Abstract: In some aspects, a non-transitory computer readable storage medium includes instructions stored thereon that, when executed by a processor, cause the processor to detect that system software is proceeding to swap memory content of a virtual machine (VM) from memory to storage, wherein the memory is allocated to the VM; buffer the memory content; and perform alternative memory reclamation of the memory.

Abstract: The present disclosure relates to a method for multi-core communication, an electronic device and a storage medium. The method includes controlling a plurality of cores to run; establishing a communication connection between a publishing core and a receiving core in the plurality of cores based on a communication layer; performing, by the publishing core, an operation on a topic message through calling a preset interface of the communication layer via a publish-subscribe layer; and accessing the topic message in response to the receiving core calling a preset interface of the publish-subscribe layer.

Abstract: Processing may be performed in accordance with a policy to assign roles of winner and loser between two nodes. The roles may be used in connection with deadlock resolution processing. A deadlock or potential deadlock may be detected between the two nodes performing processing for two transactions In response to detecting the deadlock or potential deadlock, using a current state may be used to determine whether to perform the deadlock resolution processing to resolve the deadlock or potential deadlock. The current state may indicate whether assignment of the winner and loser roles between the two nodes is in progress. Responsive to the current state indicating that processing is not in progress to assign roles of winner and loser between the two nodes, the current state may be used perform deadlock resolution processing to resolve the deadlock or potential deadlock. The current state may denote which node is the current winner.

Abstract: Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute a received instruction; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In another embodiment, the core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, to reserve a predetermined amount of memory space in a thread control memory to store return arguments, and to generate one or more work descriptor data packets to another processor or hybrid threading fabric circuit for execution of a corresponding plurality of execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

Abstract: Allocation of computational resource to requested tasks is achieved by running a scheduling operation across a plurality of schedulers, each in communication with a subset of network entities, the schedulers establishing a virtual bus. In certain embodiments, the scheduling operation is able to run continuously, allocating newly arriving task requests as resources become available.

Abstract: Performance of a computing device can be optimized in a mixed workload environment. A management service can be configured to capture telemetry data from web applications or containerized applications and use such telemetry data to detect a scenario. Based on the detected scenario, the management service can select optimized performance settings and cause the optimized performance settings to be applied within the browser or container in which the application is deployed. Machine learning techniques can be employed to detect and define optimized performance settings for a particular scenario.

Abstract: A data processing system comprising a plurality of processing nodes that are arranged to update a model in a parallel manner Each of the processing nodes starts with a different set of updates to model parameters. Each of the processing nodes is configured to perform one or more reduce-scatter collectives so as to exchange and reduce the updates. Having done so, each processing node is configured to apply the reduced set of updates to obtain an updated set of model parameters. The processing nodes then exchange the updated model parameters using an all-gather so that each processing node ends up with the same model parameters at the end of the process.

Abstract: Configuration and dynamic profiling of storage class memory (SCM) devices is provided. Information is retrieved that includes historical SCM device configurations, historical SCM device utilization, functional and non-functional properties of a plurality of SCM devices on a host node, current real time utilization of the plurality of SCM devices by an application workload of a customer running on the host node, and relationships between the plurality of SCM devices, needs of the customer, and resource capabilities and real time resource utilization on the host node. A configuration of each respective SCM device is determined based on retrieved information and an artificial intelligence-predicted SCM device future utilization trajectory of the customer. Each respective SCM device is dynamically configured with a set of SCM device partitions according to a corresponding SCM device profile based on the determined configuration of each respective SCM device of the plurality of SCM devices.

Abstract: Systems, methods, and machine-readable media for monitoring a storage system and correcting demand imbalances among nodes in a cluster are disclosed. A performance manager for the storage system may detect performance imbalances that occur over a period of time. When operating below an optimal performance capacity, the manager may cause a volume to be moved from a node with a high load to a node with a lower load to achieve a preventive result. When operating at or near optimal performance capacity, the manager may cause a QOS limit to be imposed to prevent the workload from exceeding the performance capacity, to achieve a proactive result. When operating abnormally, the manager may cause a QOS limit to be imposed to throttle the workload to bring the node back within the optimal performance capacity of the node, to achieve a reactive result. These actions may be performed independently, or in cooperation.

