A data storage system may have a data storage device with a memory arranged into a plurality of logical namespaces. A power module can be connected to the plurality of logical namespaces and configured to transition at least one memory cell in response to a workload computed for a namespace of the plurality of the logical namespaces to maintain a power consumption of 8 watts or less for the data storage device.

Systems and techniques are provided for monitoring and managing the performance of services accessed by sites on a computing platform. When a performance issue is identified, a service is monitored to determine if calls to the service exceed a threshold completion time. If so, a resource available to call the service is adaptively throttled by the platform.

Systems and methods for computer memory management by a memory coordinator and a plurality of memory consumers. An urgency and memory quota of each memory consumer is initialized by the memory coordinator, which then adjusts the memory quota of each memory consumer such that the sum of the memory quota of each memory consumer does not exceed a finite amount of computer memory. Each memory consumer adjusts its memory usage in response to the quota input and urgency input from the memory coordinator.

Various aspects are disclosed for unified resource management of containers and virtual machines. A podVM resource configuration for a pod virtual machine (podVM) is determined using container configurations. The podVM comprising a virtual machine (VM) that provides resource isolation for a pod based on the podVM resource configuration. A host selection for the podVM is received from a VM scheduler. The host selection identifies hardware resources for the podVM. A container scheduler is limited to bind the podVM to a node corresponding to the hardware resources of the host selection from the VM scheduler. The podVM is created in a host corresponding to the host selection. Containers are started within the podVM. The containers correspond to the container configurations.

A thin-provisioned multi-node computer system comprising a disaggregated memory pool and a pooled memory controller. The disaggregated memory pool is configured to make a shared memory capacity available to each of a plurality of compute nodes. The pooled memory controller is configured to assign, to each compute node of the plurality of compute nodes, a portion of the disaggregated memory pool such that a currently assigned total of assigned portions of the disaggregated memory pool is less than the shared memory capacity. The pooled memory controller is further configured to receive a request to assign an additional portion of the disaggregated memory pool such that the currently assigned total and the additional portion would exceed a predefined threshold amount of the shared memory capacity, to un-assign an assigned portion of the disaggregated memory pool, and assign the additional portion of the disaggregated memory pool.

A method for non-blocking multithreading, the method may include (a) providing, during a deep neural network (DNN) calculation iteration, to a shared computational resource, input information units related to multiple DNN threads; (b) determining whether to reduce a numerical precision of one or more DNN calculations related to at least one of the multiple DNN threads, and (c) executing, based on the determining, DNN calculations on at least some of the input information units to provide one or more results of the DNN processing.

A method and apparatus of a network device that allocates a shared memory buffer for an object is described. In an exemplary embodiment, the network device receives an allocation request for the shared memory buffer for the object. In addition, the network device allocates the shared memory buffer from shared memory of a network device, where the shared memory buffer is accessible by a writer and a plurality of readers. The network device further returns a writer pointer to the writer, where the writer pointer references a base address of the shared memory buffer. Furthermore, the network device stores the object in the shared memory buffer, wherein the writer accesses the shared memory using the writer pointer. The network device further shares the writer pointer with at least a first reader of the plurality of readers. The network device additionally translates the base address of the shared memory buffer to a reader pointer, where the reader pointer is expressed in a memory space of the first reader.

A network device includes computing resources for utilization by processes hosted by the network device and a computing resources manager. The computing resources manager automatically instantiate a new instance of a first process of the processes upon termination of the first process; makes a determination that the network device has entered an out of computing resources state; and in response to the determination: performs an action set to increase a quantity of the computing resources that are available for allocation to the processes.

A system that provides services to clients may receive and service requests, various ones of which may require different amounts of work. The system may determine whether it is operating in an overloaded or underloaded state based on a current work throughput rate, a target work throughput rate, a maximum request rate, or an actual request rate, and may dynamically adjust the maximum request rate in response. For example, if the maximum request rate is being exceeded, the maximum request rate may be raised or lowered, dependent on the current work throughput rate. If the target or committed work throughput rate is being exceeded, but the maximum request rate is not being exceeded, a lower maximum request rate may be proposed. Adjustments to the maximum request rate may be made using multiple incremental adjustments. Service request tokens may be added to a leaky token bucket at the maximum request rate.

Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.