Abstract: A Hyper Text Transfer Protocol (HTTP)-Uniform Resource Identifier (URI) error-recovery method is provided. The HTTP-URI error-recovery method is applied in user equipment (UE). The HTTP-URI error-recovery method includes the following steps. The UE transmits a first HTTP request with a first URI encoding scheme to an application server through an interface. Then, the UE receives a specific error response corresponding to the first HTTP request from the application server. Then, the UE transmits a second HTTP request with a second encoding scheme to the application server based on the specific error response.

Abstract: In general, one or more embodiments of the invention relates to systems and methods for performing a backup and later determining a level or percentage of corruption of the resulting backup set. By having a cyber-security module analyze the backup data periodically, corruption of backup data both caused by cyber-attacks or by hardware failures may be detected and characterized. By knowing how corrupted a particular corrupted backup data set is, an informed decision may be made with regards to purging the backup data set and/or using the backup data set or portion thereof in any further restorations. By making these determinations, a quick identification of possible ransomware attacks may be made, and additional degradation of a user's data may be avoided.

Abstract: Disclosed herein is a server management apparatus, comprising: a server information acquisition unit configured to acquire, from a plurality of servers respectively, server information on a server and hardware component information on hardware components included in the server, using an identifier of a control board of the server as a key; an analysis unit configured to analyze the server information and the hardware component information acquired by the server information acquisition unit, and generate a server status flag indicating whether or not any of the server and the hardware components has an abnormality; and a structured file generation unit configured to generate a structured file that hierarchically stores, in a human-readable format, the server information and the hardware component information acquired by the server information acquisition unit, the structured file including a header describing the server status flag generated by the analysis unit.

Abstract: Techniques for generating action recommendations for a data tape system are disclosed. A data tape system generates action recommendations for a data tape based on library-based metadata messages as well as a measured data quality value of the data tape. The system initiates an operation resulting in the data tape interacting with a media drive. A data tape library controller generates one or more metadata messages based on a result of a requested operation. The metadata message may include information regarding the type of error and a default recommended course of action. The system generates the recommended action for the data tape using a trained machine learning model.

Abstract: Techniques for determining workstation devices and resource parameters for software tests are discussed herein. The workstation devices may include graphic processing units (GPUs) on which portions of the software tests are performed. The workstation devices and GPUs in the workstation devices may be allocated and designated for building and testing software programs. A task to be performed on a GPU cluster of a plurality of GPU clusters may be determined. The task may be added to a scheduling queue associated with the plurality of GPU clusters. The task may be assigned to an individual GPU, based at least in part on a memory constraint associated with the task. The task may be performed with the individual GPU of the GPU cluster to determine a result. The result may be returned to a computing device.

Abstract: An embodiment may involve, in response to determining that a software service satisfies a degradation criterion, generating an alert that indicates the software service and a change request specifying that configuration data used by the software service was changed in a current snapshot of the configuration data; based on the change request, determining a set of configuration changes between the current snapshot and a previous snapshot of the configuration data; and providing a representation of the set of configuration changes indicating one or more parameters that were changed in the current snapshot and one or more paths in a tree-based arrangement of the configuration data that lead to the one or more parameters that were changed in the current snapshot.

Abstract: Systems and methods involving provision of machine-learning-based prediction of future failure, anomaly, etc. in execution of batch processes are disclosed. In one illustrative implementation, an exemplary method may comprise obtaining historical data from prior execution of one or more batch processes, training a machine learning model to predict one or more future failure(s) and/or future flag(s) in execution of a future batch process, generating and/or collecting descriptive analytics pertinent to execution of the batch processes, and predicting a future failure and/or future flag in execution of the batch processes using the trained machine learning model and/or the descriptive analytics.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a service stack recovery engine, an error message indicating a failed service in a plurality of services executed in a cloud platform, determining, by the service stack recovery engine, a set of services for recovery in response to the error message, and, for each service in the set of services, identifying, by the service stack recovery engine, an error handler that is associated with a service recovery type in a set of service recovery types, the error handler including a set of error handling operations to be executed to recover the failed service, and transmitting, by the service stack recovery engine, the error handler to a service recovery agent associated with the respective service, the service recovery agent executing error handling operations of the error handler to recover the service.

Abstract: An apparatus comprises a first processing device configured to manage, in a first mode of operation, one or more first tasks for a network switch, and to replicate, in the first mode of operation, state information for a network operating system of the network switch, the network operating system running on a second processing device in the first mode of operation. The first processing device is also configured to initiate, in response to one or more trigger conditions, an in-service switch-over from the first mode of operation to a second mode of operation. The first processing device is further configured to suspend, in the second mode of operation, at least one of the one or more first tasks, and to run, in the second mode of operation, at least a portion of the network operating system utilizing the replicated state information.

Abstract: A streamlined approach analyzes block-level backups of VM virtual disks and creates both coarse and fine indexes of backed up VM data files in the block-level backups. The indexes (collectively the “content index”) enable granular searching by filename, by file attributes (metadata), and/or by file contents, and further enable granular live browsing of backed up VM files. Thus, by using the illustrative data storage management system, ordinary block-level backups of virtual disks are “opened to view” through indexing. Any block-level copies can be indexed according to the illustrative embodiments, including file system block-level copies. The indexing occurs offline in an illustrative data storage management system, after VM virtual disks are backed up into block-level backup copies, and therefore the indexing does not cut into the source VM's performance. The disclosed approach is widely applicable to VMs executing in cloud computing environments and/or in non-cloud data centers.

