Abstract: System and method for recommending static analysis fixes in a computer program include: receiving violations from a static analysis tool and the meta data related to the violations; for each violation, encoding a code snippet in which said each violation is located into a first vector and encoding meta-data associated with each violation into a second vector; for each violation, concatenating the first vector and the second vector into a single vector; generating a utility matrix from the single vectors, where values in each cell correspond to a certain user's action on one of the violations; revising the utility matrix, based on user selection of selected violations of the plurality of violations; and executing a matrix factorization process to build intermediate representations of users and sets of violations to update the utility matrix as an updated utility matrix to obtain predictions for missing values, as recommendation to the user.

Abstract: Techniques reconstruct a storage system. A first extent access load and a second extent access load of a first malfunctioning extent and a second malfunctioning extent in a malfunctioning storage device in the storage system are acquired, respectively. The first malfunctioning extent is selected as a source extent in response to determining that the first extent access load is lower than the second extent access load. Among multiple idle extents in the storage system, a set of destination extents that can be used as a reconstruction destination of the source extent are generated. A destination extent is selected from the set of destination extents as the reconstruction destination of the source extent. It is possible to preferentially select a reconstruction destination for a malfunctioning extent with a low access load, thereby increasing the reconstruction speed of the storage system.

Abstract: Methods, systems, and apparatus, including computer-readable storage media for uncorrectable memory recovery. Different sources of uncorrectable memory error are handled to provide for recovery actions by a host kernel of a machine hosting one or more virtual machines. Rather than defaulting to kernel panic behavior, the host kernel can identify the source of uncorrectable error, and cause the host machine and/or the affected virtual machines to take recovery action that is less disruptive than abrupt shutdown from panic. For example, the host kernel can handle uncorrectable memory error caused by kernel accesses to guest memory of a host virtual machine, as well as uncorrectable memory error improperly raised as a result of known defects in host machine hardware. The host kernel can also be configured to detect sources of overflow in exceptions raised by a processor as a result of uncorrectable memory error.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.

Abstract: Container orchestration platforms, such as Kubernetes, automatically manage the execution of applications in containers on host computing systems. An application may include component applications that execute in different containers and a container orchestration platform may organize the containers for those component applications into a pod for the application. To spread the load for the application, the container orchestration platform may enable creation of more than one pod for a single application. Once created, any of the multiple pods may be selected to handle a request to the application. As such, all of the pods are considered active by the container orchestration platform.

Abstract: The disclosure provides an approach for alarm state restoration. Embodiments include determining a plurality of alarm definitions applicable to an inventory of a plurality of entities in a computing environment. Embodiments include assigning each given alarm definition of the plurality of alarm definitions to a given alarm category of a plurality of alarm categories. Embodiments include restoring declared states of the plurality of alarms definition on the inventory based on the assigning, wherein the restoring comprises, for each given alarm category of the plurality of alarm categories, performing a single traversal of the inventory to identify all respective entities of the plurality of entities that correspond to one or more alarm definitions assigned to the given alarm category.

Abstract: A method and system of cloning a multi-tiered application is disclosed and it comprises of validating received source server configuration data against received target server configuration data. Further the data at a set of nodes on the target server is restored. The cloning of the multi-tiered application is initiated based on a set of predetermined rules, wherein the cloning comprises a set of sequential actions performed at each of the set of nodes. The method of cloning comprises of generating a set of dynamic configuration files for the set of nodes based on the predefined restore rules and the validation and also generating a set of tokens for the set of nodes to communicate status of refresh. Further the target application is restored based on the set of dynamic configuration files and the set of sequential actions at each of the set of nodes is performed based on the status of set of tokens.

Abstract: This disclosure describes techniques for adaptive disaster recovery of applications running on network devices. The techniques include generating an application template and an application template clone that include application attributes usable to deploy an application stack at an application site. The techniques also include sending the application template clone to a disaster recovery site group to await deployment instructions. In some examples, an observer may determine that a health metric of the application site indicates that a disaster recovery process be triggered. A disaster recovery site of the disaster recovery site group may be selected based at least in part on a performance metric. The application stack may be deployed at the disaster recovery site utilizing the application template clone.

Abstract: Methods, systems, and devices for recovery of logical-to-physical (L2P) table information for a memory device are described. A memory system may detect an error in one or more pointers of the L2P table using an error detecting code that is uncorrectable using the code. The memory system may determine a set of candidate codewords for the set of bits, where each of the candidate codewords includes one or more corresponding candidate pointers, and check whether a candidate codeword is correct based on whether a logical address corresponding to a candidate pointer of the candidate codeword matches a logical address stored as metadata for a set of data at a physical address pointed to by the candidate pointer. The memory system may limit the set of candidate codewords or order the candidate codewords for evaluate to reduce a latency associated with identifying a correct candidate codeword.

