Dell Products Inc.

Emc IP Holding Company LLC

Emc Corporation

Open Text Corporation

Boris Shpilyuck

Igor Dubrovsky

Assaf Natanzon

David Zlotnick

Nisan Haimov

Dmitry Volchegursky

Jehuda Shemer

Dmitry Limonov

Alex Rankov

Maxim Balin

Shu-Shang Sam Wei

Dmitry Yuryevich Korshunov

Ofir Ezrielev

Linda J Wong

Yossi Matatov

Saar Cohen

Stav Sapir

Tomer Kushnir

Pavel Balan

Methods and systems for managing operation of a deployment are disclosed. The deployment may be managed by employing redundant devices within the deployment. The redundant devices may serve to replace operation of failed devices. Despite replacement of operations of a failed device, the redundant device may not have similar functionality as the failed device. To make the functionality similar to the failed device, a correction factor may be applied to the redundant device. In applying the correction factor, the output from the redundant device may be expected to match output from the failed device. In matching output from the failed device, the redundant device may operate in place of the failed device.

Employing redundant devices in a deployment

Methods and systems for prioritizing resources are disclosed. The resources in a deployment may be prioritized by training machine learning models for use in repairing impaired devices with failures of operation. The trained machine learning models may learn differences in operation. Differences in operation may pertain to output between each device of the set of devices and the digital twin in the deployment. The differences in operation may be used to prioritize a list of impaired devices that may be repaired. The prioritized list of impaired devices may be followed when available resources may be expended to repair the impaired devices.

Prioritizing resources for addressing impaired devices

Methods and systems for managing operation of a deployment are disclosed. The operation of the deployment may be managed by simulating devices with a digital twin within the deployment. The digital twin may serve to replace operation of failed devices. By replacing failed devices with the digital twin, cooperative processes requiring participation of the failed devices may be continued while the failed devices are unable to participate.

Simulating devices with a digital twin in a deployment

Systems and methods for risk assessment of user accesses to data resources are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, where the memory includes program instructions store thereon that, upon execution by the processor, cause the IHS to: obtain a plurality of resource risk weights of a respective plurality of resources, and a plurality of access permissions of a user for the respective plurality of resources; and generate based, at least in part, on the plurality of resource risk weights and the plurality of access permissions of the user, a risk score for the user that represents a level of security impact of the user on the plurality of resources.

Systems and methods for risk assessment of user accesses to data resources

A system can determine that there is a failed configuration in a microservices architecture, wherein the microservices architecture comprises a group of microservices. The system can order the microservices of the group to produce an ordered group of microservices, wherein the ordering first comprises failed microservices of the group of microservices, then dependent microservices of the group of microservices, and wherein respective failed microservices of the failed microservices depend on respective dependent microservices of the dependent microservices. The system can perform iterations of, splitting the ordered group of microservices into two subgroups of microservices, and reverting changes to one of the two subgroups of microservices. The system can determine that a microservice of the group of microservices has the failed configuration based on a result of the performing of the iterations.

Detecting configuration issues during pre-canary deployment

Methods and systems for managing inference models are disclosed. An inference model may be divided into any number of sub-network units and each sub-network unit of the sub-network units may be located in a different geographical location. Data privacy regulations may apply to the geographical locations. Therefore, a first sub-network unit may be subject to a first data privacy regulation and a third sub-network unit may be subject to a second data privacy regulation. If an output of a sub-network unit is not usable by a downstream sub-network unit due to differences in data privacy regulations, the first sub-network unit may be replaced with a second sub-network unit, the second sub-network unit generating an output that does meet the data privacy regulations of the downstream sub-network unit. Doing so may reduce interruptions to inference generation by the inference model.

Managing inference models to meet data privacy regulations

The technology described herein is directed towards combining multiple sidecar (e.g., Envoy-based) proxies into a single sidecar or reduced number of sidecars for use in association with a service. Described is identifying sidecars for merging, grouping by version compatibility, and determining their functions and configuration data. Any conflicts in the configuration data are resolved. A merged sidecar is built by combining functional code and configuration data. The merged sidecar is deployed along with its relevant service, e.g., deployed as a container in a Kubernetes environment. The merging facilitates reduction of resource utilization by having only a merged sidecar, instead of multiple sidecars, support a service.

