Disclosed herein are systems and methods for pattern-based QoS violation prediction. In one exemplary aspect, a method may comprise identifying a service on a computing device that is connected to a plurality of client devices and determining a plurality of micro-services comprised in the identified service. The method may comprise parsing access information to detect that a first client device is accessing a micro-service. The method may comprise determining, for a first period of time, QoS evaluation parameters for the access between the micro-service and the first client device. The method may comprise identifying changes in the QoS evaluation parameters within the first period of time, detecting a predetermined QoS violation pattern, and executing a QoS action based on the predetermined QoS violation pattern.

A communication method and a system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT) is provided. The disclosure is applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as a smart home, a smart building, a smart city, a smart car, a connected car, health care, digital education, a smart retail, security and safety services. A method performed by a first entity performing a network data analytics function (NWDAF) is provided. The method includes receiving, from a second entity performing network function (NF), a first message for requesting observed service experience analytics, the first message including single-network slice selection assistance information (S-NSSAI) indicating a network slice, transmitting, to a third entity performing application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the observed service experience analytics, the second message including information on at least one application, receiving, from the third entity, the service data including at least one service experience for the at least one application, identifying the observed service experience analytics based on the service data, and transmitting, to the second entity, the observed service experience analytics.

A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Techniques for generating and monitoring service level objectives (SLOs) are disclosed. The techniques include an SLO platform performing: storing a first SLO definition of a first SLO including a first error budget for a first metric associated with a first service; storing a second SLO definition of a second SLO including a second error budget for a second metric associated with a second service; obtaining first telemetry data from a first data source associated with the first service; obtaining second telemetry data from a second data source associated with the second service; monitoring the first SLO at least by computing the first metric based on the first telemetry data and evaluating the first metric against the first error budget; and monitoring the second SLO at least by computing the second metric based on the second telemetry data and evaluating the second metric against the second error budget.

In one embodiment, a method includes identifying a mobile service provider network (SPN) and a geographic location of an online social network user and accessing a service-provider table associated with the identified mobile SPN and with the geographic location. The service-provider table indexes a reliability score and a sampling amount for multiple messaging-service providers in the geographic location. The method further determines, based on the service-provider table, whether any of the messaging-service providers has a sampling amount below a threshold sampling amount and sends messaging traffic via the determined messaging-service provider until the sampling amount is greater than or equal to the threshold sampling amount. The messaging traffic is used to update the reliability score for the messaging-service provider. The method further includes selecting a messaging-service provider based on the updated reliability scores of the messaging-service providers and sending a message to the user via the selected messaging service-provider.

Methods recommend to data center customers those attributes of a data center infrastructure and application program that are associated with service-level objective (“SLO”) metric degradation and may be recorded in problem definitions. In other words, a data center customer is offered to “codify” problems primarily with atomic abnormality conditions on indicated attributes that decrease the SLO by some degree that the data center customer would like to be aware. As a result, the data center customer is warned of potentially significant SLO decline in order to prevent unwanted loss and take any necessary actions to prevent active anomalies. Methods also generate patterns of attributes that constitute core structures highly associated with degradation of the SLO metric.

Semi-automatic failover includes automatic failover by a service provider as well as self-serviced failover by a service consumer. A signal can be afforded by a service provider based on analysis of an incident that affects the service provider. Initiation of self-serviced failover by a service consumer can be predicated on the signal. In one instance, the signal provides information that aids a decision of whether or not to failover. In another instance, the signal can grant or deny permission to perform a self-serviced failover.

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: selecting a service subscription to manage; monitoring asset resource utilization of the service subscription; generating an adaptive service subscription schedule recommendation; and, managing the service subscription based upon the adaptive service schedule recommendation.

Provided herein are systems and methods for providing insights or metrics in connection with provisioning applications and/or desktop sessions to end-users. Network devices (e.g., appliances, intermediary devices, gateways, proxy devices or middle-boxes) can gather insights such as network-level statistics. Additional insights (e.g., metadata and metrics) associated with virtual applications and virtual desktops can be gathered to provide administrators with comprehensive end-to-end real-time and/or historical reports of performance and end-user experience (UX) insights. Insights relating to an application or desktop session can be used to determine and/or improve the overall health of the infrastructure of the session, Citrix Virtual Apps and Desktops, the applications (e.g., remote desktop application) being delivered using the infrastructure, and/or the corresponding user experience.

A system used for identifying issues within a telecom network. Data is obtained from sources including probes and network elements. KPIs are identified for real-time streaming aggregation. Streaming data related to the KPIs is aggregated and an approximation of count-distinct subscribers and volume count is calculated, as well as count-distinct subscribers aggregating by each identified KPI. Drill objects found in the aggregated data are identified based on the calculations and real-time trending records are generated and stored for each drill object using an exponential moving average. Baseline averages are generated based on the real-time trending records. An increase in errors can then be detected based on the baseline averages and additionally aggregated real-time streaming data. Deviations in each drill object contributing to the detected increase in errors are then analyzed and a full case report is generated based on details of the deviations.