Abstract: Writers on a host and associated with a copy service are identified. The identification includes names of the writers and writer types as being system or application writers. For each writer identified as a system writer, a size of the system writer is calculated and a check is made as to whether a volume associated with the system writer includes system data. When the volume include system data, the volume is marked as a critical volume and a size of the critical volume is obtained. A tally is maintained summing the sizes of the system writer and critical volume.

Abstract: According to one embodiment, a memory system is connectable to a host. The memory system includes a nonvolatile memory and a controller. The nonvolatile memory includes a plurality of blocks. The controller is electrically coupled to the nonvolatile memory. The controller controls the nonvolatile memory. When receiving, from the host, a first command for changing a state of an allocated block to a reallocatable state in a case where a second command that is yet to be executed or being executed involving read of data from the allocated block has been received from the host, the controller changes the state of the allocated block to the reallocatable state after the second command is finished.

Abstract: A method is disclosed and includes receiving, using a first client computing system, an instance from an administrative computing system. The instance includes a partition of computing resources of the administrative computing system. The partition of computing resources includes a virtual processor and first machine-readable instructions. The first machine-readable instructions include a first predefined software bug. The method includes executing, using the virtual processor, the first machine-readable instructions. Executing the first machine-readable instructions includes receiving, using the virtual processor, a first input indicating an identification of the first predefined software bug of the first machine-readable instructions.

Abstract: A method in an illustrative embodiment includes determining, by a first deep learning model on a storage device, whether a fault is caused by an environmental factor. The method further comprises: determining, by a second deep learning model on the storage device in response to determining that the fault is caused by the environmental factor, whether the fault can be solved locally in the storage device. The method further comprises: processing the fault according to a knowledge base in response to determining that the fault can be solved locally in the storage device. The method further comprises: sending the fault to a third deep learning model on a cloud device in response to determining that the fault cannot be solved locally in the storage device, the first deep learning model and the second deep learning model being obtained by model distillation of the third deep learning model.

Abstract: A replay tool configured in a learning mode is used to replay a recorded interaction workflow to obtain respective learning-mode test data responsive to a request from a client device to a server. A baseline response template is obtained from the respective learning-mode test data. A baseline response time of the request is also obtained from the respective learning-mode test data. The recorded interaction workflow is replayed in a testing mode to obtain testing-mode test data. Responsive to determining that a response body included in the testing-mode test data is inconsistent with the baseline response template, a first anomaly message is output. Responsive to determining that the response time included in the testing-mode test data is not within a tolerance of the baseline response time, a second anomaly message is output.

Abstract: Embodiments of this application provide a method and a device for displaying a representation indicator of an object. In the method, values of representation indicators of a plurality of monitored objects at a plurality of sampling time points are determined; and image information used to display changes of the representation indicators is generated, where the image information includes the plurality of sampling time points, values of representation indicators of a plurality of objects that meet a sorting requirement at the plurality of sampling time points, and association information, and the association information is used to associate values of representation indicators that are at a same sorting position at the plurality of sampling time points, to reflect changes of the values of the representation indicators that are at the same sorting position.

Abstract: The disclosed technology is generally directed to the processing of parallel computing jobs. In one example of the technology, for at least a first cluster of virtual machines that is assigned to a job, an initial assignment of at least one virtual machine to a parallel-computing job is made. Workers are assigned to tasks associated with the job. Upon failure of a task by one of assigned workers, the failed task is re-submitted. Upon detecting the failure of one of the workers assigned to the job, the failed worker is replaced with a replacement worker. Work associated with the failed worker is re-allocated to the replacement worker. Responsive to removal of a virtual machine assigned to the job, a new virtual machine is assigned to the job. Outputs are provided from the assigned workers.

Abstract: Techniques are described for error log anomaly detection. In an implementation, error logs from an application are processed to generate training data. The error logs, for instance, are processed to remove personal information and other data such as numerical strings. The processed error logs are converted into embeddings to generate the training data. The training data is utilized to train an anomaly detection model. For instance, as part of training the anomaly detection model, an anomaly threshold is defined based on a loss value determined from output of the anomaly detection model. Further error logs from the application are then processed by the trained anomaly detection model to determine which of the further error logs are error anomalies, such as based on comparing loss values for the further error logs to the anomaly threshold.

Abstract: Examples described herein are generally directed to backing up and restoring of container clusters. According to an example, the conventional tight coupling between namespaces and tenants is eliminated by providing within a container platform a tenant abstraction for each tenant of the container platform that shares resources of a container cluster. The tenant abstraction for a given tenant includes information indicative of a subset of namespaces of the container cluster assigned to the given tenant. Responsive to receipt via a user interface of the container platform of a request to backup a particular tenant of the container platform: the container platform determines the subset of namespaces assigned to the particular tenant; and for each namespace, causes a namespace-level backup/recovery tool to backup object data for the namespace within a backup location, and backup data associated with persistent volume claims for each unit of cluster replication within the namespace.

Abstract: Disclosed herein a method for configuration of semi-managed DHT based on NDN and system therefor. The method includes: managing and sharing manager nodes based on global information; generating a sub-range segment by partitioning a keyspace area, according to each group of the data peer nodes; managing the sub-range segment by distributing the sub-range segment corresponding to each group to data peer nodes of the each group; checking and analyzing liveness of the data peer node in the each group; estimating availability of the data peer nodes by predicting lifetime of data peer nodes through the checking and analyzing of liveliness; performing risk factor prediction that estimates data availability and sub-range management overhead; and performing a sub-range management decision at each of the manager nodes based on a result of the risk factor prediction, and performing a global stabilization decision required for global system stabilization.