workload. A scheduling hint is assigned to the workload that indicates that the particular NUMA node is to be used to execute the workload.

Abstract: This application provides a data processing method. The method is applied to a server, the server includes a first container and an Android driver (240), and a first service manager is deployed in the first container. Each container is allocated its respective private memory (2412, 2422, and 2432) and private service linked list (2411, 2421, and 2431), so as to avoid a problem in a conventional technology of data inconsistency and affected service processing caused by sharing of same memory by a plurality of containers.

Abstract: A system and method place an incoming workload within a data center having infrastructure elements (IEs) for execution. Instrumentation data are collected for both individual IEs in the data center, and workload instances executing on each of these IEs. These data are used to train a future load model according to machine learning techniques, especially supervised learning. Future loads, in turn, are used to train a ranking model that ranks IEs according to suitability to execute additional workloads. After receiving an incoming workload, the first model is used to predict, for each IE, the load on its computing resources if the workload were executed on that IE. The resulting predicted loads are then fed into the second model to predict the best ranking of IEs, and the workload is placed on the highest-ranked IE that is available to execute the workload.

Abstract: The present disclosure discloses a distributed model compilation system. A master node of the system determines the logic calculation graph of the model based on model information, divides the logic calculation graph into multiple logic calculation sub-graphs, generates a distributing message for each logic calculation sub-graph, and then transmits the distributing message to a slave node. Each of the slave nodes allocates a local computing resource to compile the logic calculation sub-graph based on the received distributing message, and transmits compilation completion information to the master node. The master node determines the completion of model compilation based on the compilation completion information returned by each slave node, and executes the target work based on the compiled model.

Abstract: In one embodiment, a method includes empirically analyzing a set of active reservations and a current set of consumable resources belonging to a class of consumable resources. Each active reservation is of a managed task type and includes a group of one or more tasks requiring access to a consumable resource of the class. The method further includes, based on the empirically analyzing, clocking the set of active reservations each clocking cycle. In addition, the method includes, responsive to the clocking, sorting a priority queue of the set of active reservations.

Abstract: Presented herein are system and methods for handling processing of data in cloud environments. A server receives a first dataset generated in response to a function of a first application. A server generates a set of identifiers defining a sequence of processing of the first dataset associated with the function. The identifiers include a first identifier indicating the first application as a predecessor for the first dataset and a second identifier indicating a second application as a successor for the first dataset. The server identifies the second application corresponding to the second identifier as the successor for processing the first dataset. The server communicates at least a portion of the first dataset with a second server hosting the second application to receive a second dataset generated by the second application. The server stores the second dataset in the cloud environment.

Abstract: Systems and methods for purposeful computing are disclosed that, among other things, include a user purpose class resource information computing environment. Such environment supports resource purpose classes, and further supports resource identification information sets that characterize their respective subject matter resources. The computing environment can be used to identify and evaluate one or more purpose class subject matter resource members.

Abstract: Systems and methods are provided for assigning and associating resources in a cloud computing environment. Virtual machines in the cloud computing environment can be assigned or associated with pools corresponding to users as dedicated, standby, or preemptible machines. The various states provide users with the ability to reserve a desired level of resources while also allowing the operator of the cloud computing environment to increase resource utilization.

Abstract: An industrial control system may receive processing information from at least two control systems associated with at least two components within an industrial automation system. The processing information may include a processing load value for each of the at least two control systems. The industrial control system may then distribute processing loads associated with the at least two control systems when a total processing load between the at least two control systems is unbalanced.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to manage a computing resource allocation for a software application. In some implementations, a method may include executing a first test function using the distributed computing system at a first plurality of allocation setpoints for the computing resource, based on the execution, obtaining one or more performance metrics for the first test function for each setpoint of the first plurality of allocation setpoints, training a machine learning model based on the obtained one or more performance metrics; and utilizing the trained machine learning model to manage the computing resource for a second function.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.