Dell Products Inc.

Emc IP Holding Company LLC

Vinay Sawal

Vivek Dharmadhikari

Sithiqu Shahul Hameed

Sachinrao Chittaranjan Panemangalore

Joseph LaSalle White

Udhaya Chandran Shanmugam

Viswanath Ponnuru

Jayanth Kumar Reddy Perneti

Kuntal Atulbhai Patel

Sanjay Sane

Swaminathan Sundararaman

Gavin Richard Cato

Marimuthu Sakthivel

Sumanth Vidyadhara

Muzhar S Khokhar

Anne-Marie McReynolds

Ramya Ramachandran

Kannan Karuppiah

Senthil Kumar Ganesan

Tsehsin Jason Liu

James Lawrence Mangin

Judith A Furlong

Vindhya Gangaraju

Ashok Kumar Janardhana

Mahendaran Pagalpatti Vijayakumar

Rajini Ramachandran Karthik

Deepak Krishna

Alan White

Shubhang Chaudhary

Russell K Mukai

Hariharan Krishnaswamy

James Laurance Mangin

Jayanth K Perneti

Amitha Shetty

Ratnesh Yadav

Sudhir Vittal Shetty

Ching-Yun Chao

Krishnamurthy Subramanian

Shree N Murthy

Erik P Smith

Amihai Savir

Jason Matthew Young

Per Henrik Fremrot

Deepak Gowda

Yi Fang

Christopher Atkinson

Mukesh Moopath Velayudhan

Praveen S Lalgoudar

Scott M Geng

Anil Venkatesh Varkhedi

Vinoth Kumar Arumugam

Bryan D Neumann

Timothy Thinh Mai

Abhishek Jain

Jason Liu

Matthew R Cullen

Jason David Shaw

Collin LeGault

Bruce Amberden

Gurudath Harikrishna Shenai

Saujanya Golwelkar

Anil Chintalapati

Newton Oku

Aditya Narravula

Geetha Sekar

Brian Gerard Pierce

Bernard Sarpong Duffour

Eric Edward Peter Seifert

Srividhya Nadhadhur Murali

Ravi Shankar Sabapathy

Kevin McClintock

Igor Pedan

Brian Rochford

Alice Jiang

Andrea Roggerone

Mauro Monteiro Silva

Jordan Leventis

One example method includes receiving, from a prospective tenant, a request to join a tenant cluster of a cloud computing environment, sending a request to verify an authenticity of the prospective tenant, a tenant membership policy applicable to the prospective tenant, and a trusted execution environment (TEE) remote attestation, checking the authenticity and TEE remote attestation against the tenant membership policy, and when the authenticity and TEE remote attestation against the tenant membership policy are verified as indicative of a compliance of the prospective tenant, adding the prospective tenant to the tenant cluster.

Multi-tenant tee attested resource provisioning

Methods and systems for managing customer-encountered issues are disclosed. To manage the customer-encountered issues, predictions regarding the causes of customer-encountered issues may be obtained and used to guide remediation processes. The predictions may be obtained using inference models that utilize correlations to filter potential causes of the issues. By filtering the causes, the resulting predictions may be more likely to be accurate thereby improving the rate at which customer-encountered issues may be resolved.

Correlation enhanced causation identification for data processing system management

Methods and systems for gracefully rerouting connections between client devices and server devices are disclosed. The method may include suspending a logical connection between a client device and a service provided by a server device set and placing the logical connection in a predetermined state. While the logical connection is in the predetermined state, modifying a transmission path supporting the logical connection to remove a first aggregator that was previously in the transmission path and add a second aggregator to a second server device set to the transmission path. Using the modified transmission path, the logical connection may be resumed to enable the client device to resume use of the service provided by the second server device set.

Rerouting of client server connections

One example method includes receiving, by a distributed attestation proxy server (DAPS) from a client, an attestation request, establishing, by the DAPS, a secure communication channel with the client, performing, by the DAPS, a resource discovery process to identify a resource that meets a requirement of the client, facilitating, by the DAPS, an attestation exchange between the client and the resource, performing, by the DAPS, a verification and attestation process that comprises generation of an attestation result, transmitting, by the DAPS, the attestation result to the client, and when the attestation result is positive, establishing, by the DAPS, a secure communication channel between the client and the resource.

Proxy attestation service for multi-cloud tee hardware

A system can receive an untrusted onboard announcement message from a remote computer, wherein the untrusted onboard announcement message comprises first data that identifies the remote computer and second data that indicates a current configuration of the remote computer. The system can identify a stored indication of an authorized configuration of the remote computer based on the data that identifies the remote computer. The system can determine that there is a mismatch between the authorized configuration of the remote computer and the current configuration of the remote computer. The system can determine a trust metrics evaluation score for the remote computer based on a type of hardware component change between the authorized configuration of the remote computer and the current configuration of the remote computer. The system can, in response to determining that the trust metrics evaluation score is greater than a threshold value, onboard the remote computer.

Trusted cloud device lifecycle management

Architectures and techniques are described that can generate or receive a dependency graph of a microservices platform. The dependency graph can be constructed based on run time operation of microservices such that the nodes of the graph can represent microservices and the edges can represent the interactions during run time. The dependency graph along with an anomaly pattern can be embedded into an embedding space, and based on an examination of the embedding space, it can be determined whether the anomaly pattern exists in the dependency graph.

