Dell & Products L.p.

Said Tabet

William Jeffery White

Mohamed Sohail

George Currie

Xin Ma

Jeffery White

Robert Anthony Lincourt, Jr

Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by a local controller associated with a DMC domain, service level objective (SLO) metrics; applying the SLO metrics to a predictive anomaly detection transformer to perform anomaly detection; making a first determination that an anomaly is detected; in response to the first determination: attempting basic remediation to resolve the anomaly; making a second determination that the basic remediation is unsuccessful; in response to the second determination: making a third determination that the anomaly is associated with a silent failure; and in response to the third determination: performing service impairment isolation to obtain a collection of services correlated to the anomaly; and performing root cause analysis to identify causal services.

Method and system for performing anomaly detection in a distributed multi-tiered computing environment

Methods, apparatus, and processor-readable storage media for deduplicating data in distributed computing environments are provided herein. An example computer-implemented method includes obtaining, by a node of a distributed computing system, a set of data from an edge device, and identifying a data type corresponding to the set of data from among a plurality of data types. The method includes using information corresponding to the identified data type to determine whether one or more portions of data in the set of data were previously uploaded to at least one server of the distributed computing system. The method also includes, in response to determining that the one or more portions of the data were previously uploaded, updating the set of data by removing the one or more portions of the data from the set of data, and uploading the updated set of data to the at least one server.

Data deduplication techniques for distributed computing environments

Embodiments of systems and methods for unsupervised data characterization utilizing drift are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: provide input data to an Artificial Intelligence (AI) or Machine Learning (ML) drift detector, where the AI/ML drift detector is associated with a characteristic undetectable in the input data; and receive a drift confidence score from the AI/ML drift detector.

Growing community of inventors

Austin, TX, United States of America

Said Tabet