2990 results about "Workload" patented technology

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

The invention provides an intelligent collaborative management method and system for network security operation and maintenance work, and relates to the technical field of network security, and the method comprises the steps: obtaining operation and maintenance work order information containing a work order priority and a processing time limit, and converting the operation and maintenance work order information into a work order feature vector; inputting the skill score, the historical completion rate and the workload of the operation and maintenance personnel into a multi-objective optimization model, and calculating a work order matching degree score and an emergency degree score; performing state coding on the work order feature vector, the personnel feature and the constraint condition by adopting a multi-layer perceptron, extracting spatial correlation by utilizing a double Q network, and calculating a target Q value; and generating a work order distribution scheme based on a Pareto optimal solution algorithm, monitoring a processing state in real time through a task collaborative scheduling model, and generating a collaborative scheduling strategy. The intelligent level of work order distribution is improved, collaborative management of operation and maintenance tasks is realized, and the operation and maintenance efficiency is improved.

An illustrative method for operating an agent configuration may include providing an unprivileged agentless workload scanning configuration to collect non-runtime workload data associated with a workload deployed within a cloud environment, determining one or more properties of the workload based on the non-runtime workload data, and performing an operation with respect to a privileged agent configuration deployed within the cloud environment and configured to collect runtime workload data associated with the workload. Likewise, an illustrative method for operating an agentless configuration may include providing a privileged agent configuration to collect runtime workload data associated with a workload deployed within a cloud environment, determining one or more properties of the workload based on the runtime workload data, and performing an operation with respect to an unprivileged agentless scanning configuration deployed within the cloud environment and configured to collect non-runtime workload data associated with the workload.

An illustrative method includes accessing, by a data platform configured to monitor a compute environment, permissions data representative of a set of permissions that specify how an entity is entitled to interact with resources within the compute environment; collecting, by the data platform, runtime workload data representative of an actual interaction by the entity with the resources over a period of time; and performing, by the data platform based on the runtime workload data, an operation associated with modifying the set of permissions for the entity.

An illustrative storage management system is configured to provide persistent data storage to workloads managed by a container orchestration platform deployed in a cluster of nodes. The storage management system is configured to receive a request comprising identifiers indicative of a plurality of layers associated with a virtual machine to be run in the cluster, generate, based on the request, a virtual machine disk image comprising the plurality of layers, the virtual machine disk image configured to be used to run the virtual machine in the cluster, and provide the virtual machine disk image in response to the request. In some implementations, the virtual machine disk image is used by a virtual machine handler associated with the container orchestration platform to run the virtual machine in a virtual machine type pod that is managed by the container orchestration platform.

An illustrative method includes identifying, based on static workload data associated with a compute environment, one or more attack paths from a network to one or more datasets associated with an entity, accessing runtime workload data associated with the compute environment, and performing, based on the runtime workload data, a risk mitigation operation associated with the one or more attack paths.

The disclosure herein describes scheduling execution of artificial intelligence (AI) workloads in a cloud infrastructure platform. A global scheduler receives AI workloads associated with resource ticket values. The scheduler distributes the AI workloads to nodes based on balancing resource ticket values. Local schedulers of the nodes schedule AI workloads on resources based on the resource ticket values of the AI workloads. Based on scheduling the AI workloads, coordinator services of the local schedulers execute the distributed AI workloads on the infrastructure resources of the nodes. The disclosure further describes scheduling AI workloads based on priority tiers. A scheduler receives AI workloads, and each AI workload is associated with a priority tier indicative of a preemption priority while being executed. The AI workloads are scheduled for execution on a distributed set of nodes based on the priority tiers and then execute based on the scheduling.

Embodiments are directed to workload allocation for file system maintenance. A file system that includes storage nodes and snapshots may be provided such that each snapshot may be associated with a plurality of data blocks. If snapshots are deleted further actions may be performed, including: determining the dead blocks associated with the deleted snapshots such that each dead block may be a data block that may be unassociated with undeleted snapshots; adding the plurality of dead blocks to dead trees located on the storage nodes; determining an urgency score based on a workload model and file system metrics; determining delete tasks based on the urgency score; determining a portion of the storage nodes based on a number of delete tasks; and executing the delete tasks on the portion storage nodes to delete the dead blocks to return storage capacity to the file system.

Systems and methods for enforcing tag-based policy on dynamic workloads include monitoring, via a cloud-based system, traffic associated with one or more customers of the cloud-based system; receiving a packet from a workload associated with a customer of the one or more customers; performing a tag lookup at one or more nodes of the cloud-based system based on the packet; enforcing one or more policies based on the tag lookup. Based on no tags being found for the workload during the tag lookup at the one or more nodes, the nodes are adapted to drop the packet; query the one or more cloud connectors for workload information; and receive, in a next packet, all tags and a version associated with the workload.

