Abstract: A system, includes processing circuitry; and a memory connected to the processing circuitry, wherein the memory is configured to store executable instructions that, in response to being executed by the processing circuitry, facilitate performance of operations to request deployment software for a telecommunications system from a central inventory; send the deployment software to a load balancer that distributes executable portions of the deployment software to two or more cloud adaptors (CAs) that are operably connected to the load balancer; generate worker threads based on the distributed executable portions of the deployment software; and distribute the worker threads to hybrid clusters for execution of the worker threads and implementation of the deployment software.

Abstract: A method of using a multi-cluster network is provided. The multi-cluster network has at least a plurality of clusters, where each cluster of the plurality of clusters has at least a node and a pod. The method includes collecting cluster and application information of the multi-cluster network. The cluster and application information includes at least a cluster capacity and an application performance metric. The application performance metric corresponds to at least an application. The method further includes analyzing the cluster and application information for a current pod arrangement on each cluster and adjusting at least the application performance metric based at least on the analyzed cluster and application information by taking at least a pod-based action achieving a new pod arrangement on a target cluster. An apparatus for using the multi-cluster network is also provided.

Abstract: Systems and methods for time-based resource allocation are disclosed herein. A system receives requests for intermediate allocation of a set of resources during a first period of time. Each request may include a target entity to receive a portion of the intermediate allocation and a user-linked resource token. The system determines a first subset of the set of resources for which a final allocation of resources from the user is requested when the first period is elapsed. During the first period, the system transfers the set of resources to target entities. During a second period, the system receives a message from a target entity indicating a return of at least a portion of the intermediate allocation. The system updates the set of resources and calculates an updated first subset of resources. Responsive to determining that the second period is elapsed, the system may automatically transmit a resource allocation command configured to trigger final allocation.

Abstract: Example methods and systems for elastic provisioning of container-based graphics processing unit (GPU) nodes are described. In one example, a computer system may monitor usage information associated with a pool of multiple container-based GPU nodes. Based on the usage information, the computer system may apply rule(s) to determine whether capacity adjustment is required. In response to determination that capacity expansion is required, the computer system may configure the pool to expand by adding (a) at least one container-based GPU node to the pool, or (b) at least one container pod to one of the multiple container-based GPU nodes. Otherwise, in response to determination that capacity shrinkage is required, the computer system may configure the pool to shrink by removing (a) at least one container-based GPU node, or (b) at least one container pod from the pool.

Abstract: A system and method for executing self-evolving property graphs on GPU hardware for unbounded experiential processing. The system stores a dynamic property graph comprising event and communication vertices in GPU memory. Input streams are projected to graph vertices through specialized operators. Multiple GPU-executable operator kernels transform the graph through geometric operations including diffusion, geodesic computation, and curvature analysis. These operators are captured as a directed acyclic graph that executes repeatedly without external scheduling, with execution frequency adjusted based on a logarithmic relationship with input stream density. A compression mechanism identifies and removes redundant graph elements based on geometric properties, maintaining memory growth proportional to the logarithm of processed inputs.

Abstract: A method for managing VDI environments includes: obtaining a first resource related parameter (FRRP) for a first resource (FR) and a second resource related parameter (SRRP) for a second resource (SR) from a database; assembling a set of API combinations to generate a set of VDI environments; testing, for a per user average resource utilization value (PUARUV) of the FR and SR, each VDI environment across a range of users based on the FRRP and SRRP; providing the range of users and set of VDI environments to an analyzer, in which the analyzer is instructed to generate a model that ranks the set of VDI environments to obtain a VDI environment that provides the lowest PUARUV; generating a trained model by training the model using the range of users, set of VDI environments, PUARUVs, FRRP, and SRRP; and initiating notification of an administrator about the trained model.

Abstract: Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.

Abstract: A plurality of resource usage profiles is generated, wherein at least two of the resource usage profiles each include a corresponding plurality of resource values that quantify real-time computing resources used by an instance of an application that previously executed under two different corresponding operating conditions. It is determined that a new instance of the application is to be initiated. A particular resource usage profile from the plurality of resource usage profiles is selected. The new instance is initiated using the particular resource usage profile.

Abstract: The present application provides a data processing method, system, electronic equipment and storage medium based on a cloud platform, which are applied to the technical field of cloud computing processing, wherein the data processing method comprises the following steps: obtaining task processing requests submitted by several target users through a distributed system, wherein the task processing requests are requests for processing scientific computing tasks; Determining whether the number of the obtained task processing requests reaches a preset capacity expansion threshold, and if so, generating a workload capacity expansion request; Performing capacity expansion processing on the computing node according to the capacity expansion request; The workload is redeployed based on the computing nodes after capacity expansion processing, so as to execute the scientific computing task based on the redeployed workload.

Abstract: Techniques are described herein for analyzing and tuning database workloads to optimize application performance. In some embodiments, a workload analyzer identifies a captured workload that includes a set of database queries executed within a particular timeframe. The workload analyzer compares the workload within one or more other workloads executed within a previous timeframe to determine differences between the different workloads. For example, the workload analyzer may identify changes in the distributions of queries, including how many queries are unchanged, missing, and/or new. The workload analyzer may further detect changes in the performance of individual queries. The workload analyzer may determine the overall performance impact of such changes on the total workload. Based on the analysis, the workload analyzer may generate reports, alerts, tuning advice, and/or recommendations to boost performance.

