120 results about "Service-level agreement" patented technology

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.

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.

In one embodiment, a device obtains information regarding temporary routing patches applied to a network. Each temporary routing patch implements a routing change in the network for a specified amount of time to avoid or mitigate against a service level agreement violation. The device evaluates, using the information regarding the temporary routing patches applied to the network, a plurality of replay scenarios for the network. The device determines, based on the plurality of replay scenarios, a long-term configuration change for the network. The device provides an indication of the long-term configuration change for display.

The invention discloses a server computing power resource dynamic scheduling method based on multi-objective optimization, and relates to the technical field of server scheduling, and the method comprises the steps: obtaining real-time operation state data of a server cluster and attribute information of a to-be-scheduled computing task in response to a dynamic scheduling trigger event; based on the real-time operation state data and the attribute information, constructing a multi-objective optimization model; the multi-objective optimization model is solved, and a Pareto optimal solution set representing different resource allocation schemes is obtained; and selecting an optimal scheduling scheme from the Pareto optimal solution set based on a preset operation strategy. The method has the advantages that the dynamic multi-objective optimization model fusing the performance, the energy efficiency and the resource utilization rate is constructed, and online solving is performed by adopting the decomposition-based evolutionary algorithm, so that comprehensive optimization and optimal balance of the computing power resource utilization rate, the task processing efficiency and the overall energy efficiency of the system are realized on the premise of ensuring the service level agreement.

The invention provides an intelligent scheduling management system based on a carbon footprint model, and the system achieves the dynamic coupling of calculation task scheduling and the real-time carbon intensity of a power grid through the cooperation of a carbon footprint modeling engine, a dynamic scheduling controller, a resource state sensing layer and a feedback execution module. The problem that a traditional data center resource scheduling system is lack of carbon sensing capacity is effectively solved. By establishing the dynamic association between the power source and the geographic position and fusing the multi-dimensional equipment energy consumption characteristics and the task demand labels, the scheduling decision automatically responds to the fluctuation of the regional renewable energy sources, and the overall carbon footprint is remarkably reduced on the premise of guaranteeing the calculation performance and the service level agreement. Meanwhile, the improved multi-objective optimization algorithm can efficiently search for an optimal balance point in a solution space formed by carbon emission, default rate and economic cost, the resource energy efficiency utilization rate is further improved through an elastic expansion mechanism based on real-time state perception, and finally unification of data center operation environment benefits and economic benefits is achieved.

The invention discloses a network operation and maintenance automatic management method based on deep learning, and the method comprises the following steps: collecting operation and maintenance data, and carrying out the preprocessing of the operation and maintenance data, and obtaining a standardized data set; adopting a Mama selective state space network to generate an operation and maintenance state vector; inputting the operation and maintenance state vector into the improved CORAL model to generate an anomaly detection result; generating a root cause analysis result by adopting a causal inference method; generating candidate operation and maintenance actions by adopting a constraint modeling method in combination with a service level agreement rule; obtaining an optimal operation and maintenance action by adopting an action evaluation method and a risk analysis method, issuing and executing the optimal operation and maintenance action, and collecting execution feedback data; and generating an operation and maintenance audit report based on the anomaly detection result, the root cause analysis result and the execution feedback data. Intelligent, automatic and auditing management of the whole operation and maintenance process is realized, and the method can be widely applied to intelligent operation and maintenance scenes of a large-scale complex network.

Disclosed is a method (500) for adjusting Service Level Agreement (SLA) parameters in a communication environment (100). The method includes collecting 5 data associated with workload, resource utilization, and performance metrics. The method further includes setting one or more SLA parameters for one or more services included a SLA. Further, the method includes predicting a future workload pattern, resource requirements, and future SLA breaches by analyzing the collected data. Furthermore, the method includes evaluating a performance of the one or more 10 services against SLA target and the future workload pattern. Furthermore, the method includes adjusting the SLA by adjusting the one or more SLA parameters in real-time to maintain a consistent service level in the communication environment.

Various systems and methods for reactive intent-driven end-to-end (E2E) orchestration are described herein. An orchestrator system, includes a processor; and memory to store instructions, which when executed by the processor, cause the system to: receive, at the orchestrator system, an intent-based service level agreement (SLA) for execution of a series of tasks on a plurality of compute nodes; calculate, based on the intent-based SLA, intermediate latency thresholds corresponding to each task of the series of tasks; calculate slack estimates based on the latency thresholds and real-time telemetry of the plurality of compute nodes or real-time telemetry of connections between the plurality of compute nodes; monitor execution of the series of tasks on the plurality of compute nodes; and perform a corrective action in response to determining that the execution of the series of tasks is predicted to exceed one of the intermediate latency thresholds.