Abstract: Establishing a diagnostic OS for an information handling system platform performing a UEFI BIOS boot to place the platform in a pre-OS state. Upon detecting a particular POST error and/or a platform configuration policy, an embedded OS kernel may be launched into a DRTM-authenticated measured launch environment (MLE). Additional objects for the diagnostic OS may be downloaded. The additional objects may include an initial ramdisk (initrd) module and one or more applications specific to the particular diagnostic OS. The diagnostic OS may be launched as follows: for each diagnostic OS application, launching the application and extending a measurement of the application into a DRTM PCR. Launching the diagnostic OS may include launching an initrd module and extending a measurement of the initrd module into the DRTM PCR. A measurement of embedded OS kernel may be extended into the TPM and the embedded OS kernel may validate the UEFI BIOS sequence.

Abstract: Exemplary methods, apparatuses, and systems include detecting a failure of a first memory subsystem of a plurality of memory subsystems. A first recovery instruction is sent to a second memory subsystem of the plurality of memory subsystems. The first recovery instruction directs the second memory subsystem to recover a first subset of data stored by the first memory subsystem. A second recovery instruction is sent to a third memory subsystem of the plurality of memory subsystems. The second recovery instruction directs the third memory subsystem to rebuild a second subset of data stored by the first memory subsystem. The first and second subsets of data differ from one another.

Abstract: In general, a device comprising a processor and a memory may be configured to perform various aspects of the techniques described in this disclosure. The processor may conduct, based on configuration parameters, each of a plurality of simulation iterations within the test environment to collect a corresponding plurality of simulation datasets representative of operating states of the network device. The processor may perform a regression analysis with respect to each of the plurality of configuration parameters and each of the plurality of simulation datasets to generate a light weight model representative of the network device that predicts an operating state of the network device. The processor may output the light weight model for use in a computing resource restricted network device to enable prediction of the operating state of the computing resource restricted network device when configured with the configuration parameters. The memory may store the light weight model.

Abstract: Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system uses a control datacenter with a set of service groups used for configuring other datacenters, for example, for performing continuous delivery of software artifacts for other datacenters. The system configures on the cloud platform: (1) a primary control datacenter, (2) a secondary control datacenter, and (3) a tertiary control datacenter. Each control datacenter is configured to act as one or more of: (a) an active control datacenter for configuring cloud infrastructure on tenant datacenters, wherein a tenant datacenter is associated with a tenant of a multi-tenant system, (b) a standby control datacenter for taking over as the active control datacenter, or (c) a recovery control datacenter for fixing components of another control datacenter.

Abstract: Data protection operations including replication operations from a production site to a replica site are disclosed. An example method assessing applications operating on a production virtual machine based on a replication strategy. The replication strategy is configured to identify related applications and ensure that the related applications are replicated to different replica virtual machines. The applications are then replicated from the production virtual machines to the replica virtual machines according to the replication strategy. The replication strategy can improve performance of the recovery operation.

Abstract: A storage system with high availability is provided. The storage system is designed to be provided with: a generation unit that generates information of an association volume which is associated with each volume, on the basis of information of each volume created at each of a plurality of storage devices designated from two or more sites; and an instruction unit that issues an instruction to each storage device, at which each volume is created, to connect each volume associated with the association volume and a compute node which operates a container, on the basis of a request for activation of the container for which the information of the association volume generated by the generation unit is designated.

Abstract: A method for managing a client environment includes obtaining, by a remediation orchestrator, a remediation request associated with a failed application upgrade on an emulation of a client device; in response to the remediation request: obtaining a remediation policy associated with the application upgrade; obtaining application upgrade information associated with the application upgrade; identifying remediation steps to service the remediation request using the application upgrade information and the remediation policy; and initiating performance of the application upgrade and the remediation steps on the client device.

Abstract: A system includes a computer system, memory, and processor. The computer system includes active units of system resources, each executing a workload unit, and redundant units of system resources. The memory stores a reinforcement learning algorithm configured to generate a sequence of resets. Executing each reset includes exchanging the active unit of system resources associated with the reset with a redundant unit of system resources assigned to the active unit of system resources. The processor measures performance metric values, and determines, based on the values, that a first probability that a failure will occur is greater than a threshold. In response, the processor generates and executes a sequence of resets. The processor measures new performance metric values, and determines, based on the new values, a second probability that the failure will occur. The processor then updates the reinforcement learning algorithm based on a difference between the first and second probabilities.

Abstract: A test and measurement system for analyzing a device under test, including a database configured to store test results related to tests performed with one or more prior devices under test, a receiver to receive new test results about a new device under test, a data analyzer configured to analyze the new test results based on the stored test results, and a health score generator configured to generate a health score for the new device under test based on the analysis from the data analyzer.

Abstract: A redundant processing fabric in an autonomous vehicle may include: processing, by a first processing unit of a plurality of processing units, sensor data from a first sensor of a plurality of sensors, where the plurality of processing units are coupled to the plurality of sensors via a switched fabric, wherein the plurality of processing units and plurality of sensors are included in the autonomous vehicle, wherein the sensor data corresponds to an environment external to the autonomous vehicle; determining a failure in processing the sensor data by the first processing unit; and severing, in the switched fabric, a first communications path between the first sensor and the first processing unit; and establishing, in the switched fabric, a second communications path between the first sensor and a redundant processing unit.