329 results about "Service-level agreement" patented technology

A computer-implemented method for automatically managing applications in environments using AI driven autonomic application management framework is disclosed. The computer-implemented method includes obtaining items of data from electronic devices associated with users, and databases; determining semantics and structure of natural language texts associated with the service level agreements (SLAs) based on analysis of natural language texts, using a first AI model; extracting service level objectives (SLOs) and associated metrics corresponding to services specified in SLAs; obtaining first real-time data including actual performance levels, and service level indictors, of the services, from monitoring platforms; determining whether actual performance levels of services, are compliant with expected performance levels; automatically updating the SLOs and associated metrics based on deviations of actual performance levels of the services from expected performance levels; and generating insights associated with actions, to be applied to corresponding services, to be performed to automatically manage the applications.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention relates to the technical field of electric power communication, in particular to an electric power communication resource topological optimization method and system based on a graph database. The specific implementation process comprises the following steps: constructing an electric power communication resource topological graph comprising communication exchange nodes, data transmission links and attribute information through a graph database; analyzing and quantifying the received business type into a routing constraint condition according to a business service level protocol; performing dynamic weighting calculation on each candidate communication path meeting the routing constraint condition, evaluating comprehensive performance indexes and outputting an optimal communication path; and converting the optimal communication path into a topology configuration instruction capable of guiding network equipment to carry out data deployment, and carrying out dynamic optimization on the power communication resource topology. According to the method, the service quality requirements of different services are quantified into dynamically adjusted routing constraints and weights, so that reasonable allocation of power communication resources and effective optimization of network topology are realized, and the working efficiency of a power communication resource network is improved.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for network slice feasibility assessment for slice orchestration in a wireless network. Some aspects relate to providing on-demand approval or rejection of a requested network slice at a device associated with service management. The device may select respective resource allocations of the requested network slice for each cell of a set of cells in a wireless network, add the respective predicted resource allocations to respective current resource utilizations at each of the cells, and output a recommendation associated with the requested network slice in accordance with the summations. The device may select the respective resource allocations of the requested network slice in accordance with a service level agreement (SLA) of the requested network slice and, in some implementations, observed network conditions at each of the cells in the wireless network.

Systems, apparatus, articles of manufacture, and methods are disclosed to test an automated query language by interfacing with a query endpoint, generating a first test suite based on a plurality of combinations of a plurality of attributes corresponding to an API specification, transmitting the first test suite to be executed on the query endpoint, the first test suite to include instructions to cause the query endpoint to access data stored in a database accessible to a plurality of secondary endpoints, obtaining a response time corresponding to the access of the data, comparing the response time corresponding to the access of the data with a service level agreement required response time, and in response to the response time corresponding to the access of the data being longer than the service level agreement required response time, notifying a developer of the plurality of secondary endpoints.

The invention provides a resource urgency degree evaluation and dynamic scheduling method and system based on behavior characteristics, and the method comprises the steps: collecting equipment protocol characteristic data, equipment resource state data and user historical behavior data, and carrying out the preprocessing, and obtaining a standardized data set; by fusing a convolutional neural network, a bidirectional gating circulation unit and a network model of an attention mechanism, extracting spatial-temporal characteristics and a long and short term dependency relationship of the standardized data set, and generating depth behavior characteristics; dynamically calculating a task urgency degree weight from three dimensions of business priority, an equipment performance baseline and environment health degree by adopting a fuzzy analytic hierarchy process based on the depth behavior characteristics, and generating a comprehensive urgency degree score; according to the comprehensive urgency score, generating a dynamic scheduling strategy and executing resource allocation; and monitoring the resource utilization rate and the service level agreement compliance rate, and feeding back for optimizing a subsequent scheduling strategy.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

This disclosure provides systems, methods, devices, and apparatuses, including computer programs encoded on computer storage media, for network slice feasibility assessment for a latency-based service level agreement (SLA). Some aspects relate to providing on-demand approval or rejection of a network slice request at a device in accordance with a latency threshold associated with an SLA of the requested network slice. In some implementations, the device may receive load information associated with the requested network slice. The device may select, in accordance with the latency threshold and the load information, a resource allocation for the network slice. In some implementations, the device may output an indication to accept or reject the network request according to the resource allocation. In some implementations, the device may use one or more machine learning (ML) models to select the acceptance or rejection of the network slice request, to select the resource allocation, or both.