Controlling carbon emissions by workload at the storage level is provided. A storage system that can run a workload within a remainder of a specified time period without exceeding a maximum carbon dioxide (CO2) equivalent emission threshold level defined for the workload in a sustainability service level agreement (SLA) is identified based on analyzing retrieved CO2 equivalent emission data in a time-series format from a plurality of storage controllers corresponding to a plurality of storage systems using a set of machine learning models. The workload is migrated to the storage system prior to the specified time period being met to decrease CO2 equivalent emissions of the workload in accordance with the sustainability SLA.

A method may include, obtaining, by one or more processors, transaction logs of a database server captured in a predetermined time period, identifying, by the one or more processors, a rate of change of data within the transaction logs, classifying, by the one or more processors, portions of the time period according to the rate of change of data within the portions of the time period, and setting, by the one or more processors, a service level agreement (SLA) for each portion of the time period according to the classification of each portion.

The invention provides a dynamic data-backup scheduling system. The system includes a memory storing one or more processor-executable routines and a processor communicatively coupled to the memory. The processor is configured to execute one or more processor-executable routines to determine a dynamic backup schedule for one or more computing machines based upon data change patterns of the respective computing machines. The processor is further configured to access user inputs corresponding to data backup for the one or more computing machines. The user inputs comprise at least one of a tolerable data loss threshold, critical data type, file sensitivity data, and exceptions data time period information. The processor is further configured to create one or more service level agreement (SLA) configurations for data backup based on the user inputs. The processor is further configured to access historical data corresponding to one or more data backup cycles of the one or more computing machines. The historical data comprises one or more of time series changes, the addition and / or change of data information for pre-determined data sets, and corresponding file metadata of the data sets. The processor is further configured to implement one of the active and passive modes of data backup using the user inputs and the historical data. The processor is further configured to trigger a backup job for one or more computing machines based on a comparison between the SLA configurations and dynamic information received about data addition and / or data change of the computing machines in the active mode. The processor is further configured to trigger a backup job for one or more computing machines based on a comparison between the SLA configurations and historical time series change and / or addition of data of the computing machines in the passive mode.

Examples described herein relate to a configurable switch with dynamically configurable device connections to a processor socket, where the device connections are configured to meet service level agreement (SLA) parameters of a first service executing on the processor socket. For a second service that is to execute on the processor socket and the second service is higher priority than the first service, device connections of the switch to the processor socket are dynamically reconfigured to meet SLA parameters of the second service.

Terms of a SLA between a user device of a computing system and a service provider of the computing system are identified. The service provider is to provide a service to the user device in accordance with the terms of the SLA. The terms are translated into a sequence of instructions corresponding to a smart contract, where the smart contract is to provide the user device with a service credit based on an availability of a service for the time interval and in accordance with the SLA. The sequence of instructions is transmitted to a first node of a distributed ledger network. Availability data indicating an availability of the service at the user device during each time period of a time interval is obtained. The availability data is provided to a second node of the distributed ledger network to cause the second node to execute the sequence of instructions.

The present disclosure discloses a redundant backup deployment method for microservice instances in edge computing environments. Redundant backup deployment of the microservice instances and the selection of a primary instance are acquired using Transformer-based deep reinforcement learning (T-DRL). For a service failing to satisfy the service level agreement (SLA), the actual service reliability is improved by downgrading the switching priority of the microservice instances; for a service satisfying the SLA, the deployment cost of the microservice instances is reduced using a pruning algorithm. The present disclosure effectively reduces the deployment cost of microservice instances and minimizes the number of active edge nodes while ensuring the SLA, thereby effectively reducing the resource consumption in edge computing environments.

A hybrid forecasting system (100) for tiered cloud pricing using ensemble learning, comprising: a data ingestion module configured to continuously ingest and aggregate heterogeneous data from a variety of sources, including historical cloud usage data, real-time resource consumption metrics, customer subscription profiles, service-level agreement parameters, and external demand indicators; a preprocessing engine functionally coupled with the data ingestion module, the preprocessing engine being configured to perform data cleansing, normalization, transformation, feature extraction, and dimensionality reduction to generate structured and model-compatible datasets;a model training unit that is functionally coupled with the preprocessing engine, wherein the model training unit comprises a variety of heterogeneous predictive models, including at least one statistical model, at least one machine learning model, and at least one deep learning model, each configured to process the structured datasets independently to generate predictive results that meet future cloud resource needs, workload variability, and price sensitivity across multiple service tiers;an ensemble aggregation layer that is functionally coupled to the model training unit, wherein the ensemble aggregation layer is configured to receive and combine the forecast results generated by the multitude of forecasting models using ensemble learning techniques, including weighted averaging, stacking or boosting, with the weights assigned to each forecasting model being dynamically adjusted based on predefined performance evaluation metrics to generate a uniform and optimized forecast;a price optimization engine that is functionally connected to the ensemble aggregation layer, wherein the price optimization engine is configured to determine and dynamically adjust tiered price structures based on the unified forecast, taking into account parameters such as forecasted demand, user segmentation, demand elasticity, infrastructure capacity constraints, and predefined optimization goals such as revenue maximization and resource utilization efficiency;and a feedback adjustment module that is functionally coupled with the price optimization engine and the model training unit, wherein the feedback adjustment module is configured to monitor system performance in real time, user response to price adjustments and resource utilization results, and iteratively updates model parameters and pricing strategies using adaptive learning mechanisms, including reinforcement learning, wherein the system (100) is configured to operate in a multi-tenant cloud environment, supports real-time data processing and decision-making, and enables automated, scalable, and adaptive optimization of tiered cloud pricing.

