Methods, apparatuses, and computer program products for telemetry-based network switch configuration validation are disclosed. An analytics engine captures a first network snapshot including telemetry data received from one or more network switches in a first state. Upon receiving a notice indicating that a network configuration change has been applied, the analytics engine initiates a timer in response to receiving the notice. The analytics engine captures, in response to expiration of the timer, a second network snapshot including telemetry data received from the one or more network switches in a second state and compares the first network snapshot and the second network snapshot. In dependence upon the comparison of the first network snapshot to the second network snapshot, the analytics engine validates the network configuration change.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

In systems and methods for communicating an anomaly notification message to a wireless communication network, a wireless device may generate an anomaly notification message comprising an anomaly notification object in response to determining that the received information satisfies one or more threshold criteria indicative of the anomaly condition, configure the anomaly notification message with a transport layer anomaly code, and send sending the configured anomaly notification message via an anomaly-specific network communication link to a wireless communication network. A communication network device may receive the anomaly notification message, and in response to determining that the anomaly notification message was received via the anomaly-specific network communication link may associate the anomaly notification message with an anomaly priority that is higher than a normal traffic priority.

In systems and methods for communicating an anomaly notification message to a wireless communication network, a wireless device may generate an anomaly notification message comprising an anomaly notification object in response to determining that the received information satisfies one or more threshold criteria indicative of the anomaly condition, configure the anomaly notification message with a transport layer anomaly code, and send sending the configured anomaly notification message via an anomaly-specific network communication link to a wireless communication network. A communication network device may receive the anomaly notification message, and in response to determining that the anomaly notification message was received via the anomaly-specific network communication link may associate the anomaly notification message with an anomaly priority that is higher than a normal traffic priority.

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

The described technology is generally directed towards methods for data latency evaluation. The techniques disclosed herein can provide useful information about when a data consumer can expect to receive data. Methods can create and compare data latency cumulative probability distributions comprising probabilities associated with different latency values, at various different levels of completeness.

A cloud network is a complex environment in which hundreds and thousands of users or entities can each host, create, modify, and develop multiple virtual machines. Each virtual machine can have complex behavior unknown to the provider or maintainer of the cloud. Technologies disclosed include methods, systems, and apparatuses to monitor the complex environment to detect network anomalies using machine learning techniques. In addition, techniques to modify and adapt to user feedback are provided allowing the developed models to be tuned for specific use cases, virtual machine types, and users.

Provided is a setting device and the like with which correct estimation of a communication band is possible. The setting device 101 has a transmission unit 102 that, on the basis of a first timing at which a first information processing device 401 transmits to a second information processing device 402 a first signal for measuring a communication band which pertains to a communication network 403, transmits to the second information processing device 402 a setting signal for setting a communication unit 407 of the second information processing device 402 to a communication-enabled state.