Abstract: Techniques described herein relate to a method for providing software defined storage (SDS) services on edge information handling systems (IHSs). The method includes obtaining a composition request associated with performing an SDS service on edge IHSs, wherein the SDS service is associated with a minimum quantity of SDS nodes that is greater than a quantity of available edge IHSs; identifying a first edge IHS and a second edge IHS specified by the composition request; identifying a number of nodes associated with the SDS nodes; allocating heterogeneous compute resources of the first IHS and the second IHS to each SDS node of the SDS nodes; allocating a power domain to each SDS node of the SDS nodes; allocating portions of an at least one hardware resource set to each SDS node of the SDS nodes; and preparing the SDS nodes to perform the SDS service.

Abstract: A method of reconstructing and verifying a proprietary cloud based on state transition is provided, including: obtaining information of an underlying infrastructure platform of a proprietary cloud server cluster to be constructed; if the underlying infrastructure platform meets the deployment requirement, obtaining cluster gene information of a standard proprietary cloud platform, and performing refactoring of an underlying system component, refactoring of an underlying dependency component and refactoring of a product line of a proprietary cloud platform according to the cluster gene information and a state of the proprietary cloud server cluster to be constructed; obtaining user demand information, configuring the cluster gene information according to the user demand information, and selecting a product line component of the standard proprietary cloud platform for deployment, so as to obtain a reconstructed proprietary cloud platform; and verifying a correctness and an effectiveness of a component of the re

Abstract: Systems and methods for universal auto-scaling are disclosed. In one embodiment, a method may include: (1) monitoring, by an auto-scale computer program executed by a computer processor, a utilization level at each of a plurality of data layers in a data pod, wherein each data layer comprises at least one node; (2) comparing, by the auto-scale computer program, each of the utilization levels to a threshold; (3) identifying, by the auto-scale computer program, that one of the thresholds is met or exceeded; and (4) deploying, by the auto-scale computer program, an additional node to the data layer with the met or exceeded utilization level.

Abstract: A computing resource scheduling method includes determining expected cycle numbers of computing resources used in an application scenario based on a first condition and a second condition, and scheduling the computing resources in a remaining available time period according to the expected cycle numbers of the computing resources. The first condition includes that the computing resources are operated according to the expected cycle numbers to have a utilization rate of a remaining available object to be greater than a target utilization rate. The second condition includes that a total performance of the computing resources meets a threshold value when the computing resources are operated according to the expected cycle numbers, and a performance of any one computing resource is positively correlated with computing power of the computing resource.

Abstract: A method and an apparatus for accessing a critical resource, a computer device, and a readable storage medium are provided, to help reduce a quantity of operations for contending for a lock by a thread, and computing resources, and improve performance of an ARM processor. The method for accessing a critical resource includes: selecting a first thread from the first thread and a second thread to contend for a target lock (201), where a first task to be executed by the first thread and a second task to be executed by the second thread correspond to the target lock; and if the first thread obtains the target lock through contention, enabling the first thread to execute the first task, and replace the second thread to execute the second task (202).

Abstract: A computing resource may be monopolized or dominated by a client, if the client has a large quantity of tasks for execution and/or the tasks from that client take a long time to execute. In some embodiments, each client is associated with a respective counter, and the counter is indicative of how much the computing resource has been recently occupied by the client associated with that counter. In some embodiments, the computing resource refrains from executing new tasks for a client if its counter is within a particular range. In some embodiments, a counter increments when the computing resource is occupied by a client and decrements otherwise based on the passage of time. In some embodiments, different counters may have different rates of incrementing or decrementing, or different particular ranges.

Abstract: This disclosure generally relates to enabling a hypervisor of a host machine to provide virtual interrupts to select virtual processors or a set of virtual processors. More specifically, the present disclosure describes how a hypervisor of a host machine may monitor the status of one or more virtual processors that are executing on the host machine and deliver interrupts to the virtual processors based on a number of factors including, but not limited to, a priority of the interrupt, a priority of the virtual processor, a current workload of the virtual processor and so on.

Abstract: A method for processing tasks in parallel is performed by at least one processor, and includes performing a first task associated with a first instruction, determining whether the first instruction is a burst load instruction, in response to determining that the first instruction is the burst load instruction, acquiring a second instruction, and performing a second task associated with the acquired second instruction, in which the first task and the second task are performed in parallel.

Abstract: Systems and methods for determining and reporting actual utilization of a core of a central processing unit (CPU) of a host. Prior to implementation of aspects of the present disclosure, running a poll querying endpoints of a process for work appears to the host's operating system as busy work (e.g., taking full use of the core for the poll duration). However, only a percentage of the duration of the poll is used to process a task of the process, where the remaining duration of the poll is spent querying the endpoints (idle time) and the core is not performing a task. Accordingly, a core utilization reporting system and method automatically detects the processing time of the tasks of a process, determines actual CPU utilization of the core based on a percentage of the time the core is busy polling (doing effectively no work) versus doing actual work (processing a task).

Abstract: Disclosed is a distributed computer system that includes a plurality of worker nodes that are coupled together via a data communication network to exchange data therebetween, wherein computation by the worker nodes is managed within the distributed computer system. The distributed computer system configured to implement a scheme for mapping cryptographic keys to a specific shard in the distributed ledger arrangement that can be modified by a user worker node and an operator worker node of the distributed ledger arrangement; employ a pricing mechanism for specifying transaction costs that incentivizes user worker nodes to submit transactions that are restricted to a single shard; adopt algorithms specifying internal logic for wallet software that access shards so as to minimise their transaction costs; and execute procedures for reorganizing state databases when shards are duplicated or merged so that subsequent transactions can be maximally executed in parallel.