Systems and methods for monitoring and enforcing a service level agreement for an entity are provided. A system includes a scanning engine, an assessment engine, a terms repository, an agreements repository, an assessments repository, and a processing circuit. The processing circuit is configured to perform operations including: receive device connectivity data; determine an entity; identify a data set of computing resources associated with the entity; reference a service level agreement associated with the entity; parse at least one term from the service level agreement; retrieve an assessment map for the at least one parsed term, wherein the assessment map comprises a key performance indicator, a data set of actual service level values, and a response protocol; score the identified data set of computing resources associated with the entity; determine an assessed service level value; store the assessed service level value; and monitor the identified data set of computing resources.

Techniques, methods, and solutions for supporting accurate and resilient positioning, navigation, and timing (PNT) services. In some examples, a cellular PNT (Cell-PNT) network may provide end-to-end monitoring and control of PNT capabilities. For example, a server may coordinate resource allocation between devices. The server may receive a service request and requested performance indicators according to a service level agreement. The server may additionally receive determined performance indicators associated with the service. Based on differences between determined performance indicators and requested performance indicators, the server may determine how to adjust resources to satisfy the requested performance indicators. Indication of the adjusted resources may be transmitted to a device associated with the determined performance indicators. The device may adjust data indications and signals based on the adjusted resources.

The embodiments herein provide a method for managing knowledge for solving vulnerability issues using an organizational knowledgebase. The method includes linking a vulnerability management platform knowledgebase (KB) article to an organization's core documents. The method further includes applying a vulnerability management platform formula, to enhance the knowledge management and security practices within the organization's knowledgebase. The method for applying the vulnerability management platform formula comprises tagging (CVEs) or (CWEs), to the vulnerability platform knowledgebase (KB) articles; attaching rich articles as links to a ticket tracking system; linking the vulnerability management platform KB articles to a new ticket created; and automatically linking a code commit to the vulnerability management platform KB articles. The method further comprises defining and configuring a plurality of Service Level Agreements (SLAs) at an application or sub-application level to set acceptable timelines for acknowledging and mitigating security concerns.

The invention relates to an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The present invention belongs to the technical field of data processing, and particularly relates to an intelligent computing power scheduling method and system based on dynamic programming. By real-time monitoring the current available resource information of multiple computing power clusters, calculating the resource difference value between clusters to evaluate the balance of resource allocation, and determining a preliminary computing power grouping scheme. On this basis, according to the historical task types and resource consumption characteristics, match the suitable task types for each group, and adjust the preliminary grouping scheme to form an optimized computing power grouping. Using the optimized computing power grouping, calculate the service capacity scores of each group according to the preset service level agreement, and provide a recommended set of computing power resource services to users after sorting by the scores. The present invention optimizes the computing power grouping through a dynamic evaluation mechanism, ensures the balanced resource allocation and can meet the requirements of different types of tasks, and improves the overall utilization rate and service quality of computing power resources.

An approach is provided for determining a carbon footprint-based consumption of cloud resources. Cloud resource requirements and a carbon footprint cap of a workload of a cloud consumer are received. The requirements and cap are based on a sustainability target, published by the cloud consumer, and subscribed by cloud service providers. A list of cloud resources that satisfy the requirements are sent. Carbon emission values of the cloud resources at different load levels of the workload are sent. Based on a service level agreement requirement, a criticality level, and a peak load duration of the workload, and previous success rates of satisfying cloud resource requirements by cloud service providers, an optimized configuration of cloud resource(s) and cloud service provider(s) is selected. The cloud resource(s) are selected from the list and have a carbon footprint that does not exceed the cap at a given load level.

A system can determine that a first containerized function invokes a second containerized function. The system can identify a first number of instances of the first containerized function, and a second number of instances of the second containerized function. The system can determine a first cost of executing the first number of instances of the first containerized function, and the second number of instances of the second containerized function, wherein the first cost comprises a first computer memory cost, a first computer storage cost, and a first service level agreement violation cost. The system can determine a second cost associated with executing a third number of instances of a third container that comprises the first function and the second function. The system can, in response to determining that the second cost is less than the first cost, execute the third number of instances of the third container.

