Classical management of qubit requests is provided. In particular, a classical computing device receives a payload from another classical computing device via a classical computing connection, such as a Hypertext Transfer Protocol (HTTP) connection. The classical computing device queries a quantum computing device regarding availability of a qubit, whether targeted or agnostic, according to instructions provided in the payload. Such instructions may include inserting data into a qubit, manipulating a qubit, and/or reserving a qubit. If the qubit is available, the classical computing device sends the payload to the quantum computing device. If the qubit is unavailable, the classical computing device continues to query the quantum computing device until the qubit is available. Such a configuration provides granular control of qubits by a classical computing device and/or shifts management loads from the quantum computing device to the classical computing device.

A data processing system is disclosed that includes one or more processors that can perform producer processes to produce work and consumer processes that can consume work produced by a producer process. The system includes a pool of plural communication resources that may be used for communications between a producer process and a consumer process. The system tracks the usage of communication resources of the pool of communication resources, and allocates a communication resource from the pool of communication resources based on the tracking.

A computer architecture employs multiple special-purpose processors having different affinities for program execution to execute substantial portions of general-purpose programs to provide improved performance with respect to a general-purpose processor executing the general-purpose program alone.

An exemplary embodiment of the present application is a system and method for work allocation optimization. In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing the utilization of agents. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize utilization of agents. The work items are then routed to the agents accordingly.

Methods and systems are provided for assigning nodes to execute functions in a serverless computing environment. In one embodiment, a method is provided that includes receiving a function for execution in a serverless computing environment and identifying a storage pool needed during execution of the function. The serverless computing environment may include nodes for executing functions and a first set of nodes may be identified that implement the storage pool. Colocation measures may be determined between the first set of nodes and a second set of nodes. Available computing resources may be determined for the second set of nodes, such as available processing cores and available memory. The second set of nodes may be ranked according to the colocation measures and the available computing resources and a first node may be selected based on the ranking. The first node may be assigned to execute the function.

Systems, computer-implemented methods, and computer program products that can facilitate performance biased resource scheduling based on runtime performance of a certain workload type on one or more nodes are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a performance component that assigns performance points to different nodes based on execution of one or more workload types. The computer executable components can further comprise a scheduler extender component that modifies a scheduling decision to run a workload type on a node based on the performance points.

A scheduler for a storage node uses multi-dimensional weighted resource cost matrices to schedule processing of IOs. A separate matrix is created for each computing node of the storage node via machine learning or regression analysis. Each matrix includes distinct dimensions for each emulation of the computing node for which the matrix is created. Each dimension includes modeled costs in terms of amounts of resources of various types required to process an IO of various IO types. An IO received from a host by a computing node is not scheduled for processing by that computing node unless enough resources are available at each emulation of that computing node. If enough resources are unavailable at an emulation, then the IO is forwarded to a different computing node that has enough resources at each of its emulations. A weighted resource cost for processing the IO is calculated and used to determine scheduling priority. The weights or regression coefficients from the model may be used to calculate weighted resource cost.

Embodiments of this disclosure disclose a method and system for optimizing resources for cloud-based scalable distributed search data analytics service. The method may include generating a computing capacity rightsizing recommendation on the computing capacity based on the computing utilization metrics and generating a memory capacity rightsizing recommendation on the memory capacity based on the memory utilization metrics. The method may further include determining a recommended instance type for the service resource unit based on the computing capacity rightsizing recommendation and the memory capacity rightsizing recommendation. The method may further include performing a storage volume check on the service resource unit to obtain a storage volume check result. The method may further include determining whether to accept the recommended instance type as a final optimization recommendation based on the storage volume check result, the computing capacity rightsizing recommendation, and the memory capacity rightsizing recommendation.

Systems and methods are provided for offloading a task from a central processor in a radio access network (RAN) server to one or more heterogeneous accelerators. For example, a task associated with one or more operational partitions (or a service application) associated with processing data traffic in the RAN is dynamically allocated for offloading from the central processor based on workload status information. One or more accelerators are dynamically allocated for executing the task, where the accelerators may be heterogeneous and my not comprise pre-programming for executing the task. The disclosed technology further enables generating specific application programs for execution on the respective heterogeneous accelerators based on a single set of program instructions. The methods automatically generate the specific application programs by identifying common functional blocks for processing data traffic and mapping the functional blocks to the single set of program instructions to generate code native to the respective accelerators.

Systems and methods are provided for offloading a task from a central processor in a radio access network (RAN) server to one or more heterogeneous accelerators. For example, a task associated with one or more operational partitions (or a service application) associated with processing data traffic in the RAN is dynamically allocated for offloading from the central processor based on workload status information. One or more accelerators are dynamically allocated for executing the task, where the accelerators may be heterogeneous and may not comprise pre-programming for executing the task. The disclosed technology further enables generating specific application programs for execution on the respective heterogeneous accelerators based on a single set of program instructions. The methods automatically generate the specific application programs by identifying common functional blocks for processing data traffic and mapping the functional blocks to the single set of program instructions to generate code native to the respective accelerators.