Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

Maintaining a synchronous replication relationship between two or more storage systems, including: receiving, by at least one of a plurality of storage systems across which a dataset will be synchronously replicated, timing information for at least one of the plurality of storage systems; and establishing, based on the timing information, a synchronous replication lease describing a period of time during which the synchronous replication relationship is valid, wherein a request to modify the dataset may only be acknowledged after a copy of the dataset has been modified on each of the storage systems.

An insider attack resistant system for providing cloud services integrity checking is disclosed. In particular, the system utilizes an automated integrity checking script and virtual machines to check the integrity of a service. The system may utilize the integrity checking script and virtual machines to execute a set of operations associated with the service so as to check the integrity of the service. When executing the set of operations, the system may only have access to the minimum level of access to peripherals that is required for each operation in the set of operations to be executed. After each operation is executed, the system may log each result for each operation, and analyze each result to determine if a failure exists for any of the operations. If a failure exists, the system may determine that a change in an expected system behavior associated with the service has occurred.

A method of monitoring a closed system, an apparatus thereof and a monitoring device are provided. The method of monitoring the closed system includes: performing a page capturing on a web page of the closed system; searching from a captured page, according to configuration information of data to be monitored of the closed system, a text content corresponding to the data to be monitored; and converting the text content corresponding to the data to be monitored into monitored data which a system monitoring platform is capable of recognizing, and storing the monitored data.

A storage error identification/reduction system includes a storage error identification/reduction subsystem coupled to a storage subsystem including a block. The storage error identification/reduction subsystem receives first data, and writes the first data to first storage locations in the block while writing storage error identification data to second storage location(s) in the block that each are located adjacent at least one of the first storage locations, with the storage error identification data including predetermined values that are written to predetermined locations included in the second storage location(s) in the block. The storage error identification/reduction subsystem then reads the storage error identification data from the second storage location(s) and, based on the predetermined values and predetermined locations of the storage error identification data, identifies errors resulting from the reading of the storage error identification data. Based on the errors, the storage error identification/reduction subsystem determines and performs error reduction operation(s).

Described is a system for detection of network activities using transitive tensor analysis. The system divides a tensor into multiple subtensors, where the tensor represents communications on a communications network of streaming network data. Each subtensor is decomposed, separately and independently, into subtensor mode factors. Using transitive mode factor matching, orderings of the subtensor mode factors are determined. A set of subtensor factor coefficients is determined for the subtensor mode factors, and the subtensor factor coefficients are used to determine the relative weighting of the subtensor mode factors, and activity patterns represented by the subtensor mode factors are detected. Based on the detection, an alert of an anomaly is generated, indicating a in the communications network and a time of occurrence.

Systems and methods for reporting health status for a plurality of computing devices such as within a data center are disclosed. A management device connected to the computing devices via a network executes a management application that periodically requests and receives status data from the computing devices. A pool checker may be used to track corresponding pool status data, and an environment checker may periodically request and receive environmental data from environmental sensors for temperature, humidity, and audio. A report generator creates health reports and assigns device health classifications based on the device status data, the environmental data, and the pool health data. The data may be associated with one or more locations and customers, permitting filtering of the report.

Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

The present disclosure relates to a method of operating a database system. The database system comprises: a database; a first compute node comprising a first database proxy; and a second compute node comprising a second database proxy. The method comprises receiving and processing, at the first database proxy, a first plurality of access requests to access the database; receiving and processing, at the second database proxy, a second plurality of database access requests to access the database; monitoring for a failure event associated with the first database proxy; and, in response to the monitoring indicating a failure event, initiating a failover procedure between the first database proxy and the second database proxy. The failover procedure comprises: redirecting the first plurality of access requests to the second database proxy; and processing, at the second database proxy, the first plurality of access requests.

A system-on-a-chip sharing a graphics processing unit supporting multi-master is provided. A system on chip (SoC) comprises a plurality of central processing units (CPUs) for executing at least one operating system, a graphics processing unit (GPU) that is connected to each of the plurality of CPUs via a bus interface and communicates with each of the plurality of CPUs, and at least one state monitoring device that is selectively connected to at least one CPU among the plurality of CPUs and transmits execution state information of at least one operating system executed in the connected CPU to the GPU. The GPU is shared by at least one operating system and controls a sharing operation by the at least one operating system based on the execution state information of the at least one operating system.