Abstract: Consumer threads can assist in performing progressive chunking for a data queue. For example, a consumer thread can determine a current-chunk identifier indicating a current memory chunk of an unbounded queue, where the current memory chunk is associated with a producer thread that is different from the consumer thread. The consumer thread can determine a target-chunk identifier indicating a target memory chunk to which the producer thread is to write a data item. In response to determining that the target-chunk identifier is greater than the current-chunk identifier, the consumer thread can append a new memory chunk to the unbounded queue for use as the target memory chunk by the producer thread.

Abstract: A computer-implemented method, a computer program product, and a computer processing system are provided for selecting from among multiple Graphics Processing Unit (GPU) execution modes for a Neural Network (NN) having a size greater than a threshold size. The multiple GPU execution modes include a normal memory mode, an Out-of-Core (OoC) execution mode, and a Unified Memory (UM) mode. The method includes starting an execution on the NN with the UM mode and measuring the memory usage for each of layers of the NN. The method further includes selecting an execution mode based on the memory usage of all of the layers.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: A memory device includes a plurality of memory dies, each memory die of the plurality of memory dies comprising a memory array and control logic. The control logic comprises a plurality of processing threads to execute memory access operations on the memory array concurrently, a thread selection component to identify one or more processing threads of the plurality of processing threads for a power management cycle of the associated memory die and a power management component to determine an amount of power associated with the one or more processing threads and request the amount of power during the power management cycle.

Abstract: Various examples are disclosed for performing cross-cluster load balancing. In some aspects, a workload is selected for cross-cluster migration. A destination cluster is identified for a migration of the workload from a source cluster to the destination cluster. A cross-cluster migration recommendation is generated to migrate the workload from the source cluster to the destination cluster.

Abstract: A system including a distributed network of one or more virtual machines, having a first portion of committed virtual machines dedicated to a user and a second portion of on-demand virtual machines. The system may also include a workload scheduler configured to receive workloads associated with the user. The scheduler may determine whether to schedule a given workload to be performed by a combination of virtual machines in the first and second portions, or by virtual machines included in only the first portion. The given workload may be scheduled to be performed by virtual machines in only the first portion at a first time if a sum of an anticipated resource consumption level of the given workload and a first consumption level of the first portion of virtual machines at the first time is less than or equals a total amount of resources included in the first portion.

Abstract: A method may include receiving, by a privileged component executed by a processing device, bytecode of a packet processing component from an unprivileged component executed by the processing device, analyzing, by the privileged component, the bytecode of the packet processing component to identify whether the bytecode comprises a first command that returns a redirect, analyzing, by the privileged component, the bytecode of the packet processing component to identify whether the bytecode comprises a second command that returns a runtime computed value, and responsive to determining that the bytecode comprises the first command or the second command, setting a redirect flag maintained by the privileged component.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for acquiring information. The method may include: acquiring cloud resource use information; comparing the cloud resource use information with a use rate threshold value, to obtain a comparison result; obtaining use state information of a cloud resource corresponding to the cloud resource use information according to the comparison result; and generating cloud resource state information based on the use state information.

Abstract: A computer-implemented method, a computer program product, and a computer system for placements of workloads in a system of multiple platforms as a service. A computer detects a triggering event for modifying a matrix that pairs respective workloads on respective platforms and includes attributes of running respective workloads on respective platforms. The computer recalculates the attributes in the matrix, in response to the triggering event being detected. The computer determines optimal placements of the respective workloads on the respective platforms, based on information in the matrix. The computer places the respective workloads on the respective platforms, based on the optimal placements.

Abstract: A computer-implemented method, a computer program product, and a computer system for optimizing workload placements in a system of multiple platforms as a service. A computer first places respective workloads on respective platforms that yield lowest costs for the respective workloads. The computer determines whether mandatory constraints are satisfied. The computer checks best effort constraints, in response to the mandatory constraints being satisfied. The computer determines a set of workloads for which the best effort constraints are not satisfied and determines a set of candidate platforms that yield the lowest costs and enable the best effort constraints to be satisfied. From the set of workloads, the computer selects a workload that has a lowest upgraded cost and updates the workload by setting an upgraded platform index.

Abstract: Techniques for transaction tracking for a high availability architecture are described herein. An aspect includes receiving a first request from a client, the first request corresponding to a start of a transaction having transaction affinity. Another aspect includes, based on receiving the first request from the client, generating a transaction tracking token. Another aspect includes sending the first request with the transaction tracking token to a gateway. Another aspect includes receiving a first response corresponding to the first request from the gateway, the first response including middleware instance information corresponding to a middleware instance, wherein a plurality of subsequent requests from the client corresponding to the transaction are processed by the middleware instance corresponding to the middleware instance information.