One or more techniques and / or systems are provided for dynamically provisioning logical storage pools of storage devices for applications. For example, a logical storage pool, of one or more storage devices, may be constructed based upon a service level agreement for an application (e.g., an acceptable latency, an expected throughput, etc.). Real-time performance statistics of the logical storage pool may be collected and evaluated against the service level agreement to determine whether a storage device does not satisfy the service level agreement. For example, a latency of a storage device within the logical storage pool may increase overtime as log files and / or other data of the application increase. Accordingly, a new logical storage pool may be automatically and dynamically defined and provisioned for the application to replace the logical storage pool. The new logical storage pool may comprise storage devices expected to satisfy the storage level agreement.

Disclosed are automatic application-based multi-path routing for SD-WAN services. This application discloses example network devices, systems, and methods. In one example, a network device includes a memory configured to store information associated with one or more service level agreements (SLAs) for applications in a software-defined wide area network (SD-WAN), and an application-based multi-path routing (AMR) module including processing circuitry. The AMR module is configured to identify one or more applications for AMR based on criteria, where each of the criteria is associated with a corresponding attribute of the applications. The AMR module is configured to determine a violation of one of the SLAs on each WAN link associated with a first application of the identified one or more applications. The AMR module is configured to apply AMR to the first application in response to determining the violation.

The present invention disclosure provides a system for performing root cause analysis, comprising a processor and a memory, the processor is configured to at least one problem associated with running of an application by at least one user, wherein identifying problem includes determining a priority level of one or more services being impacted based on service level agreements, identify at least one root cause associated with the problem, identify one or more root cause corrective actions (RCCA) based on root cause and implement at least one RCCA for troubleshooting the identified problem. Further, the system generates a RCCA form for use by a user group for identifying and validating an additional RCCA and update the database to include the additional RCCA based on a quality score for the identified root cause corrective action. The system is configured to create a RCCA form, auto-fill the RCCA form, identify one or more RCCA implemented for the identified problem, and determine a quality score of the RCCA.

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.

Proliferation of edge devices has significantly advanced technologies in sectors such as autonomous driving and surveillance. However, deploying machine learning models on these resource-constrained devices presents challenges including scalability and managing unpredictable workloads thereby affecting real-time performance in edge-only environments. The present disclosure discloses a method and system for workload deployment in a content guided and service level agreement (SLA) aware edge-cloud architecture. In the present disclosure, a camera feed content-guided load balancing technique is provided that dynamically manages workloads between edge and cloud. Features are extracted from an incoming camera feed to perform load-balancing process efficiently. The load balancer determines a maximum number of concurrent feeds for processing at the edge, with the remaining feeds handled by the cloud based on content of the incoming camera feed. Additionally, a cost model to estimate expenses of deploying the edge-cloud architecture in real-world scenarios with dynamic workloads is provided.

The application provides a micro-service management method, device, equipment and storage medium, comprising: carrying a label of a current state of a target service in a first routing instruction, so that a traffic scheduling component dynamically generates a corresponding routing strategy, and realizes fine traffic scheduling. The mechanism constructs a closed-loop management and control process of "trial operation -> observation -> decision -> state migration -> routing linkage", ensures that services passing through service level agreement verification under real traffic can be officially released, and services that do not meet the standard or are abnormally running are automatically limited in a controlled state or transferred to a rectification process, effectively avoiding the risk of fault diffusion caused by "release as all-network visible". At the same time, it supports dynamic rollback to the rectification state after release, enhances the resilience and recoverability of online services, and significantly improves the safety, verification effectiveness and overall stability of micro-service online.

A method for implementing network slice service including multiple network slices for users of different service categories with different service level agreement (SLA) requirements in an Open Radio Access Network (O-RAN) system having an O-RAN distributed unit (O-DU) and an O-RAN radio unit (O-RU) configured for implementing Demodulation Reference Signal-based Beamforming (DMRS-BF), includes: sending, by the O-DU to the O-RU, a slice priority indicator specific to a user; determining, by the O-RU, a specified service level of the user from the priority indicator; associating, by the O-RU, the specified service level of the user with a corresponding delay profile comprising at least one O-RU latency parameter value; and scheduling, by the O-RU, a receive (Rx) window for control plane (C-plane) messages and a transmit (Tx) window for user plane (U-plane) messages according to the at least one O-RU latency parameter value specified in the delay profile.