A network device may receive an indication that a radio access network fails to support a network slice target service level agreement for a user equipment associated with a network slice. The network device may provide, to the radio access network, a network slice identifier associated with the network slice target service level agreement not supported by the radio access network. The network device may provide, to the radio access network, an indication of whether a neighbor radio access network supports the network slice target service level agreement to cause the radio access network to redirect the user equipment to a neighbor radio access network or to move the user equipment to another network slice.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

Systems and techniques for dynamically optimizing a wireless network topology to minimize energy consumption while preserving user quality of experience (QoE) are described. An example technique includes determining a set of applications that have a target service level agreement (SLA). Network traffic is monitored from the set of applications being executed by one or more client STAs within a network. A topology of the network is dynamically adapted to reduce an amount of energy consumption in the network while maintaining a threshold amount of the network traffic that satisfies the target SLA, based on monitoring the network traffic.

Techniques are described for providing service level agreement performance in a link aggregation group computer networking environment. A performance measurement data packet such as a bi-directional forwarding detection (BFD) packet is received. The performance measuring data packet can be considered a parent performance measurement data packet is split into multiple child performance measurement data packets which are each different constituent links of a link aggregation database. The performance of each constituent is tested to determine which constituents satisfy service level agreement parameters. Data packets can then be sent to constituents that meet the data packet's service level agreement performance parameters while still allowing link aggregation grouping.

A network entity for controlling distribution of slice service level agreement (SLA) information of a network slice in a mobile communication network is provided, wherein the network entity is configured to: obtain a plurality of slice SLA indications, wherein each slice SLA indication is obtained from a network control entity of a first plurality of network control entities; and provide to a network control entity local slice SLA information, wherein the local slice SLA information is based on the obtained plurality of slice SLA indications. A network control entity for supporting distribution of slice service level agreement (SLA) information for a mobile communication network is provided, wherein the network control entity is configured to: provide a plurality of slice SLA indications to a network entity for controlling distribution of slice SLA information of a network slice; obtain local slice SLA information related to a network slice from a second entity, wherein the local slice SLA information is based on the obtained plurality of slice SLA indications.

The invention discloses a data center uninterruptible power supply intelligent control method and device based on reinforcement learning, and relates to the technical field of energy management. The method comprises the steps that a health perception state space is constructed, and the state space comprises operation state features and standby battery multi-dimensional health vectors generated through training on a plurality of distributed uninterruptible power supply stations through a federated learning method; defining a multi-target reward function for comprehensively quantifying the economic cost based on the time-of-use electricity price, the standby duration satisfaction degree of the service level agreement and the battery life loss cost; through a reinforcement learning algorithm with a security constraint layer, based on the state space and the reward function, training an intelligent agent to learn an optimal power supply control strategy, the security constraint layer forcibly executing a preset operation security boundary in training and execution; and according to the learned optimal strategy, a real-time control instruction for the uninterruptible power supply is generated and executed.

This disclosure describes techniques for routing application traffic on a network path based on an application-specific Service Level Agreement (SLA) threshold that may be dynamically and / or automatically determined. An edge device of an SD-WAN may send probes over the SD-WAN over multiple paths and may determine network telemetry data representing the network performance associated with the multiple paths. The edge device may then send the network telemetry data to a control plane. The control plane may also receive quality of experience (QoE) data indicating the quality of an application experience for a user. Based on the network telemetry data and the QoE data, an application-specific SLA threshold may be determined for the application. The edge device may then route traffic of the application through a path of the multiple paths based at least in part on the path satisfying the SLA threshold.

This disclosure describes techniques for improving routing policy awareness in a network. The method includes detecting, by a controller, an application initiated for use at an edge node of a network. Then, generating, by an analytics engine coupled to the controller, analytical data of traffic flow at the edge node of the network wherein the traffic flow is in accordance with a routing policy for routing traffic associated with the application. Further, routing of the traffic through a path from one or more paths configured at the edge node that is in accordance with at least a Service Level Agreement (SLA) for traffic flow. Also, in response to an SLA violation during routing of the traffic, causing an action, by the controller, of routing traffic flow through another path that is in accordance with at least the SLA for traffic flow based on analytical data received of the traffic flow.

The invention discloses a dynamic resource allocation method and system based on 5G slices, and relates to the field of communication, and the method comprises the steps: collecting multi-domain network operation parameters and service portrait information; analyzing an access network resource utilization rate, a transmission link bandwidth, a core network queue length and slice quota information, and outputting a default probability of a slice service level protocol; calculating an elastic interval and a cross-slice borrowing matrix of slice resources in the digital twin model based on the default probability; based on the elastic interval and the cross-slice borrowing matrix, solving is carried out through a security reinforcement learning algorithm, and a target resource allocation vector and an action strategy are generated; and issuing the target resource allocation vector and the action strategy to an execution layer, controlling an access network scheduling weight, a transmission queue threshold and a core network session anchor point, and realizing slice resource dynamic allocation. According to the invention, the resource utilization rate is effectively improved, the cross-domain mismatching risk is reduced, and the service continuity and stability in a complex service scene are enhanced.