Cloud proxy-based sidecar coalescence

Methods and systems for managing the operation of data processing systems are disclosed. To manage the operation of the data processing systems, access control standards for software may be enforced during development and/or deployment of software. The access control standards may indicate the extent of access controls for data that are to be in place for various pieces of software. The access control standards may also indicate a level of consistency in the sources of truth for permissions enforced by the access controls.

Access control management for systems

Methods and systems for managing operation of a distributed system comprising a data center and edge devices. The operation of the distributed system may be managed by monitoring the edge devices. The edge devices may be monitored by identifying the health state of an edge device. The health state of the edge device may be identified by collecting data from operation of similar edge devices and the edge device and comparing the differences in between the data. If the differences between the data from the operation of the similar edge devices and operation of the edge device may exceed criteria for deviation, then the edge device may be determined to be in an unhealthy health state.

Identifying the health state of edge devices

Methods and systems for managing a backup system are disclosed. Edge infrastructures may be made up of large numbers of edge devices that produce and store a wide variety of data. A data backup system is implemented to locate and monitor locations of data stored in these edge infrastructures such that users wanting to backup data from these edge infrastructures are not required to know an exact location and/or an exact name of the data to be backed up. In particular, the data backup system could backup data using only natural language descriptions of the data that is provided by the user.

System and method for backing up data from edge infrastructures

Methods and systems for managing trust in distributed are disclosed. To manage trust, a behavior and characteristic based trust model may be used. The trust model may utilize similarity between devices and public activity of devices over time to ascertain levels of trust that should be afforded devices of the distributed system. The levels of trust may be used to ascertain whether requests from devices of the distributed systems should be honored, or rejected. The trust models may facilitate establishment of trust in environments where physical intrusion based threats are present.

Trust management in distributed systems

One example method includes processing a file of a VM to create a decreased quality file that is a version of the file, storing the decreased quality file at a recovery site along with metadata indicating a file type and path name for the decreased quality file, in response to a disaster recovery request, creating a partial user VM at the recovery site, and the partial user VM includes the decreased quality file, and with the partial user VM, serving the decreased quality file to a user in response to a request from the user.

Fast disaster recovery in the cloud for virtual machines with significant media content

A system can determine complexity data representative of a complexity of changes to computer code that is executable to operate at least one updated microservice that is part of a group of microservices, wherein at least one current microservice is deployed, and wherein the at least one updated microservice corresponds to an update of the at least one current microservice. The system can determine a rate at which invocations of the at least one current microservice are made. The system can determine a threshold number of calls to be processed to proceed from a first stage of a progressive deployment plan to a second stage of the progressive deployment plan based on the complexity data and the rate. The system can progressively direct traffic to the at least one updated microservice based on the progressive deployment plan.

Automatic canary deployments with dynamically updating call thresholds

Methods and systems for device shutdown in a deployment are disclosed. Device shutdown may be considered to conserve energy and simplify processes in a deployment. To conserve energy and simplify processes, all devices within a deployment may undergo a redundancy analysis and qualification analysis. The redundancy analysis may produce lists of redundant and non-redundant devices. All redundant devices may be candidates for device shutdown. Next, qualification analysis may qualify devices for shutdown by energy consumption and output data accuracy and uncertainty qualification. Devices that may not meet prescribed qualifiers may also be candidates for shutdown. With all devices that may be candidates for shutdown assembled in a list, device shutdown may commence in the deployment.

Device shut down in a deployment

A system can determine respective health statuses for respective microservices of respective instances of a group of microservices. The system can monitor the requests to determine a correlation between respective requests of the requests and respective subgroups of microservices of the group of microservices that carry out the respective requests. The system can determine a subgroup of container clusters of container clusters that are available to serve a first request type, based on determining an intersection between the respective subgroups of microservices of the group of microservices that carry out the respective requests, and the respective health statuses for respective microservices of respective instances of the group of microservices. The system can, in response to receiving a first request of the first request type, assign, by a load balancer, the first request to be served by a first container cluster of the subgroup of container clusters.

Growing community of inventors

Ashdod, Israel

Boris Shpilyuck