Anomaly detection for a microservices platform

A system can receive an untrusted onboard announcement message from a remote computer. The system can, based on the untrusted onboard announcement message, initiate an onboarding service policy to verify a device location claims policy associated with the remote computer. The system can receive an indication of a remote access controller hardware component of the remote computer, wherein the indication is of verifying a network port authentication policy applicable to determine whether a port-based network access control protocol certificate hostname that is associated with the remote computer matches an entity attestation token attribute extensible authentication protocol-transport layer platform certificate hostname of the remote access controller hardware component to determine a network authentication status. The system can determine whether device onboard location is successful based on a device location verification policy status and the indication.

Location aware trusted cloud resource provisioning

Methods and systems for managing operation of data processing systems are disclosed. A management system may obtain resource data based on operation of the data processing systems while using resources managed by the management system. The resource data may be semantically enhanced based on ontology definitions and represented as a graph structure. The graph structure may be used by a graph inference model (e.g., a graph neural network) to obtain predicted tags for the resources. The graph inference model may be based on a plurality of graph structures that may provide information regarding generalized relationships between at least the resources. The predicted tags may be used by the management system to update operation of the data processing systems.

Managing resources using a graph inference model to infer tags

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset data, the data center asset data comprising historical data center asset telemetry data; training a prediction model to generate predictions using the historical data center asset telemetry data; and, interpolating additional data center asset telemetry data using the predications generated using the prediction model.

Data center monitoring and management operation including data center asset telemetry interpolation operation

A system can receive, from a remote computer of remote computers, inventory data representative of an inventory, wherein the inventory data indicates that the remote computer is configured to implement a shared memory pool with the remote computers according to a compute express link protocol. The system can receive, from the remote computer, a request to onboard the remote computer, wherein the request adheres to a defined security protocol and data model architecture, and wherein the system is configured to remotely manage the remote computers as part of a hybrid cloud platform that comprises the remote computers. The system can authenticate the remote computer based on the request and according to the defined security protocol and data model architecture. The system can implement the shared memory pool for the remote computers.

Memory pool management using a cloud platform

Embodiments for handling configuration drift in a data storage system having a plurality of storage nodes. A configuration drift manager system defines a golden configuration dataset for the data storage system, obtains a current configuration dataset of each storage node of the plurality of storage nodes, each of the golden and configuration datasets comprising a plurality of sentences defining a node configuration parameter; determines a distance between each sentence of the golden configuration dataset with each other sentence of the current configuration datasets for each of the plurality of storage nodes; ranks each node based on a distance of its sentences with the golden configuration dataset, and triggers an action on a corresponding node based on its respective ranking.

Handling configuration drift in backup storage systems

Health data relating to health of field replaceable units (FRUs) can be collected, determined, and managed. FRU can comprise a data health management component (DHMC) that can periodically or dynamically analyze sensor data relating to operational characteristics of the FRU received from sensors associated with components of or associated with the FRU, historical sensor data, or historical FRU health data, wherein the sensor data can indicate usage or accelerated usage of the components of the FRU. Based on such analysis, DHMC can determine or update an expected amount of time of remaining usable life of the FRU, which can take into account any accelerated usage of the FRU. DHMC can store, in a data store of the FRU, or present, via an interface of the FRU, information relating to the expected amount of time of remaining usable life of the FRU or other FRU health data.

Enhanced management of device health data

Presented herein are embodiments that use a language model to embed or encode configuration elements (e.g., commands, prompts, etc.) into dense, latent representations that incorporate semantic and contextual information. Using a trained language model, a configuration for a network device may be converted into a set of configuration path sentences. Given a first set of encoded configuration path sentences for a first configuration and a second set of encoded configuration path sentences for a second configuration, these two sets may be compared to gauge a degree of difference between the two sets. In one or more embodiments, an Optimal Transport method with Wasserstein distance metric may be used to obtain a comparison value that gauges difference between the two configurations. In one or more embodiments, the comparison valuation may be labeled or classified by comparing the comparison value to one or more pre-defined thresholds.

Systems and methods for gauging differences between network configurations

Methods and systems for managing undesired operation of managed systems are disclosed. To manage the undesired operation, information regarding the undesired operation may be obtained. A multistage learning process may be performed to identify likely root causes for the undesired operation using the obtained information. Remediation processes based on the likely root causes may be performed to reduce the likelihood of the undesired operation from occurring in the future.

System and method for management of systems using multistage learning

Methods and systems for managing a configuration of a client deployment to provide a service are disclosed. The configuration may be managed by managing configuration drift that may occur in the configuration of the client deployment. The configuration drift may be managed by monitoring for changes in the configuration and restoring the configuration to a goal configuration. The changes may be monitored by constructing knowledge graphs using the configurations. A knowledge graph of a current configuration may be compared to a knowledge graph of a goal configuration to find a difference. The difference may represent a configuration drift. The configuration drift in the current configuration may be remedied to restore the current configuration to the goal configuration.

Configuration management using knowledge graphs

Growing community of inventors

Fremont, CA, United States of America

Vinay Sawal