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.

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 system, method and program product for handling potentially problematic events in an enterprise computing platform. A method is disclosed that includes: determining whether a received event includes a feature driven folder processing event; in response to a determination that the event is not a feature driven folder processing event, process the event and determine whether a file count for a folder is impacted; in response to the file count being impacted, evaluate the file count relative to a set of processing thresholds to determine which feature driven folder processing events are enabled for the folder, and to update associated metadata for the folder; and in response to a determination that the event is a feature driven folder processing event, using the computing device to check the metadata for an associated folder to determine whether a requested feature is enabled.

A method and node of a network of clusters supporting containerized workloads running in workload cluster namespaces in communication with at least one workload cluster are disclosed. In one aspect, a method implemented in a workload cluster in a network of workload clusters supporting containerized workloads running in cluster namespaces in communication with at least one workload cluster is provided. A request is received from a cluster user quota controller, the request containing a proposed set of resource limits for a cluster namespace of the cluster user. The proposed set of resource limits to be evaluated is relayed to a cluster administrator quota controller. A request is received from a cluster administrator quota controller. The request contains a first set of resource limits selected from the proposed set of resource limits to apply to a cluster namespace of the cluster user.

At a first compute instance run on a virtualization host, a local instance scaling manager is launched. The scaling manager determines, based on metrics collected at the host, that a triggering condition for redistributing one or more types of resources of the first compute instance has been met. The scaling manager causes virtualization management components to allocate a subset of the first compute instance's resources to a second compute instance at the host.

Embodiments are described for an autonomously and dynamically allocating resources in a distributed network based on forecasted a-priori CPU resource utilization, rather than a manual throttle setting. A multivariate (CPU idle %, disk I/O, network and memory) rather than single variable approach for Probabilistic Weighted Fuzzy Time Series (PWFTS) is used for forecasting compute resources. The dynamic throttling is combined with an adaptive compute change rate detection and correction. A single spike detection and removal mechanism is used to prevent the application of too many frequent throttling changes. Such a method can be implemented for several use cases including, but not limited to: cloud data migration, replication to a storage server, system upgrades, bandwidth throttling in storage networks, and garbage collection.

Systems and methods for allocating resources are disclosed. Resources as processing time, writes or reads are allocated. Credits are issued to the clients in a manner that ensure the system is operating in a safe allocation state. The credits can be used not only to allocate resources but also to throttle clients where necessary. Credits can be granted fully, partially, and in a number greater than requested. Zero or negative credits can also be issued to throttle clients. Segment credits are associated with identifying unique fingerprints or segments and may be allocated by determining how many credits a CPU/cores can support. This maximum number may be divided amongst clients connected with the server.

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.

As part of a container initialization procedure, a maximum number of hardware threads per processor core in a set of cores of a computer system are enabled, the container initialization procedure configuring an operating system executing on the computer system for container execution and configuring a first container for execution on the operating system. From a set of available cores in the set of cores, an execution core is selected. In the selected execution core, a number of threads per core to be used during execution of the first container is configured, the number of threads per core specified for the container initialization procedure by a first simultaneous multithreading (SMT) parameter. Using the configured execution core, the first container is executed, the executing virtualizing the operating system.

Systems and methods for connection virtualization in data storage device arrays are described. A host connection identifier may be determined for a storage connection request. A target storage device and corresponding completion connection identifier may be determined for a storage command including the host connection identifier. A command tracker may be stored that associates the storage command with the host connection identifier and the completion connection identifier and the storage command may be sent to the processing queue associated with the completion connection identifier.