Embodiments are described for providing backup resources in a multi-tier network, by providing a plurality of different storage devices, each with different operating characteristics and costs for storage of data assets, and organizing the storage devices into a plurality of tiers based on respective operating characteristics, such as based on performance versus cost parameters. Certain Service Level Agreements (SLAs) dictating storage requirements for each of the data assets to a backup program are provided to the backup system, and a mapper maps each SLA to one or more tiers of the plurality of tiers based on the storage requirements of a respective SLA to the operating characteristics of each tier.

The invention discloses a load-aware large model reasoning service routing method and device, relates to the technical field of computers, comprehensively captures the real load condition of a reasoning execution unit through multi-dimensional operation state data, realizes the accurate quantification of the load state in combination with a multi-index weighted evaluation model, and then adapts to a service demand dynamic distribution request to realize the routing of the load-aware large model reasoning service. Therefore, the reasoning request can efficiently match the optimal resource, the imbalance problem that part of nodes are overloaded and queued and part of nodes are idle is avoided, and the effects of adapting to the characteristics of high computing power demand, large response time fluctuation and the like of the large model reasoning service are achieved. Therefore, on the premise of guaranteeing different user service level agreement (SLA) requirements, the cluster resource utilization rate and the service response efficiency can be remarkably improved, and meanwhile, the stability and the scene adaptation capability of the large model reasoning service are enhanced.

The invention discloses a load-aware multi-micro-service replica elastic scaling method in a computing power network, and aims to cope with a dynamic online working load environment, ensure that end-to-end response time delay meets a service level agreement (SLA) and improve the utilization rate of micro-service resources at the same time. According to the method, firstly, historical performance data of all micro-services in a Kubernetes cluster are collected through plug-ins such as Prometheus, cAdvisor and Istio, the data are stored in a time sequence database, and a training data set is constructed; then, a graph data structure is constructed according to the calling relation between the micro-services and the micro-service features in the data set, the multi-micro-service CPU utilization rate and a P90 response delay predictor are trained through a graph attention network, and therefore an intelligent agent interactive simulation environment is formed. Based on the simulation environment, a deep context multi-arm bandit model is designed and trained, and an optimal copy adjustment strategy is selected in a large state space and a complex nonlinear relation in combination with an upper confidence bound (UCB) strategy, so that automatic horizontal stretching of the multi-micro service system is realized. And finally, deploying the trained model in an actual environment, regularly monitoring and updating context information of the micro-service through Prometheus, performing real-time copy adjustment, reducing an SLA violation risk and improving a resource utilization rate.

The invention relates to the technical field of internet risk collaborative management, and discloses an internet data risk early warning and collaborative management system and method.The internet data risk early warning and collaborative management method comprises the steps that a service grid enhanced federal learning unified framework is constructed, and a tetrad structure is formed; a seamless integrated architecture of federal learning and a service grid is realized; developing a federal contribution evaluation mechanism based on a service level agreement; a self-adaptive service discovery and dynamic combination technology is realized, and an optimal service combination scheme is generated; constructing a federated aggregation algorithm of data quality perception, and introducing a quality weight factor optimization model for training; designing a multi-level cache strategy; according to the invention, through the fusion of federal learning and the micro-service architecture, the purpose of keeping efficient collaboration while protecting data privacy is realized, the original data of each participant is always kept locally, only necessary model parameters are shared, and the privacy leakage risk is reduced.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for network slice feasibility assessment for slice orchestration in a wireless network. Some aspects relate to providing on-demand approval or rejection of a requested network slice at a device associated with service management. The device may select respective resource allocations of the requested network slice for each cell of a set of cells in a wireless network, add the respective predicted resource allocations to respective current resource utilizations at each of the cells, and output a recommendation associated with the requested network slice in accordance with the summations. The device may select the respective resource allocations of the requested network slice in accordance with a service level agreement (SLA) of the requested network slice and, in some implementations, observed network conditions at each of the cells in the wireless network.