Described are techniques for identifying a design of an infrastructure on an industrial floor to service a user. A knowledge corpus of information to be used as a sample data set for training a machine learning model is generated, which includes information, such as mobility patterns of users on the industrial floor for various manufacturing process flows, and interaction of users on the industrial floor with different services at different locations on the industrial floor for various manufacturing process flows. A machine learning model is then trained to identify the designs of the infrastructure on the industrial floor based on the knowledge corpus. Upon receiving the requirements of a service level agreement and the current manufacturing process flow, the trained machine learning model is used to identify a particular design of the infrastructure of cloud and edge computing resources on the industrial floor that optimally services the user.

The invention relates to the technical field of information and communication suitable for supervision, in particular to a block chain-based transformer operation and maintenance data management system, which comprises the following steps of: analyzing a service level protocol into automatically executable commercial contract terms deployed on a chain in a smart contract form through a protocol analyzer; the commercial contract terms are automatically triggered to generate an operation and maintenance work order based on transformer real-time data monitored by an oracle machine; receiving and verifying a completion report, driving a multi-party collaborative acceptance process through commercial contract terms, and finally generating an acceptance voucher containing multi-party digital signatures; generating a verifiable digital certificate for the digital identity of the transformer according to the acceptance certificate, taking the verifiable digital certificate as a non-tampering historical record, and solidifying the verifiable digital certificate in a distributed ledger; according to the invention, high-credibility transformer operation and maintenance management is realized based on the block chain technology, and intelligentization of transformer operation and maintenance management is realized through the chain contract which can be automatically executed.

A method for establishing free-space data transmission channels between a movable network node and a spatially fixed network node includes collecting dynamic position information regarding movable network nodes and static position information relating to spatially fixed nodes, calculating parameters for the nodes from position information, creating a service level agreements list indicating transmission links to be set up between two or more of the nodes with predetermined service performance parameters, creating a prioritization list of the agreements based on the list, checking which of a selection of nodes are available for a directional free-space data transmission channel between the two or more of the nodes with the predetermined service performance parameters defined by the agreement having the highest priority in the prioritization list, and setting up a directional free-space data transmission channel between the two or more of the nodes using the selection.

A communication method and a communication apparatus. A data analysis network element may obtain a correspondence between a plurality of bit rates and a plurality of pieces of service level agreement SLA information; determine, based on a target bit rate of a first service, target SLA information corresponding to the first service; and send the target SLA information to a first network element. The first network element determines a policy for increasing a current bit rate of the first service to the target bit rate. The bit rate of the first service can be quickly increased, thereby improving resource utilization.

A method performed by a radio access network intelligent controller (RIC) in a wireless communication system is provided. The method includes transmitting, to an open radio access network distributed unit (O-DU), a subscription request message including configuration information for reporting whether the service level agreement is satisfied for each terminal or terminal group, receiving, from the O-DU through an E2 interface, an indication message including information on whether the service level agreement is satisfied, and transmitting, to the O-DU, a control message including scheduling information configured based on the indication message.

A system and method include receiving, by a database engine of a database system associated with a virtual computing system, a user request via a dashboard for provisioning a source database with the database system, receiving, by the database engine via the dashboard, selection of a database engine type, and receiving, by the database engine via the dashboard, selection of a Service Level Agreement (“SLA”) and a protection schedule. The system and method also include provisioning, by the database engine, the source database based upon the database engine type, creating, by the database engine, an instance of a database protection system based upon the SLA and the protection schedule, including associating the instance of the database protection system with the source database, and displaying, by the database engine, the source database within the dashboard.

The embodiments herein provide a method and a system for security risk identification and controlling release management of software application. The method provides security risk identification in a software application as well as integrated tools to direct the efforts of developers to build and maintain secure software. The method provides a unique approach of defining a plurality of Service Level Agreement (SLA) to control a release build for a software application by checking vulnerabilities. Furthermore, the method is flexible enough to adapt in most complicated enterprises and different Software Development Life Cycle (SDLC) processes.

Techniques described herein can perform per-queue network performance measurement mapping, in which per-queue network performance measurements are determined and then mapped back to service level agreement (SLA) classes assigned to use the queues. Network traffic associated with an SLA class can be processed through an assigned router queue. Wide area network (WAN) as well as local performance measurements from the assigned queue can be combined to determine a combined performance measurement associated with the assigned queue. The combined performance measurement can then be mapped or otherwise associated with the SLA class. Similarly, combined performance measurements can be determined for other router queues and mapped or otherwise associated with other SLA classes.