The invention discloses a parallel processor dynamic resource allocation system and method based on reconfigurable hardware, and relates to the technical field of computer chips, and the system comprises a workload monitoring module which is responsible for monitoring the workload type and the resource demand of a task processed by a parallel processor in real time, and determining the task type and the resource demand by identifying the task type and the resource demand; a monitoring result is fed back to the resource allocation control module; the resource allocation control module is responsible for generating a corresponding resource allocation control signal based on feedback information of the workload monitoring module and dynamically configuring the reconfigurable hardware module; the reconfigurable hardware module is responsible for realizing efficient adaptation to diversified tasks through a plurality of reconfigurable units formed by programmable logic devices according to different working load dynamic reconfiguration functions and connection modes; and the data caching and transmission module is responsible for cross-module data circulation and global data storage. Different task requirements can be accurately adapted, and the resource utilization rate is improved.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

Systems and methods are described for allocating requests to implement new workloads within a set of servers. Each server can have a given proportion of various resources, based on a hardware configuration of the server. Placing multiple workloads that do not match that proportion can result in stranded resources, which are unused but cannot be used due to a lack of other required resources. Embodiments of the present disclosure include load balancer that routes workload requests based on the proportion of resources expected to be used by the workload, by selecting a target server based on whether the target server hosts other workloads that are correlated or anti-correlated with the requested workload. To reduce maintained state information, the load balancer can characterize workloads in terms of clusters, rather than maintaining information as to individual workloads.

The invention discloses a database operation and maintenance method, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a user intention matched with an operation and maintenance demand problem of a user; if the intention is a general operation and maintenance knowledge acquisition demand, performing retrieval in a knowledge base according to an operation and maintenance demand problem, and generating a first response result for feedback; if the intention is an actual operation and maintenance scene knowledge acquisition demand, real-time operation data of a database is acquired through a database monitoring module, retrieval is performed in a knowledge base according to the real-time operation data and an operation and maintenance demand problem, and a second response result is generated for feedback; and if the intention is to execute the target operation and maintenance task, retrieving an operation and maintenance instruction set matched with the target operation and maintenance task in the knowledge base, and issuing the operation and maintenance instruction set to an operation and maintenance task execution module, so that the operation and maintenance task execution module arranges and executes the target operation and maintenance task according to the task priority. According to the embodiment of the invention, the workload of operation and maintenance personnel is reduced while the operation and maintenance efficiency is improved.

The invention discloses a certificate management method and device, computer equipment and a storage medium. The method comprises the following steps: reading certificate information in a system; judging whether the certificate expiration time in the certificate information is smaller than a set threshold value or not; and if the time from the expiration of the certificate in the certificate information is smaller than a set threshold value, alarm information is sent out. By implementing the method, service interruption or data security risks caused by expiration of the certificates can be effectively avoided, continuity and stability of application services are ensured, the workload of operation and maintenance personnel in the aspect of certificate management is reduced, the operation and maintenance personnel do not need to manually check the expiration time of each certificate, and the operation and maintenance efficiency is improved. The automatic management mode not only reduces the operation labor cost, but also improves the operation and maintenance efficiency, and the technical scheme can be applied to the fields of medical treatment and finance.

The invention particularly relates to an automatic index creating method based on a large model. According to the automatic index creation method based on the large model, after a user inputs a demand and carries out preprocessing, named entity recognition is carried out by utilizing the large language model, key entity information is extracted, and a structured demand parameter is generated; matching a template from the SQL template library and dynamically filling the template, and obtaining an SQL statement after grammar verification and performance optimization; connecting a data source through a database connection technology, executing the SQL statement, and obtaining an index calculation result; and checking the result, defining parameters of the API interface according to the interface specification, generating an interface document, and issuing the interface document. According to the automatic index creation method based on the large model, the working efficiency is greatly improved, the labor cost is reduced, meanwhile, index calculation errors caused by manual understanding deviation can be avoided, the accuracy of an index calculation result is ensured, the interface adaptation and development workload is reduced, the system integration difficulty is reduced, and the implementation is easy. And the data sharing and interaction efficiency is improved.

An apparatus providing workload integrity and passive noise analysis via secure virtualized telemetry is disclosed. The apparatus includes processor circuitry comprising a secure telemetry endpoint circuitry to: maintain a fingerprint counter that is to provide measurements over a set of base telemetry counters of the processor circuitry, wherein the base telemetry counters provide measurement data to the secure telemetry endpoint circuitry; receive new measurements from the set of base telemetry counters; apply post-processing to the new measurements to at least one of normalize the new measurements, reduce dimensionality among the new measurements, or reduce attestation false positives to generate a post-processed version of the new measurements; update a value of the fingerprint counter by concatenating the post-processed version of the new measurements to the value; and recompute a hash digest of the fingerprint counter based on the updated value of the fingerprint counter.

An embodiment for optimally organizing and dispatching workloads in systems having multiple processor types. The embodiment may retrieve processor information and historical task data associated with a target system. The embodiment may calculate and map, for tasks performable by the target system during a series of candidate timeslots, average expected processor usage values for a series of different processor types available within the target system. The embodiment may construct, based on the calculated and mapped average expected processor usage values for the tasks performable by the target system, a workload dispatch graph for a candidate workload to be performed by the target system, the workload dispatch graph including dispatch strategies. The embodiment may determine, for the candidate workload, based on the constructed workload dispatch graph, an optimal dispatch strategy having a lowest expected processor cost value.

The invention discloses a large model cue word intelligent design and verification system supporting multi-person cooperation, which designs cue words in a manner of cue word project management and multi-person cooperation to realize specialized design of the cue words. The difficulty of cue word design is simplified by using AI aided design; the operation stability and accuracy of cue words in different models and different computing power are verified through single-point testing and batch testing, so that cue word verification and iteration form a closed loop, and the cue word quality is improved; after the cue word is published, directly outputting an interface with an access key for external calling, and storing the interface as a cue word case into a cue word knowledge base; compared with an existing system and technology, the cue word version management difficulty is effectively reduced, the workload of cue word designers is reduced, the cue word quality is improved, and the risk of cue word asset loss is reduced.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

The invention discloses an IT equipment maintenance task dynamic scheduling method fused with fault prediction, and relates to the technical field of maintenance task scheduling. The IT equipment maintenance task dynamic scheduling method fused with fault prediction comprises the following steps: constructing an equipment state spatial-temporal characteristic matrix and a historical fault frequency matrix of IT equipment, and determining an occurrence frequency matrix of each fault type of the IT equipment in combination with a working condition correction factor stored in a database; determining a fault maintenance task amount based on the number of IT devices and the occurrence frequency matrix of each fault type; according to the system and the method, the expected completion state of the fault maintenance task is determined based on the current working state of the resident maintenance personnel, and the working mode and personnel of the resident maintenance personnel are adjusted based on the expected completion state of the fault maintenance task, so that the fault quantity can be intelligently evaluated, and the manual workload distribution can be optimized to improve the fault repair efficiency. Downtime is reduced, and user experience is improved.

A method for managing a workload deployment includes: receiving a request; obtaining metadata and a policy associated with an edge node (EN); analyzing, against the policy, the request and the metadata to infer a current state (CS) of the EN; making, based on the analyzing, a determination that the CS of the EN is healthy and a workload associated with the request is suitable for the EN; and sending, based on the determination, a response to a scheduler to indicate that the scheduler is allowed to deploy the workload to the EN.

A system and method for inspecting multiple instances across cloud computing environments for a cybersecurity issue is configured to detect a code object in a configuration code file, the code object utilized to deploy a virtual instance in a cloud computing environment; generate in a security graph a code object node representing the code object; generate in the security graph a resource node representing a virtual instance deployed in a first cloud computing environment based on the code object, wherein the resource node is connected to the code object node; detect a cybersecurity issue on the virtual instance; and generate an instruction to inspect a second virtual instance deployed in a second cloud computing environment based on the code object, the second virtual instance represented by a second resource node connected to the code object node.

The disclosed computing device can include collection circuitry configured to collect current resource utilization per shared resource location. The computing device can also include formulation circuitry configured, using expected resource utilization information of a new workload and the current resource utilization, to provide a scheduler with one or more expected utilization metrics. Various other methods, systems, and computer-readable media are also disclosed.

Confidential workload provisioning is disclosed. To overcome various hardware constraints, a workload (e.g., image or container) to be executed confidentially in an enclave is verified and then re-signed by the cloud service. Re-signing the workload allows workloads to be provisioned confidentially in enclaves or other secure environments while eliminating hardware constraints that may impact workload provisioning.

Systems and methods are provided for determining expert placement layouts and / or prompt and toke routing for a mixture of experts (MoE) model. In some instances, an expert workload distribution with respect to a plurality of experts is determined based on a gating neural network. In some instances, an expert placement layout with respect to a plurality of computing units is determined based on the determined expert workload distribution. In some instances, a two-level routing strategy is provided to first adaptively route an incoming prompt to a suitable computing device, then perform a token routing within that computing device to ensure workload balance between different computing units of that computing device.